From e56119d11d32778a9a41a8f9d5eb1f9ae10b2bf8 Mon Sep 17 00:00:00 2001 From: gyyyno1 <2606891588@qq.com> Date: Wed, 24 Jul 2024 17:02:58 +0800 Subject: [PATCH 01/23] images generated in different steps --- tests/BW_after_R2_watershed.png | Bin 0 -> 15216 bytes tests/BW_binaryfill.png | Bin 0 -> 18724 bytes tests/BW_clearborder.png | Bin 0 -> 15216 bytes tests/BW_closing.png | Bin 0 -> 22711 bytes tests/BW_image.png | Bin 0 -> 23987 bytes tests/Ld2.png | Bin 0 -> 10336 bytes tests/R.png | Bin 0 -> 448922 bytes tests/R2.png | Bin 0 -> 15216 bytes tests/R3.png | Bin 0 -> 121421 bytes tests/h.png | Bin 0 -> 14440 bytes 10 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 tests/BW_after_R2_watershed.png create mode 100644 tests/BW_binaryfill.png create mode 100644 tests/BW_clearborder.png create mode 100644 tests/BW_closing.png create mode 100644 tests/BW_image.png create mode 100644 tests/Ld2.png create mode 100644 tests/R.png create mode 100644 tests/R2.png create mode 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[PATCH 02/23] Default function and config file it uses as preset parameters --- bubble_analyser/config.toml | 39 +++++++++ bubble_analyser/default.py | 161 ++++++++++++++++++++++++++++++++++++ 2 files changed, 200 insertions(+) create mode 100644 bubble_analyser/config.toml create mode 100644 bubble_analyser/default.py diff --git a/bubble_analyser/config.toml b/bubble_analyser/config.toml new file mode 100644 index 0000000..60cdb5a --- /dev/null +++ b/bubble_analyser/config.toml @@ -0,0 +1,39 @@ +# Parameters are described by its name, value, and range. +# All three items separated by commas + +# Default PARAMETERS + +# Morphological element used for binary operations, e.g. opening, closing, etc. +Morphological_element_size = 5 +Morphological_element_size_range = "{3 10}" + +# Connectivity used, use 4 or 8 +Connectivity = 8 +Connectivity_range = "(4 8)" + +# Marker size for watershed segmentation +Marker_size = 10 +Marker_size_range = "{2 30}" + +# Images can be resampled to make processing faster +resample = 0.5 +resample_range = "[0.1 1]" + +# Reject abnormal bubbles from quantification. E>0.85 or S<0.9 +Max_Eccentricity = 0.85 +Max_Eccentricity_range = "[0.1 1]" +Min_Solidity = 0.9 +Min_Solidity_range = "[0.1 1]" + +# Also ignore too small bubbles (equivalent diameter in mm) +min_size = 0.1 +min_size_range = "[0 50]" + +# Image resolution +px2mm = 1.0 + +# Background image +background_img = "./tests/calibration_files/Background.png" + +# Batch processing flag +do_batch = false diff --git a/bubble_analyser/default.py b/bubble_analyser/default.py new file mode 100644 index 0000000..9e3e5b6 --- /dev/null +++ b/bubble_analyser/default.py @@ -0,0 +1,161 @@ +import tomllib +from pprint import pprint + +import matplotlib.pyplot as plt +import numpy as np +from scipy import ndimage +from skimage import ( + color, + filters, + io, + measure, + morphology, + segmentation, + transform, +) +from skimage.io import imsave + + +def extendedmin(img, H): + mask = img.copy() + marker = mask + H + hmin = morphology.reconstruction(marker, mask, method="erosion") + return morphology.local_minima(hmin) + + +def imposemin(img, minima): + marker = np.full(img.shape, np.inf) + marker[minima == 1] = 0 + mask = np.minimum((img + 1), marker) + return morphology.reconstruction(marker, mask, method="erosion") + + +def default(image_path, params): + # Read the input image + img = io.imread(image_path) + + # Extract parameters from the params dictionary + se = morphology.disk( + params["Morphological_element_size"] + ) # Structuring element for morphological operations + nb = params["Connectivity"] # Neighborhood connectivity (4 or 8) + marker_size = params["Marker_size"] # Marker size for watershed segmentation + px2mm = params["px2mm"] # Image resolution conversion factor (pixels to mm) + img_resample = params["resample"] # Resampling factor for image resizing + bknd_img_path = params["background_img"] # Path to the background image + E_max = params["Max_Eccentricity"] # Maximum eccentricity threshold + S_min = params["Min_Solidity"] # Minimum solidity threshold + Dmin = params["min_size"] # Minimum bubble size (in mm) + do_batch = params["do_batch"] # Flag for batch processing + + # Resize the image for faster processing + img = transform.resize( + img, + (int(img.shape[0] * img_resample), int(img.shape[1] * img_resample)), + anti_aliasing=True, + ) + if img.ndim > 2: + img = color.rgb2gray(img) # Convert to grayscale if the image is in RGB + + # Use background image for thresholding if provided + # if bknd_img_path is not None: + # bknd_img = io.imread(bknd_img_path) + # if bknd_img.ndim > 2: + # bknd_img = color.rgb2gray(bknd_img) # Convert background image to grayscale + # T = filters.threshold_local(bknd_img, 3) # Local threshold for background image+- + # imsave('T.png', T.astype(np.uint8) * 255) + # BW = img > transform.rescale(T, img_resample) # Binary image using background threshold + # # BW = img > T + # else: + + BW = img > filters.threshold_otsu(img) # Binary image using Otsu's thresholding + imsave("tests/BW_image.png", BW.astype(np.uint8) * 255) + + # Perform morphological closing and fill holes + B = morphology.closing(~BW, se) + imsave("tests/BW_closing.png", B.astype(np.uint8) * 255) + B = ndimage.binary_fill_holes(B) + imsave("tests/BW_binaryfill.png", B.astype(np.uint8) * 255) + B = segmentation.clear_border(B) + imsave("tests/BW_clearborder.png", B.astype(np.uint8) * 255) + + # Use distance transform and watershed for segmentation + R = -ndimage.distance_transform_edt(~B) + print(R) + R2 = morphology.h_minima(-R, marker_size) + Ld2 = segmentation.watershed(R2, connectivity=nb) + B[Ld2 == 0] = 0 + imsave("tests/BW_after_R2_watershed.png", B.astype(np.uint8) * 255) + + CH = morphology.convex_hull_image(B) + R3 = -ndimage.distance_transform_edt(~CH) + + mask = extendedmin(R3, nb) + R4 = imposemin(R3, mask) + Ld3 = segmentation.watershed(R4, connectivity=nb) + CH[Ld3 == 0] = 0 + + imsave("tests/R.png", R.astype(np.uint8) * 255) + imsave("tests/R2.png", R2.astype(np.uint8) * 255) + imsave("tests/R3.png", R3.astype(np.uint8) * 255) + imsave("tests/Ld2.png", Ld2.astype(np.uint8) * 255) + + # Fix peak_local_max and watershed usage + + # Label the connected components and calculate geometric properties + CC = measure.label(CH, connectivity=4) + props = measure.regionprops(CC, "equivalent_diameter", "eccentricity", "solidity") + + # Extract properties and apply thresholds + E = np.array([prop.eccentricity for prop in props]) + D = np.array([prop.equivalent_diameter for prop in props]) + S = np.array([prop.solidity for prop in props]) + D = D * px2mm / img_resample + + # Filter out abnormal bubbles + idx = (E >= E_max) | (S <= S_min) | (D < Dmin) + D = D[~idx] + + # Collect extra information about bubbles + extra_info = {"Eccentricity": E[~idx], "Solidity": S[~idx]} + + # Create a labeled image if not doing batch processing + if do_batch: + L_image = None + else: + allowableAreaIndexes = ~idx + keeperIndexes = np.where(allowableAreaIndexes)[0] + keeperBlobsImage = np.isin(measure.label(CH), keeperIndexes + 1) + keeperBlobsImage = transform.resize( + keeperBlobsImage, (img.shape[0], img.shape[1]), anti_aliasing=True + ) + L_image = measure.label(keeperBlobsImage, connectivity=nb) + + return D, L_image, extra_info + + +def load_toml(file_path) -> dict: + """Load TOML data from file""" + with open(file_path, "rb") as f: + toml_data: dict = tomllib.load(f) + return toml_data + + +if __name__ == "__main__": + # Load parameters from the TOML configuration file + params = load_toml("./bubble_analyser/config.toml") + pprint(params) + + # Path to the input image + image_path = "./tests/sample_images/03.jpg" + + # Run the default image processing algorithm + D, L_image, extra_info = default(image_path, params) + + # Print the diameters of detected bubbles + print("Diameters (mm):", D) + + # Display the labeled image if it exists + if L_image is not None: + plt.imshow(L_image) + plt.show() From 7a7a62a00b839ed5c34fc740431c3deaa42e7a43 Mon Sep 17 00:00:00 2001 From: Yiyang Guan Date: Tue, 13 Aug 2024 00:47:24 +0800 Subject: [PATCH 03/23] Delete unnecessary images saved during the code running --- tests/BW_after_R2_watershed.png | Bin 15216 -> 0 bytes tests/BW_binaryfill.png | Bin 18724 -> 0 bytes tests/BW_clearborder.png | Bin 15216 -> 0 bytes tests/BW_closing.png | Bin 22711 -> 0 bytes tests/BW_image.png | Bin 23987 -> 0 bytes tests/Ld2.png | Bin 10336 -> 0 bytes tests/R.png | Bin 448922 -> 0 bytes tests/R2.png | Bin 15216 -> 0 bytes tests/R3.png | Bin 121421 -> 0 bytes tests/h.png | Bin 14440 -> 0 bytes 10 files changed, 0 insertions(+), 0 deletions(-) delete mode 100644 tests/BW_after_R2_watershed.png delete mode 100644 tests/BW_binaryfill.png delete mode 100644 tests/BW_clearborder.png delete mode 100644 tests/BW_closing.png delete mode 100644 tests/BW_image.png delete mode 100644 tests/Ld2.png delete mode 100644 tests/R.png delete mode 100644 tests/R2.png delete mode 100644 tests/R3.png delete mode 100644 tests/h.png diff --git 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zrkGBM|HM=K_@D8o3ed?oa#lCr3$RB(;Vc;a{C$0D7W!VLXpF$h=YP3C`=0vLSo9t5 z@+2t8N!4t3T(k5qm-x}A&uR_YD@!zW1|n9i^)`7vhbM!5Z+)sSzE`$Es2Lh54%}V;3{#+&eC%zoGjX2FG^_ubf-)w+%%itn)*q z=H&Xf6*DLM+r zq?PZtq%if-c)jkfi3HM01%;=)cz@CCSw)L|A2y2Fo#KHX{ma#WR`9aTcmSuaZDpE9lAap^`cJk5S*Ts2^E)G}5Zps+cS3ij%NDOYG!; zn8(d8Oem6VCrc1YPvJW1zwnbO{-SMda)KO|EJZ38b3!@d17cSxlZ7&bIGi58pf_ACRrbEl!>4bF*B&ANb4- z*~i>B?`P_9W8uM?-;y#iNvN+UCdOu}%C{x#oDdim`~R_QYB`ZA1dbNtd!Gwz{%Owz z1!B4Fg^pYkd9Mfx+6f~&POkO{0Q1{HC6+Q1dF=#+;+KPWT=rK5Adfq7wKOJEd{Q3x z_Fo=Zb#j|WAgE?Kmo!Kp&F>*(Lv`go8KRM?OYe^$3CMH}{vEl`1J1CuHOu z9oF1E&@jS{y*>D#)HhSReIeA}dNxuS(KK`%1~N93ev;gw3(#UZ5wLyP3U?03INvyY z$8mqN2vF1=+jkdWYJdfZ)s*fdI0G!}J4i1lCa(dkukf3>rk*T_u|Fe$HSx+~FIMi{zMOF!;TVDfWuz-#BD zWoN|#pGE|X0kCg(UX%)jk^}@mi`g2M zpg@befg;+Y^GDyJ6fo>p@$HFb)D8TffBzQ};P`Uf|MiD;VckF9;;SsyTbbTjOAY&9 Db+DIy From b0fbb85878d32094e36eb5ab391cf2a825c4c060 Mon Sep 17 00:00:00 2001 From: Yiyang Guan Date: Tue, 13 Aug 2024 00:55:55 +0800 Subject: [PATCH 04/23] Add default function under __main__.py --- bubble_analyser/__main__.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/bubble_analyser/__main__.py b/bubble_analyser/__main__.py index 495fd02..7adbdff 100644 --- a/bubble_analyser/__main__.py +++ b/bubble_analyser/__main__.py @@ -1 +1,6 @@ """The entry point for the Bubble Analyser program.""" + +from default import default + +if __name__ == "__main__": + default() From 8aade31edd2ded1d99d4b4073e8bca90a22aa78c Mon Sep 17 00:00:00 2001 From: Yiyang Guan Date: Tue, 13 Aug 2024 00:57:57 +0800 Subject: [PATCH 05/23] Modify some variables in config file to the correct list format --- bubble_analyser/config.toml | 21 ++++++++++++++------- 1 file changed, 14 insertions(+), 7 deletions(-) diff --git a/bubble_analyser/config.toml b/bubble_analyser/config.toml index 60cdb5a..477d23a 100644 --- a/bubble_analyser/config.toml +++ b/bubble_analyser/config.toml @@ -5,35 +5,42 @@ # Morphological element used for binary operations, e.g. opening, closing, etc. Morphological_element_size = 5 -Morphological_element_size_range = "{3 10}" +Morphological_element_size_range = [3, 10] # Connectivity used, use 4 or 8 Connectivity = 8 -Connectivity_range = "(4 8)" +Connectivity_range = [4, 8] # Marker size for watershed segmentation Marker_size = 10 -Marker_size_range = "{2 30}" +Marker_size_range = [2, 30] # Images can be resampled to make processing faster resample = 0.5 -resample_range = "[0.1 1]" +resample_range = [0.1, 1] # Reject abnormal bubbles from quantification. E>0.85 or S<0.9 Max_Eccentricity = 0.85 -Max_Eccentricity_range = "[0.1 1]" +Max_Eccentricity_range = [0.1, 1] Min_Solidity = 0.9 -Min_Solidity_range = "[0.1 1]" +Min_Solidity_range = [0.1, 1] # Also ignore too small bubbles (equivalent diameter in mm) min_size = 0.1 -min_size_range = "[0 50]" +min_size_range = [0, 50] # Image resolution px2mm = 1.0 +# Target image +target_img = "./tests/sample_images/03.jpg" + # Background image background_img = "./tests/calibration_files/Background.png" +threshold_value = 0.1 + +# Ruler image +ruler_img = "./tests/calibration_files/Ruler.png" # Batch processing flag do_batch = false From a6ee25b073c0472bc2233f6a8d0dc2cfbd5fb400 Mon Sep 17 00:00:00 2001 From: Yiyang Guan Date: Tue, 13 Aug 2024 01:23:35 +0800 Subject: [PATCH 06/23] Finish the basic function of default algorithm, see docstring for more details --- bubble_analyser/default.py | 283 +++++++++++++++++++++---------------- 1 file changed, 161 insertions(+), 122 deletions(-) diff --git a/bubble_analyser/default.py b/bubble_analyser/default.py index 9e3e5b6..44d50e7 100644 --- a/bubble_analyser/default.py +++ b/bubble_analyser/default.py @@ -1,54 +1,50 @@ import tomllib from pprint import pprint +import cv2 import matplotlib.pyplot as plt import numpy as np -from scipy import ndimage +from calculate_circle_properties import calculate_circle_properties +from calculate_px2cm import calculate_px2cm +from image_preprocess import image_preprocess +from morphological_process import morphological_process from skimage import ( color, - filters, io, - measure, morphology, - segmentation, transform, ) -from skimage.io import imsave +from threshold import threshold -def extendedmin(img, H): - mask = img.copy() - marker = mask + H - hmin = morphology.reconstruction(marker, mask, method="erosion") - return morphology.local_minima(hmin) +def load_image(image_path: str, img_resample: float) -> tuple[np.ndarray, np.ndarray]: + """Read and preprocess the input image. + This function loads an image from the specified path, resizes it according to the + given resampling factor, and converts it to grayscale if the image is in RGB format. -def imposemin(img, minima): - marker = np.full(img.shape, np.inf) - marker[minima == 1] = 0 - mask = np.minimum((img + 1), marker) - return morphology.reconstruction(marker, mask, method="erosion") + Args: + image_path: The file path of the image to load. + img_resample: The factor by which the image will be resampled (e.g., 0.5 for reducing the size by half). - -def default(image_path, params): + Returns: + A tuple containing: + - The preprocessed grayscale image (if the original was in RGB) or the original + grayscale image. + - The resized image in RGB format. + """ # Read the input image img = io.imread(image_path) - # Extract parameters from the params dictionary - se = morphology.disk( - params["Morphological_element_size"] - ) # Structuring element for morphological operations - nb = params["Connectivity"] # Neighborhood connectivity (4 or 8) - marker_size = params["Marker_size"] # Marker size for watershed segmentation - px2mm = params["px2mm"] # Image resolution conversion factor (pixels to mm) - img_resample = params["resample"] # Resampling factor for image resizing - bknd_img_path = params["background_img"] # Path to the background image - E_max = params["Max_Eccentricity"] # Maximum eccentricity threshold - S_min = params["Min_Solidity"] # Minimum solidity threshold - Dmin = params["min_size"] # Minimum bubble size (in mm) - do_batch = params["do_batch"] # Flag for batch processing + imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + scale_percent = img_resample * 100 # percent of original size + width = int(imgRGB.shape[1] * scale_percent / 100) + height = int(imgRGB.shape[0] * scale_percent / 100) + dim = (width, height) + + # resize image + imgRGB = cv2.resize(imgRGB, dim, interpolation=cv2.INTER_AREA) - # Resize the image for faster processing img = transform.resize( img, (int(img.shape[0] * img_resample), int(img.shape[1] * img_resample)), @@ -57,105 +53,148 @@ def default(image_path, params): if img.ndim > 2: img = color.rgb2gray(img) # Convert to grayscale if the image is in RGB - # Use background image for thresholding if provided - # if bknd_img_path is not None: - # bknd_img = io.imread(bknd_img_path) - # if bknd_img.ndim > 2: - # bknd_img = color.rgb2gray(bknd_img) # Convert background image to grayscale - # T = filters.threshold_local(bknd_img, 3) # Local threshold for background image+- - # imsave('T.png', T.astype(np.uint8) * 255) - # BW = img > transform.rescale(T, img_resample) # Binary image using background threshold - # # BW = img > T - # else: - - BW = img > filters.threshold_otsu(img) # Binary image using Otsu's thresholding - imsave("tests/BW_image.png", BW.astype(np.uint8) * 255) - - # Perform morphological closing and fill holes - B = morphology.closing(~BW, se) - imsave("tests/BW_closing.png", B.astype(np.uint8) * 255) - B = ndimage.binary_fill_holes(B) - imsave("tests/BW_binaryfill.png", B.astype(np.uint8) * 255) - B = segmentation.clear_border(B) - imsave("tests/BW_clearborder.png", B.astype(np.uint8) * 255) - - # Use distance transform and watershed for segmentation - R = -ndimage.distance_transform_edt(~B) - print(R) - R2 = morphology.h_minima(-R, marker_size) - Ld2 = segmentation.watershed(R2, connectivity=nb) - B[Ld2 == 0] = 0 - imsave("tests/BW_after_R2_watershed.png", B.astype(np.uint8) * 255) - - CH = morphology.convex_hull_image(B) - R3 = -ndimage.distance_transform_edt(~CH) - - mask = extendedmin(R3, nb) - R4 = imposemin(R3, mask) - Ld3 = segmentation.watershed(R4, connectivity=nb) - CH[Ld3 == 0] = 0 - - imsave("tests/R.png", R.astype(np.uint8) * 255) - imsave("tests/R2.png", R2.astype(np.uint8) * 255) - imsave("tests/R3.png", R3.astype(np.uint8) * 255) - imsave("tests/Ld2.png", Ld2.astype(np.uint8) * 255) - - # Fix peak_local_max and watershed usage - - # Label the connected components and calculate geometric properties - CC = measure.label(CH, connectivity=4) - props = measure.regionprops(CC, "equivalent_diameter", "eccentricity", "solidity") - - # Extract properties and apply thresholds - E = np.array([prop.eccentricity for prop in props]) - D = np.array([prop.equivalent_diameter for prop in props]) - S = np.array([prop.solidity for prop in props]) - D = D * px2mm / img_resample - - # Filter out abnormal bubbles - idx = (E >= E_max) | (S <= S_min) | (D < Dmin) - D = D[~idx] - - # Collect extra information about bubbles - extra_info = {"Eccentricity": E[~idx], "Solidity": S[~idx]} - - # Create a labeled image if not doing batch processing - if do_batch: - L_image = None - else: - allowableAreaIndexes = ~idx - keeperIndexes = np.where(allowableAreaIndexes)[0] - keeperBlobsImage = np.isin(measure.label(CH), keeperIndexes + 1) - keeperBlobsImage = transform.resize( - keeperBlobsImage, (img.shape[0], img.shape[1]), anti_aliasing=True - ) - L_image = measure.label(keeperBlobsImage, connectivity=nb) - - return D, L_image, extra_info - - -def load_toml(file_path) -> dict: - """Load TOML data from file""" + return img, imgRGB + + +def load_toml(file_path: str) -> dict[str, str | int | float]: + """Load configuration parameters from a TOML file. + + This function reads the TOML configuration file from the specified path and loads + its contents into a dictionary. + + Args: + file_path: The file path of the TOML configuration file. + + Returns: + A dictionary containing the configuration parameters from the TOML file. + """ with open(file_path, "rb") as f: - toml_data: dict = tomllib.load(f) + toml_data: dict[str, str | int | float] = tomllib.load(f) return toml_data -if __name__ == "__main__": +def run_algorithm( + target_img: np.ndarray, + bknd_img: np.ndarray, + imgRGB: np.ndarray, + params: dict, + px2cm: float, + threshold_value: float, +) -> None: + """Execute the image processing algorithm on the target image. + + This function performs a series of image processing steps on the target image, + including thresholding, morphological processing, and watershed segmentation. + It then calculates properties of the detected circular features, such as equivalent + diameter and area, in centimeters using the provided pixel-to-centimeter ratio. + + Args: + target_img: The preprocessed target image. + bknd_img: The background image used for thresholding. + imgRGB: The resized target image in RGB format. + params: A dictionary of parameters loaded from the TOML file. + px2cm: The conversion factor between pixels and centimeters. + + Returns: + None. The function displays and saves various intermediary and final images, and + prints the properties of the detected circular features. + """ + # Extract parameters from the dictionary + element_size = morphology.disk( + params["Morphological_element_size"] + ) # Structuring element for morphological operations + connectivity = params["Connectivity"] # Neighborhood connectivity (4 or 8) + marker_size = params["Marker_size"] # Marker size for watershed segmentation + max_eccentricity = params["Max_Eccentricity"] # Maximum eccentricity threshold + min_solidity = params["Min_Solidity"] # Minimum solidity threshold + min_bubble_size = params["min_size"] # Minimum bubble size (in mm) + do_batch = params["do_batch"] # Flag for batch processing + + # Display the original image + plt.figure() + plt.subplot(231) + plt.title("1. Original image") + plt.imshow(target_img, cmap="gray") + + # Apply thresholding and morphological processing + plt.subplot(232) + imgThreshold = threshold(target_img, bknd_img, threshold_value) + imgThreshold = morphological_process(imgThreshold, element_size) + plt.title("2. Thresh&morph process") + plt.imshow(imgThreshold * 255, cmap="gray") + + # Apply distance transform + plt.subplot(233) + distTrans = cv2.distanceTransform(imgThreshold, cv2.DIST_L2, 5) + plt.title("3. Distance Transform") + plt.imshow(distTrans) + + # Apply thresholding to the distance transform + plt.subplot(234) + _, distThresh = cv2.threshold( + distTrans, 0.3 * distTrans.max(), 255, cv2.THRESH_BINARY + ) + plt.title("4. Threshold of distTrans") + plt.imshow(distThresh) + + # Apply connected component labeling + plt.subplot(235) + distThresh = np.uint8(distThresh) + _, labels = cv2.connectedComponents(distThresh) + plt.title("5. Labels") + plt.imshow(labels) + + # Apply watershed segmentation + plt.figure() + plt.subplot(121) + labels = np.int32(labels) + labels = cv2.watershed(imgRGB, labels) + plt.title("6. Final graph after watershed") + plt.imshow(labels) + + # Display the images + plt.show() + + # Calculate and print the circle properties + circle_properties = calculate_circle_properties(labels, px2cm) + pprint(circle_properties) + + +def default() -> None: + """Run the default image processing routine. + + This function loads the configuration parameters from the TOML file, calculates the + pixel-to-centimeter ratio using a reference ruler image, and then runs the image + processing algorithm on the target image to detect and analyze circular features. + + Args: + None. + + Returns: + None. The function orchestrates the loading of images, execution of the algorithm, + and display of results. + """ # Load parameters from the TOML configuration file params = load_toml("./bubble_analyser/config.toml") - pprint(params) - # Path to the input image - image_path = "./tests/sample_images/03.jpg" + # Read path and image resample factor + ruler_img_path: str = params.get("ruler_img", "") + target_img_path: str = params.get("target_img", "") + bknd_img_path: str = params.get("background_img", "") + img_resample_factor: float = params.get("resample", 0.0) + threshold_value: float = params.get("threshold_value", 0.0) + + # Calculate the pixel to cm ratio + px2cm = calculate_px2cm(ruler_img_path, img_resample_factor) + print(f"Pixel to cm ratio: {px2cm} cm/pixel") + + # Read the background and target image, resize and process into gray scale + bknd_img, _ = image_preprocess(bknd_img_path, img_resample_factor) + target_img, imgRGB = image_preprocess(target_img_path, img_resample_factor) # Run the default image processing algorithm - D, L_image, extra_info = default(image_path, params) + run_algorithm(target_img, bknd_img, imgRGB, params, px2cm, threshold_value) - # Print the diameters of detected bubbles - print("Diameters (mm):", D) - # Display the labeled image if it exists - if L_image is not None: - plt.imshow(L_image) - plt.show() +if __name__ == "__main__": + default() From c0ccb2000d1c071e2f632cdc1a63a7237ae0982d Mon Sep 17 00:00:00 2001 From: Yiyang Guan Date: Tue, 13 Aug 2024 01:24:20 +0800 Subject: [PATCH 07/23] Update dependencies --- poetry.lock | 691 ++++++++++++++++++++++++++++++++++++++++++++++++- pyproject.toml | 6 + 2 files changed, 696 insertions(+), 1 deletion(-) diff --git a/poetry.lock b/poetry.lock index 1a3f2a1..6c5f584 100644 --- a/poetry.lock +++ b/poetry.lock @@ -179,6 +179,69 @@ files = [ {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"}, ] +[[package]] +name = "contourpy" 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version = "2.0.1" @@ -1138,4 +1827,4 @@ watchmedo = ["PyYAML (>=3.10)"] [metadata] lock-version = "2.0" python-versions = "^3.10" -content-hash = "e8abd3bdd438c75c7770f41870a8c050f2824284d2bb0bf9180552b35a59950d" +content-hash = "b6cb4db4b79d74ae382f8f7a06670ee57386169c5c405d3744e178bc878c1b83" diff --git a/pyproject.toml b/pyproject.toml index 7f80c4a..1b8064d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -9,6 +9,12 @@ authors = [ [tool.poetry.dependencies] python = "^3.10" +matplotlib = "^3.9.1.post1" +toml = "^0.10.2" +opencv-python = "^4.10.0.84" +numpy = "^2.0.1" +scikit-image = "^0.24.0" +scipy = "^1.14.0" [tool.poetry.dev-dependencies] pytest = "^8.2" From ae7ea68fdeaf5893adaff2548207e35dd2349308 Mon Sep 17 00:00:00 2001 From: Yiyang Guan Date: Tue, 13 Aug 2024 01:36:11 +0800 Subject: [PATCH 08/23] Functions created for using in the default algorithm --- .../background_subtraction_threshold.py | 74 ++++++++ .../calculate_circle_properties.py | 50 ++++++ bubble_analyser/calculate_px2cm.py | 160 ++++++++++++++++++ bubble_analyser/image_preprocess.py | 123 ++++++++++++++ bubble_analyser/morphological_process.py | 34 ++++ bubble_analyser/threshold.py | 67 ++++++++ 6 files changed, 508 insertions(+) create mode 100644 bubble_analyser/background_subtraction_threshold.py create mode 100644 bubble_analyser/calculate_circle_properties.py create mode 100644 bubble_analyser/calculate_px2cm.py create mode 100644 bubble_analyser/image_preprocess.py create mode 100644 bubble_analyser/morphological_process.py create mode 100644 bubble_analyser/threshold.py diff --git a/bubble_analyser/background_subtraction_threshold.py b/bubble_analyser/background_subtraction_threshold.py new file mode 100644 index 0000000..7963111 --- /dev/null +++ b/bubble_analyser/background_subtraction_threshold.py @@ -0,0 +1,74 @@ +import cv2 +import numpy as np + + +def convert_grayscale(image: np.ndarray) -> np.ndarray: + """Converts an image to grayscale. + + Args: + image (np.ndarray): The input image to be converted. + + Returns: + np.ndarray: The converted image in grayscale. + """ + if len(image.shape) == 3: + image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + return image + + +def background_subtraction( + target_img: np.ndarray, background_img: np.ndarray +) -> np.ndarray: + """Performs background subtraction on two images. + + Args: + target_img (np.ndarray): The target image where the objects of interest are located. + background_img (np.ndarray): The background image (without objects of interest). + + Returns: + np.ndarray: The difference image after background subtraction. + """ + difference_img = cv2.absdiff(target_img, background_img) + return difference_img + + +def threshold(difference_img: np.ndarray, threshold_value: int) -> np.ndarray: + """Applies a binary threshold to the given difference image. + + Args: + difference_img (np.ndarray): The input difference image to be thresholded. + threshold_value (int): The threshold value to apply to the difference image. + + Returns: + np.ndarray: The thresholded image. + """ + _, thresholded_img = cv2.threshold( + difference_img, threshold_value, 255, cv2.THRESH_BINARY + ) + return thresholded_img + + +def background_subtraction_threshold( + target_img: np.ndarray, background_img: np.ndarray, threshold_value: int +) -> np.ndarray: + """Perform background subtraction and apply thresholding. + + Args: + target_img: The target image where the objects of interest are located. + background_img: The background image (without objects of interest). + threshold_value: The threshold value to apply after background subtraction. + + Returns: + A binary image with the objects of interest isolated. + """ + # Ensure both images are in grayscale + target_img = convert_grayscale(target_img) + background_img = convert_grayscale(background_img) + + # Subtract the background image from the target image + difference_img = background_subtraction(target_img, background_img) + + # Apply a threshold to the difference image + thresholded_img = threshold(difference_img, threshold_value) + + return thresholded_img diff --git a/bubble_analyser/calculate_circle_properties.py b/bubble_analyser/calculate_circle_properties.py new file mode 100644 index 0000000..f176446 --- /dev/null +++ b/bubble_analyser/calculate_circle_properties.py @@ -0,0 +1,50 @@ +import numpy as np +from skimage import measure + + +def calculate_circle_properties( + labels: np.ndarray, px2cm: float +) -> list[dict[str, float]]: + """Calculate geometric properties of labeled regions in an image. + + This function computes various properties that describe the "circularity" of regions + within the labeled image, such as area, equivalent diameter, eccentricity, solidity, + and circularity. These properties are calculated in centimeters based on the provided + pixel-to-centimeter ratio. + + Args: + labels: A labeled image where each distinct region (or "circle") is represented + by unique labels. + px2cm: The ratio of centimeters per pixel, used to convert measurements from + pixels to centimeters. + + Returns: + A list of dictionaries, each containing the following properties for a region: + - area: The area of the region in square centimeters. + - equivalent_diameter: The diameter of a circle with the same area as the region, + in centimeters. + - eccentricity: The eccentricity of the ellipse that has the same second-moments + as the region. + - solidity: The proportion of the pixels in the convex hull that are also in the + region. + - circularity: A measure of how close the shape is to a perfect circle, calculated + using the perimeter and area. + """ + properties = measure.regionprops(labels) + circle_properties = [] + for prop in properties: + area = prop.area * (px2cm**2) + equivalent_diameter = prop.equivalent_diameter * px2cm + eccentricity = prop.eccentricity + solidity = prop.solidity + circularity = (4 * np.pi * area) / (prop.perimeter * px2cm) ** 2 + circle_properties.append( + { + "area": area, + "equivalent_diameter": equivalent_diameter, + "eccentricity": eccentricity, + "solidity": solidity, + "circularity": circularity, + } + ) + return circle_properties diff --git a/bubble_analyser/calculate_px2cm.py b/bubble_analyser/calculate_px2cm.py new file mode 100644 index 0000000..5d54380 --- /dev/null +++ b/bubble_analyser/calculate_px2cm.py @@ -0,0 +1,160 @@ +import cv2 +import numpy as np +from image_preprocess import load_image +from numpy.typing import NDArray + + +def resize_to_target_width( + image: NDArray[np.uint8], target_width: int = 1000) -> tuple[np.ndarray, float]: + + """Resizes an image to a specified target width while maintaining the aspect ratio. + + Args: + image (NDArray[np.uint8]): The input image to be resized. + target_width (int, optional): The desired width of the resized image. Defaults to 1000. + + Returns: + NDArray[np.uint8]: The resized image. + scale_percent: The scaling percentage applied to the image during resizing. + """ + # Scale down the image to a width of 1000 pixels, keeping the aspect ratio the same + scale_percent: float = ( + target_width / image.shape[1] + ) # Calculate the scale percent to make width 1000 + width: int = target_width # Set the new width to 1000 pixels + height: int = int( + image.shape[0] * scale_percent + ) # Adjust the height to maintain the aspect ratio + dim: tuple[int, int] = (width, height) # Define the new dimensions + image = cv2.resize(image, dim, interpolation=cv2.INTER_AREA) # Resize the image + + return image, scale_percent + + +def draw_line(event: int, x: int, y: int, flags: int, param: dict[str, object]) -> None: + """Callback function to draw a line on the image. + + This function is used as a mouse callback to allow the user to draw a line on the image. + + Args: + event: The type of mouse event (e.g., left button down, mouse move, left button up). + x: The x-coordinate of the mouse event. + y: The y-coordinate of the mouse event. + flags: Any relevant flags passed by OpenCV. + param: A dictionary containing reference points and drawing state. + """ + refPt, drawing, img, img_copy = ( + param["refPt"], + param["drawing"], + param["img"], + param["img_copy"], + ) + + if event == cv2.EVENT_LBUTTONDOWN: + refPt.append((x, y)) + param["drawing"] = True + + elif event == cv2.EVENT_MOUSEMOVE: + if param["drawing"]: + img_copy[:] = img[:] # Reset to the original image before drawing the line + cv2.line(img_copy, refPt[0], (x, y), (0, 255, 0), 2) + cv2.imshow("image", img_copy) + + elif event == cv2.EVENT_LBUTTONUP: + refPt.append((x, y)) + param["drawing"] = False + cv2.line(img, refPt[0], refPt[1], (0, 255, 0), 2) + cv2.imshow("image", img) + + +def get_pixel_distance(img: NDArray[np.uint8]) -> float: + """Display the image and allow the user to draw a line representing 1 cm. + + This function uses OpenCV to display the image and capture the user input + for drawing a line that represents 1 cm on the ruler. It calculates the + Euclidean distance between the two points of the line in pixels. + + Args: + img: The image on which the user will draw a line. + + Returns: + The distance in pixels between the two drawn points. Returns 0 if the + line was not drawn correctly. + """ + refPt: list[tuple[int, int]] = [] + drawing: bool = False + img_copy: NDArray[np.uint8] = img.copy() + + cv2.namedWindow("image") + cv2.setMouseCallback( + "image", + draw_line, + {"refPt": refPt, "drawing": drawing, "img": img, "img_copy": img_copy}, + ) + + print( + "Draw a line representing 1 cm according to the ruler's scaling in the image." + ) + + while True: + cv2.imshow("image", img_copy) + key: int = cv2.waitKey(1) & 0xFF + if key == ord("q"): + break + + cv2.destroyAllWindows() + + if len(refPt) == 2: + pixel_distance: float = np.sqrt( + (refPt[1][0] - refPt[0][0]) ** 2 + (refPt[1][1] - refPt[0][1]) ** 2 + ) + return pixel_distance + else: + print("Line was not drawn correctly.") + return 0.0 + + +def get_cm_per_pixel( + pixel_distance: float, scale_percent: float, img_resample: float +) -> float: + """Calculate the conversion ratio from pixels to centimeters. + + Args: + pixel_distance: The distance in pixels between the two drawn points. + scale_percent: The scaling percentage applied to the image during resizing. + img_resample: The resampling factor applied to the original target and background image. + + Returns: + The conversion factor in centimeters per pixel, corrected for the resampling + applied to the original image. + """ + original_pixel_distance: float = pixel_distance / scale_percent + cm_per_pixel: float = 1.0 / original_pixel_distance + cm_per_pixel = cm_per_pixel / img_resample + return cm_per_pixel + + +def calculate_px2cm(image_path: str, img_resample: float) -> float: + """Calculates the conversion factor from pixels to centimeters. + This function reads an image of a ruler, allows the user to draw a line corresponding + to 1 cm on the ruler, and calculates the pixel-to-centimeter conversion factor. The + image is scaled down for easier interaction, but the final calculation accounts for + this scaling as well as the resample factor for target and background images to + ensure accuracy relative to the original image size. + + Args: + image_path (str): The path to the image file. + img_resample (float): The resampling factor applied to the original target and background image. + + Returns: + float: The conversion factor in centimeters per pixel, corrected for the resampling applied to the original image. + """ + image = load_image(image_path) + image, scale_percent = resize_to_target_width(image) + pixel_distance: float = get_pixel_distance(image) + if pixel_distance > 0: + cm_per_pixel: float = get_cm_per_pixel( + pixel_distance, scale_percent, img_resample + ) + print(f"Conversion factor: {cm_per_pixel} cm per pixel") + return cm_per_pixel diff --git a/bubble_analyser/image_preprocess.py b/bubble_analyser/image_preprocess.py new file mode 100644 index 0000000..6f6013d --- /dev/null +++ b/bubble_analyser/image_preprocess.py @@ -0,0 +1,123 @@ +import cv2 +import numpy as np +from skimage import ( + color, + io, + transform, +) + + +def load_image(image_path: str) -> np.ndarray: + """Read and preprocess the input image. + + Args: + image_path (str): The file path of the image to load. + + Returns: + np.ndarray: The image read in ndarray format. + """ + # Read the input image + + img = io.imread(image_path) + + return img + + +def get_greyscale(image: np.ndarray) -> np.ndarray: + """Converts an image to grayscale if it is in RGB format. + + Args: + image (np.ndarray): The input image to be converted. + + Returns: + np.ndarray: The grayscale image. + """ + if image.ndim > 2: + image = color.rgb2gray(image) # Convert to grayscale if the image is in RGB + return image + + +def get_RGB(image: np.ndarray) -> np.ndarray: + """Converts an image from BGR color space to RGB color space. + + Args: + image (np.ndarray): The input image in BGR format. + + Returns: + np.ndarray: The converted image in RGB format. + """ + imgRGB = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + return imgRGB + + +def resize_for_RGB(image: np.ndarray, img_resample_factor: float) -> np.ndarray: + """Resizes an image in RGB format based on the provided resampling factor using method + provided by openCV. + + Args: + image (np.ndarray): The input image in RGB format. + img_resample_factor (float): The factor by which the image will be resampled. + + Returns: + np.ndarray: The resized image in RGB format. + """ + scale_percent = img_resample_factor * 100 # percent of original size + width = int(image.shape[1] * scale_percent / 100) + height = int(image.shape[0] * scale_percent / 100) + img_resample_dimension = (width, height) + + image = cv2.resize(image, img_resample_dimension, interpolation=cv2.INTER_AREA) + return image + + +def resize_for_original_image( + image: np.ndarray, img_resample_factor: float +) -> np.ndarray: + """Resizes an image based on the provided resampling factor for original image using method + provided by skimage. + + Args: + image (np.ndarray): The input image to be resized. + img_resample_factor (float): The factor by which the image will be resampled. + + Returns: + np.ndarray: The resized image. + """ + image = transform.resize( + image, + ( + int(image.shape[0] * img_resample_factor), + int(image.shape[1] * img_resample_factor), + ), + anti_aliasing=True, + ) + return image + + +def image_preprocess( + img_path: str, img_resample: float +) -> tuple[np.ndarray, np.ndarray]: + """Preprocesses an image by loading it, converting it to 1. RGB, 2. grayscale + and resizing it based on a provided resampling factor. + The resized grayscale image (img) is for use in the following "default" watershed algorithm + as a target image. And the RGB image (imgRGB) is for the visualization of the results. + They are resized in different ways for different use cases (the output format of the + methods are differnt). + + Args: + img_path (str): The file path of the image to preprocess. + img_resample (float): The resampling factor to apply to the image. + + Returns: + tuple[np.ndarray, np.ndarray]: A tuple containing the resized grayscale image + and the resized RGB image. + """ + image = load_image(img_path) + image_RGB = get_RGB(image) + + image = resize_for_original_image(image, img_resample) + image = get_greyscale(image) + + image_RGB = resize_for_RGB(image_RGB, img_resample) + + return image, image_RGB diff --git a/bubble_analyser/morphological_process.py b/bubble_analyser/morphological_process.py new file mode 100644 index 0000000..f1f49f7 --- /dev/null +++ b/bubble_analyser/morphological_process.py @@ -0,0 +1,34 @@ +import numpy as np +from scipy import ndimage +from skimage import ( + morphology, + segmentation, +) + + +def morphological_process(target_img: np.ndarray, element_size: int) -> np.ndarray: + """Apply morphological operations to process the target image. + + This function performs a series of morphological operations on the input image, + including closing, filling holes, and clearing borders. These operations help in + refining the binary image by removing noise and filling gaps. + + Args: + target_img: A binary image (numpy array) where the regions of interest are + typically in white (True) and the background in black (False). + se: A structuring element used for morphological closing, typically a disk-shaped array. + + + Returns: + A processed binary image (numpy array) where the regions of interest are more + defined, with filled holes and cleared borders. + """ + # Perform morphological closing and fill holes + image_processed = morphology.closing(target_img, element_size) + image_processed = ndimage.binary_fill_holes(image_processed) + image_processed = segmentation.clear_border(image_processed) + + image_processed = image_processed.astype(np.uint8) + # opening = cv2.morphologyEx(B,cv2.MORPH_OPEN,kernel, iterations = 2) + + return image_processed diff --git a/bubble_analyser/threshold.py b/bubble_analyser/threshold.py new file mode 100644 index 0000000..d6d0990 --- /dev/null +++ b/bubble_analyser/threshold.py @@ -0,0 +1,67 @@ +import numpy as np +from background_subtraction_threshold import background_subtraction_threshold +from numpy.typing import NDArray +from skimage import ( + filters, +) + + +def otsu_threshold(target_img: np.ndarray) -> np.ndarray: + """Apply Otsu's thresholding to the target image and return an inverted binary mask. + + This function takes a target image and a background image. It applies Otsu's thresholding + to the target image to create a binary mask where the foreground objects are separated + from the background. The binary mask is then inverted, so the foreground becomes the + background and vice versa, the inversion is for easier processing in following morphologcal process. + + Args: + target_img: A grayscale image (2D array) representing the target image. + bknd_img: A grayscale image (2D array) representing the background image (not + used in this function). + + Returns: + A binary (inverted) image where the foreground and background are swapped. + """ + binary_image: NDArray[np.bool_] = target_img > filters.threshold_otsu( + target_img + ) # Binary image using Otsu's thresholding + return ~binary_image # Invert the binary image + + +def select_threshold_method() -> str: + """Selects a threshold method from a list of available options. + Prompts the user to choose a threshold method from the list of options. + The options are displayed with their corresponding numbers, and the user + is asked to input the number of their chosen method. + + Returns: + str: The chosen threshold method. + """ + options = ["OTSU's method", "Background subtraction"] + print("Select a threshold method:") + for i, option in enumerate(options): + print(f"{i+1}. {option}") + choice = input("Enter the number of your choice: ") + return options[int(choice) - 1] + + +def threshold( + target_img: np.ndarray, bknd_img: np.ndarray, threshold_value: float +) -> np.ndarray: + """Applies a threshold to the target image based on the selected method. + + Args: + target_img (np.ndarray): The target image to apply the threshold to. + bknd_img (np.ndarray): The background image used for background subtraction. + threshold_value (float): The threshold value used for background subtraction. + + Returns: + np.ndarray: The thresholded image. + """ + method = select_threshold_method() + if method == "OTSU's method": + return otsu_threshold(target_img) + elif method == "Background subtraction": + return background_subtraction_threshold(target_img, bknd_img, threshold_value) + else: + raise ValueError(f"Unsupported threshold method: {method}") From 0cd6affdc7f5f94cd5e56ef6a4b15b69164a23ad Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 12 Aug 2024 17:53:41 +0000 Subject: [PATCH 09/23] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- bubble_analyser/calculate_px2cm.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/bubble_analyser/calculate_px2cm.py b/bubble_analyser/calculate_px2cm.py index 5d54380..b851908 100644 --- a/bubble_analyser/calculate_px2cm.py +++ b/bubble_analyser/calculate_px2cm.py @@ -5,8 +5,8 @@ def resize_to_target_width( - image: NDArray[np.uint8], target_width: int = 1000) -> tuple[np.ndarray, float]: - + image: NDArray[np.uint8], target_width: int = 1000 +) -> tuple[np.ndarray, float]: """Resizes an image to a specified target width while maintaining the aspect ratio. Args: From 2203cc40a84b753dd647aa11c091eeb9ad5254c1 Mon Sep 17 00:00:00 2001 From: Yiyang Guan Date: Wed, 14 Aug 2024 15:40:03 +0800 Subject: [PATCH 10/23] Modified the code based on the Pydantic tool --- bubble_analyser/default.py | 77 ++++++++++++++++++++++++++++---------- 1 file changed, 58 insertions(+), 19 deletions(-) diff --git a/bubble_analyser/default.py b/bubble_analyser/default.py index 44d50e7..d625465 100644 --- a/bubble_analyser/default.py +++ b/bubble_analyser/default.py @@ -1,6 +1,37 @@ +"""Bubble Analyser: Image Processing for Circular Feature Detection. + +This script is designed to process and analyze images to detect and evaluate circular +features, such as bubbles, using various image processing techniques. The script +integrates several modular functions for loading images, processing them, and +calculating properties of detected features in terms of real-world measurements. + +The key functionalities include: + +1. Loading configuration parameters from a TOML file, which govern the image processing + steps and parameters. +2. Loading and preprocessing images, including conversion to grayscale and resizing + based on a given resampling factor. +3. Calculating the conversion factor from pixels to centimeters using a reference ruler + image, ensuring that measurements of detected features are accurate and scalable. +4. Executing the image processing algorithm, which involves thresholding, morphological + processing, distance transformation, connected component labeling, and watershed + segmentation to isolate and analyze circular features. +5. Displaying and saving intermediary and final images, along with calculating and + printing properties such as equivalent diameter and area of the detected features. + +The script is structured to allow easy customization and extension, making it suitable +for a wide range of image analysis tasks that involve circular feature detection and +measurement. + +To run the script, simply execute the `default()` function, which orchestrates the +entire process from loading configurations and images to running the analysis and +displaying results. +""" + import tomllib from pprint import pprint +import Config import cv2 import matplotlib.pyplot as plt import numpy as np @@ -25,7 +56,8 @@ def load_image(image_path: str, img_resample: float) -> tuple[np.ndarray, np.nda Args: image_path: The file path of the image to load. - img_resample: The factor by which the image will be resampled (e.g., 0.5 for reducing the size by half). + img_resample: The factor by which the image will be resampled (e.g., 0.5 for + reducing the size by half). Returns: A tuple containing: @@ -56,7 +88,7 @@ def load_image(image_path: str, img_resample: float) -> tuple[np.ndarray, np.nda return img, imgRGB -def load_toml(file_path: str) -> dict[str, str | int | float]: +def load_toml(file_path: str) -> Config: """Load configuration parameters from a TOML file. This function reads the TOML configuration file from the specified path and loads @@ -68,16 +100,20 @@ def load_toml(file_path: str) -> dict[str, str | int | float]: Returns: A dictionary containing the configuration parameters from the TOML file. """ + # with open(file_path, "rb") as f: + # toml_data: dict[str, str | int | float] = tomllib.load(f) + # return toml_data with open(file_path, "rb") as f: - toml_data: dict[str, str | int | float] = tomllib.load(f) - return toml_data + toml_data = tomllib.load(f) + + return Config.Config(**toml_data) def run_algorithm( target_img: np.ndarray, bknd_img: np.ndarray, imgRGB: np.ndarray, - params: dict, + params: Config.Config, px2cm: float, threshold_value: float, ) -> None: @@ -94,6 +130,7 @@ def run_algorithm( imgRGB: The resized target image in RGB format. params: A dictionary of parameters loaded from the TOML file. px2cm: The conversion factor between pixels and centimeters. + threshold_value: Threshold value for background subtraction Returns: None. The function displays and saves various intermediary and final images, and @@ -101,14 +138,16 @@ def run_algorithm( """ # Extract parameters from the dictionary element_size = morphology.disk( - params["Morphological_element_size"] + params.Morphological_element_size ) # Structuring element for morphological operations - connectivity = params["Connectivity"] # Neighborhood connectivity (4 or 8) - marker_size = params["Marker_size"] # Marker size for watershed segmentation - max_eccentricity = params["Max_Eccentricity"] # Maximum eccentricity threshold - min_solidity = params["Min_Solidity"] # Minimum solidity threshold - min_bubble_size = params["min_size"] # Minimum bubble size (in mm) - do_batch = params["do_batch"] # Flag for batch processing + + # Below are variables that might be used in the future coding + # connectivity = params.Connectivity # Neighborhood connectivity (4 or 8) + # marker_size = params.Marker_size # Marker size for watershed segmentation + # max_eccentricity = params.Max_Eccentricity # Maximum eccentricity threshold + # min_solidity = params.Min_Solidity # Minimum solidity threshold + # min_bubble_size = params.min_size # Minimum bubble size (in mm) + # do_batch = params.do_batch # Flag for batch processing # Display the original image plt.figure() @@ -171,18 +210,18 @@ def default() -> None: None. Returns: - None. The function orchestrates the loading of images, execution of the algorithm, - and display of results. + None. The function orchestrates the loading of images, execution of the + algorithm, and display of results. """ # Load parameters from the TOML configuration file params = load_toml("./bubble_analyser/config.toml") # Read path and image resample factor - ruler_img_path: str = params.get("ruler_img", "") - target_img_path: str = params.get("target_img", "") - bknd_img_path: str = params.get("background_img", "") - img_resample_factor: float = params.get("resample", 0.0) - threshold_value: float = params.get("threshold_value", 0.0) + ruler_img_path: str = params.ruler_img_path + target_img_path: str = params.target_img_path + bknd_img_path: str = params.background_img_path + img_resample_factor: float = params.resample + threshold_value: float = params.threshold_value # Calculate the pixel to cm ratio px2cm = calculate_px2cm(ruler_img_path, img_resample_factor) From f76936d82db08fd71480c662ba6eba4c2b1c5e4f Mon Sep 17 00:00:00 2001 From: Yiyang Guan Date: Wed, 14 Aug 2024 15:41:19 +0800 Subject: [PATCH 11/23] Add missing explanations for some variables --- bubble_analyser/config.toml | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/bubble_analyser/config.toml b/bubble_analyser/config.toml index 477d23a..848f1a4 100644 --- a/bubble_analyser/config.toml +++ b/bubble_analyser/config.toml @@ -33,14 +33,16 @@ min_size_range = [0, 50] px2mm = 1.0 # Target image -target_img = "./tests/sample_images/03.jpg" +target_img_path = "./tests/sample_images/03.jpg" # Background image -background_img = "./tests/calibration_files/Background.png" +background_img_path = "./tests/calibration_files/Background.png" + +# Threshold value for background subtraction threshold_value = 0.1 # Ruler image -ruler_img = "./tests/calibration_files/Ruler.png" +ruler_img_path = "./tests/calibration_files/Ruler.png" # Batch processing flag do_batch = false From 5ff62d49b5a09bc0f332c4a71a4d49ef4fb30bf9 Mon Sep 17 00:00:00 2001 From: Yiyang Guan Date: Wed, 14 Aug 2024 16:58:04 +0800 Subject: [PATCH 12/23] Create a class based on Pydantic tool, for passing the mypy checkerror for dictionary type annotation --- bubble_analyser/Config.py | 273 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 273 insertions(+) create mode 100644 bubble_analyser/Config.py diff --git a/bubble_analyser/Config.py b/bubble_analyser/Config.py new file mode 100644 index 0000000..7b58afa --- /dev/null +++ b/bubble_analyser/Config.py @@ -0,0 +1,273 @@ +"""This module defines the configuration parameters for the Bubble Analyser project. + +The `Config` class is a Pydantic model that validates and manages the configuration +parameters used in the image processing and analysis routines. These parameters +include morphological element sizes, connectivity, marker size, image resampling +factors, and more. The class also includes methods to validate the ranges of these +parameters, ensuring that they are logically consistent before being used in the +processing algorithms. + +Classes: + Config: A Pydantic model for storing and validating configuration parameters. + +Methods: + check_morphological_element_size_range: Validates the morphological element size + range. + check_connectivity_range: Validates the connectivity range. + check_marker_size_range: Validates the marker size range. + check_resample_range: Validates the resample range. + check_max_eccentricity_range: Validates the maximum eccentricity range. + check_min_solidity_range: Validates the minimum solidity range. + check_min_size_range: Validates the minimum size range. +""" + +from pathlib import Path + +import typing_extensions +from pydantic import ( + BaseModel, + PositiveFloat, + PositiveInt, + StrictBool, + StrictFloat, + model_validator, +) + + +class Config(BaseModel): + """A Pydantic model for storing and validating configuration parameters. + + The class contains parameters for image processing and analysis, such as + morphological element sizes, connectivity, marker size, image resampling + factors, maximum eccentricity, and more. The class also includes methods to + validate the ranges of these parameters, ensuring that they are logically + consistent before being used in the processing algorithms. + # Morphological element used for binary operations, e.g. opening, closing, etc. + Morphological_element_size: PositiveInt + Morphological_element_size_range: tuple[PositiveInt, PositiveInt] + + Attributes: + Morphological_element_size: PositiveInt + Morphological_element_size_range: tuple[PositiveInt, PositiveInt] + Connectivity: PositiveInt + Connectivity_range: tuple[PositiveInt, PositiveInt] + Marker_size: PositiveInt + Marker_size_range: tuple[PositiveInt, PositiveInt] + resample: PositiveFloat + resample_range: tuple[PositiveFloat, PositiveFloat] + Max_Eccentricity: PositiveFloat + Max_Eccentricity_range: tuple[PositiveFloat, PositiveFloat] + Min_Solidity: PositiveFloat + Min_Solidity_range: tuple[PositiveFloat, PositiveFloat] + min_size: StrictFloat + min_size_range: tuple[StrictFloat, StrictFloat] + px2mm: PositiveFloat + target_img_path: Path + background_img_path: Path + threshold_value: PositiveFloat + ruler_img_path: Path + do_batch: StrictBool + """ + + # Default PARAMETERS + + # Morphological element used for binary operations, e.g. opening, closing, etc. + Morphological_element_size: PositiveInt + Morphological_element_size_range: tuple[PositiveInt, PositiveInt] + + # Connectivity used, use 4 or 8 + Connectivity: PositiveInt + Connectivity_range: tuple[PositiveInt, PositiveInt] + + # Marker size for watershed segmentation + Marker_size: PositiveInt + Marker_size_range: tuple[PositiveInt, PositiveInt] + + # Images can be resampled to make processing faster + resample: PositiveFloat + resample_range: tuple[PositiveFloat, PositiveFloat] + + # Reject abnormal bubbles from quantification. E>0.85 or S<0.9 + Max_Eccentricity: PositiveFloat + Max_Eccentricity_range: tuple[PositiveFloat, PositiveFloat] + Min_Solidity: PositiveFloat + Min_Solidity_range: tuple[PositiveFloat, PositiveFloat] + + # Also ignore too small bubbles (equivalent diameter in mm) + min_size: StrictFloat + min_size_range: tuple[StrictFloat, StrictFloat] + + # User input Image resolution + px2mm: PositiveFloat + + # Path for Target image + target_img_path: Path + + # Path for Background image + background_img_path: Path + + # Threshold value for background subtraction + threshold_value: PositiveFloat + + # Path for Ruler image + ruler_img_path: Path + + # Batch processing flag + do_batch: StrictBool + + @model_validator(mode="after") + def check_morphological_element_size_range(self) -> typing_extensions.Self: + """Validates the morphological element size range. + + Ensures that the lower bound of the range is less than the upper bound. + If the bounds are in the wrong order, a ValueError is raised. + + Returns: + Self: The instance itself, for method chaining. + """ + low, high = self.Morphological_element_size_range + # Check if the lower bound is less than the upper bound + if low >= high: + # Raise a ValueError if the bounds are in the wrong order + raise ValueError( + "Limits for the Morphological_element_size_range are in the wrong order" + ) + return self + + @model_validator(mode="after") + def check_connectivity_range(self) -> typing_extensions.Self: + """Validates the connectivity range. + + Ensures that the lower bound of the range is less than the upper bound. + If the bounds are in the wrong order, a ValueError is raised. + + Returns: + Self: The instance itself, for method chaining. + """ + low, high = self.Connectivity_range + # Check if the lower bound is less than the upper bound + if low >= high: + # Raise a ValueError if the bounds are in the wrong order + raise ValueError("Limits for the Connectivity_range are in the wrong order") + return self + + @model_validator(mode="after") + def check_marker_size_range(self) -> typing_extensions.Self: + """Validates the marker size range. + + Ensures that the lower bound of the range is less than the upper bound. + If the bounds are in the wrong order, a ValueError is raised. + + Returns: + Self: The instance itself, for method chaining. + """ + # Get the lower and upper bounds of the marker size range + low, high = self.Marker_size_range + + # Check if the lower bound is less than the upper bound + if low >= high: + # Raise a ValueError if the bounds are in the wrong order + raise ValueError("Limits for the Marker_size_range are in the wrong order") + + # Return the instance itself for method chaining + return self + + @model_validator(mode="after") + def check_resample_range(self) -> typing_extensions.Self: + """Validates the resample range. + + Ensures that the lower bound of the range is less than the upper bound. + If the bounds are in the wrong order (lower bound >= upper bound), a ValueError + is raised. + + Returns: + Self: The instance itself, for method chaining. + + Raises: + ValueError: If the lower bound is greater than or equal to the upper bound. + """ + # Get the lower and upper bounds of the resample range + low, high = self.resample_range + + # Check if the lower bound is less than the upper bound + if low >= high: + # Raise a ValueError if the bounds are in the wrong order + raise ValueError("Limits for the resample_range are in the wrong order") + + # Return the instance itself for method chaining + return self + + @model_validator(mode="after") + def check_max_eccentricity_range(self) -> typing_extensions.Self: + """Validates the maximum eccentricity range. + + Ensures that the lower bound of the range is less than the upper bound. + If the bounds are in the wrong order (lower bound >= upper bound), a ValueError + is raised. + + Returns: + Self: The instance itself, for method chaining. + + Raises: + ValueError: If the lower bound is greater than or equal to the upper bound. + """ + # Get the lower and upper bounds of the maximum eccentricity range + low, high = self.Max_Eccentricity_range + + # Check if the lower bound is less than the upper bound + if low >= high: + # Raise a ValueError if the bounds are in the wrong order + raise ValueError( + "Limits for the Max_Eccentricity_range are in the wrong order" + ) + + # Return the instance itself for method chaining + return self + + @model_validator(mode="after") + def check_min_solidity_range(self) -> typing_extensions.Self: + """Validates the minimum solidity range. + + Ensures that the lower bound of the range is less than the upper bound. + If the bounds are in the wrong order (lower bound >= upper bound), a ValueError + is raised. + + Returns: + Self: The instance itself, for method chaining. + + Raises: + ValueError: If the lower bound is greater than or equal to the upper bound. + """ + # Get the lower and upper bounds of the minimum solidity range + low, high = self.Min_Solidity_range + + # Check if the lower bound is less than the upper bound + if low >= high: + # Raise a ValueError if the bounds are in the wrong order + raise ValueError("Limits for the Min_Solidity_range are in the wrong order") + + return self + + @model_validator(mode="after") + def check_min_size_range(self) -> typing_extensions.Self: + """Validates the minimum size range. + + Ensures that the lower bound of the range is less than the upper bound. + If the bounds are in the wrong order (lower bound >= upper bound), a ValueError + is raised. + + Returns: + Self: The instance itself, for method chaining. + + Raises: + ValueError: If the lower bound is greater than or equal to the upper bound. + """ + # Get the lower and upper bounds of the minimum size range + low, high = self.min_size_range + + # Check if the lower bound is less than the upper bound + if low >= high: + # Raise a ValueError if the bounds are in the wrong order + raise ValueError("Limits for the min_size_range are in the wrong order") + # Return the instance itself for method chaining + return self From a6e02d7e34f302ccd37faee1679ce573fb6e2e97 Mon Sep 17 00:00:00 2001 From: Yiyang Guan Date: Wed, 14 Aug 2024 17:00:13 +0800 Subject: [PATCH 13/23] Update dependencies used --- poetry.lock | 147 ++++++++++++++++++++++++++++++++++++++++++++++++- pyproject.toml | 3 + 2 files changed, 149 insertions(+), 1 deletion(-) diff --git a/poetry.lock b/poetry.lock index 6c5f584..1fb5597 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,5 +1,16 @@ # This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +[[package]] +name = "annotated-types" +version = "0.7.0" +description = "Reusable constraint types to use with typing.Annotated" +optional = false +python-versions = ">=3.8" +files = [ + {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"}, + {file = "annotated_types-0.7.0.tar.gz", 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a/pyproject.toml b/pyproject.toml index 1b8064d..57e5e23 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,6 +15,9 @@ opencv-python = "^4.10.0.84" numpy = "^2.0.1" scikit-image = "^0.24.0" scipy = "^1.14.0" +pydantic = "^2.8.2" +pathlib = "^1.0.1" +typing-extensions = "^4.12.2" [tool.poetry.dev-dependencies] pytest = "^8.2" From b8598d6ce476c5f9febeef576010b55e44b15f6e Mon Sep 17 00:00:00 2001 From: Yiyang Guan Date: Wed, 21 Aug 2024 16:48:21 +0800 Subject: [PATCH 14/23] Add more details for the importing modules' location(skipped the verification) --- bubble_analyser/__main__.py | 2 +- bubble_analyser/calculate_px2cm.py | 2 +- bubble_analyser/default.py | 21 ++++++++++++++------- bubble_analyser/threshold.py | 2 +- 4 files changed, 17 insertions(+), 10 deletions(-) diff --git a/bubble_analyser/__main__.py b/bubble_analyser/__main__.py index 7adbdff..fd29cb7 100644 --- a/bubble_analyser/__main__.py +++ b/bubble_analyser/__main__.py @@ -1,6 +1,6 @@ """The entry point for the Bubble Analyser program.""" -from default import default +from .default import default if __name__ == "__main__": default() diff --git a/bubble_analyser/calculate_px2cm.py b/bubble_analyser/calculate_px2cm.py index b851908..5d39b66 100644 --- a/bubble_analyser/calculate_px2cm.py +++ b/bubble_analyser/calculate_px2cm.py @@ -1,6 +1,6 @@ import cv2 import numpy as np -from image_preprocess import load_image +from .image_preprocess import load_image from numpy.typing import NDArray diff --git a/bubble_analyser/default.py b/bubble_analyser/default.py index d625465..c3036d9 100644 --- a/bubble_analyser/default.py +++ b/bubble_analyser/default.py @@ -28,24 +28,25 @@ displaying results. """ -import tomllib +import toml as tomllib from pprint import pprint -import Config + +from .Config import Config import cv2 import matplotlib.pyplot as plt import numpy as np -from calculate_circle_properties import calculate_circle_properties -from calculate_px2cm import calculate_px2cm -from image_preprocess import image_preprocess -from morphological_process import morphological_process +from .calculate_circle_properties import calculate_circle_properties +from .calculate_px2cm import calculate_px2cm +from .image_preprocess import image_preprocess +from .morphological_process import morphological_process from skimage import ( color, io, morphology, transform, ) -from threshold import threshold +from .threshold import threshold def load_image(image_path: str, img_resample: float) -> tuple[np.ndarray, np.ndarray]: @@ -237,3 +238,9 @@ def default() -> None: if __name__ == "__main__": default() + +# First background subtraction (optional) then otsu thresholding +# Let user define limitations based on the properties of the bubbles for filtering them +# Output the image that eliminate the bubbles being filtered out +# Table and Histogram +# Let user modify the parameters in UI \ No newline at end of file diff --git a/bubble_analyser/threshold.py b/bubble_analyser/threshold.py index d6d0990..dfd61f8 100644 --- a/bubble_analyser/threshold.py +++ b/bubble_analyser/threshold.py @@ -1,5 +1,5 @@ import numpy as np -from background_subtraction_threshold import background_subtraction_threshold +from .background_subtraction_threshold import background_subtraction_threshold from numpy.typing import NDArray from skimage import ( filters, From 222fc72e66c7a12fc5aad4bda37c44a824b5aa62 Mon Sep 17 00:00:00 2001 From: Yiyang Guan Date: Wed, 21 Aug 2024 16:48:49 +0800 Subject: [PATCH 15/23] Add more details for the importing modules' location(skipped the verification) --- bubble_analyser/calculate_px2cm.py | 3 ++- bubble_analyser/default.py | 16 ++++++++-------- bubble_analyser/threshold.py | 3 ++- 3 files changed, 12 insertions(+), 10 deletions(-) diff --git a/bubble_analyser/calculate_px2cm.py b/bubble_analyser/calculate_px2cm.py index 5d39b66..bff2a0b 100644 --- a/bubble_analyser/calculate_px2cm.py +++ b/bubble_analyser/calculate_px2cm.py @@ -1,8 +1,9 @@ import cv2 import numpy as np -from .image_preprocess import load_image from numpy.typing import NDArray +from .image_preprocess import load_image + def resize_to_target_width( image: NDArray[np.uint8], target_width: int = 1000 diff --git a/bubble_analyser/default.py b/bubble_analyser/default.py index c3036d9..80ce331 100644 --- a/bubble_analyser/default.py +++ b/bubble_analyser/default.py @@ -28,24 +28,24 @@ displaying results. """ -import toml as tomllib from pprint import pprint - -from .Config import Config import cv2 import matplotlib.pyplot as plt import numpy as np -from .calculate_circle_properties import calculate_circle_properties -from .calculate_px2cm import calculate_px2cm -from .image_preprocess import image_preprocess -from .morphological_process import morphological_process +import toml as tomllib from skimage import ( color, io, morphology, transform, ) + +from .calculate_circle_properties import calculate_circle_properties +from .calculate_px2cm import calculate_px2cm +from .Config import Config +from .image_preprocess import image_preprocess +from .morphological_process import morphological_process from .threshold import threshold @@ -243,4 +243,4 @@ def default() -> None: # Let user define limitations based on the properties of the bubbles for filtering them # Output the image that eliminate the bubbles being filtered out # Table and Histogram -# Let user modify the parameters in UI \ No newline at end of file +# Let user modify the parameters in UI diff --git a/bubble_analyser/threshold.py b/bubble_analyser/threshold.py index dfd61f8..db2c076 100644 --- a/bubble_analyser/threshold.py +++ b/bubble_analyser/threshold.py @@ -1,10 +1,11 @@ import numpy as np -from .background_subtraction_threshold import background_subtraction_threshold from numpy.typing import NDArray from skimage import ( filters, ) +from .background_subtraction_threshold import background_subtraction_threshold + def otsu_threshold(target_img: np.ndarray) -> np.ndarray: """Apply Otsu's thresholding to the target image and return an inverted binary mask. From dcffcd95de4e0a489ba5e1ac170acd36ee66e31c Mon Sep 17 00:00:00 2001 From: Diego Alonso Alvarez Date: Wed, 21 Aug 2024 13:51:39 +0100 Subject: [PATCH 16/23] Update pyproject.toml file --- poetry.lock | 13 ++++++++++++- pyproject.toml | 14 ++++++++------ 2 files changed, 20 insertions(+), 7 deletions(-) diff --git a/poetry.lock b/poetry.lock index 1fb5597..f0811bc 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1877,6 +1877,17 @@ files = [ {file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"}, ] +[[package]] +name = "types-toml" +version = "0.10.8.20240310" +description = "Typing stubs for toml" +optional = false +python-versions = ">=3.8" +files = [ + {file = "types-toml-0.10.8.20240310.tar.gz", hash = "sha256:3d41501302972436a6b8b239c850b26689657e25281b48ff0ec06345b8830331"}, + {file = "types_toml-0.10.8.20240310-py3-none-any.whl", hash = "sha256:627b47775d25fa29977d9c70dc0cbab3f314f32c8d8d0c012f2ef5de7aaec05d"}, +] + [[package]] name = "typing-extensions" version = "4.12.2" @@ -1972,4 +1983,4 @@ watchmedo = ["PyYAML (>=3.10)"] [metadata] lock-version = "2.0" python-versions = "^3.10" -content-hash = "fe021ae21d3c1df5c211b7fe4d934eba0ae668d51401e0d525392ea95d0af52d" +content-hash = "0f151e30f9dff59463c7635ecb20c77817fdca510ad5fd7a9db13f05a23e79ae" diff --git a/pyproject.toml b/pyproject.toml index 57e5e23..67be9cf 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -19,27 +19,29 @@ pydantic = "^2.8.2" pathlib = "^1.0.1" typing-extensions = "^4.12.2" -[tool.poetry.dev-dependencies] +[tool.poetry.group.docs.dependencies] +mkdocs = "^1.6.0" +mkdocs-material = "^9.5.29" + +[tool.poetry.group.dev.dependencies] pytest = "^8.2" pytest-cov = "^5.0.0" pytest-mypy = "^0.10.0" pytest-mock = "^3.7.0" pre-commit = "^3.0.4" ruff = "^0.5.2" - -[tool.poetry.group.docs.dependencies] -mkdocs = "^1.6.0" -mkdocs-material = "^9.5.29" +types-toml = "^0.10.8.20240310" [build-system] requires = ["poetry-core>=1.0.0"] build-backend = "poetry.core.masonry.api" [tool.mypy] +ignore_missing_imports = true disallow_any_explicit = true disallow_any_generics = true warn_unreachable = true -warn_unused_ignores = true +warn_unused_ignores = false disallow_untyped_defs = true exclude = [".venv/"] From 9ad667732cb622ab2600790392a92e3dfa459b61 Mon Sep 17 00:00:00 2001 From: Diego Alonso Alvarez Date: Wed, 21 Aug 2024 13:52:18 +0100 Subject: [PATCH 17/23] Update pre-commit hooks with extra deps --- .pre-commit-config.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index e0de92c..af1f6b6 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -27,6 +27,7 @@ repos: rev: "v1.10.0" hooks: - id: mypy + additional_dependencies: ["types-toml"] - repo: https://github.com/igorshubovych/markdownlint-cli rev: v0.40.0 hooks: From f004b2a1c2f6ac7e47af5b381ccb963ce1a436ce Mon Sep 17 00:00:00 2001 From: Diego Alonso Alvarez Date: Wed, 21 Aug 2024 13:53:18 +0100 Subject: [PATCH 18/23] Fix typing --- bubble_analyser/Config.py | 2 +- .../background_subtraction_threshold.py | 39 +++++++++------ .../calculate_circle_properties.py | 3 +- bubble_analyser/calculate_px2cm.py | 36 ++++++------- bubble_analyser/default.py | 43 ++++++++-------- bubble_analyser/image_preprocess.py | 50 +++++++++++-------- bubble_analyser/morphological_process.py | 5 +- bubble_analyser/threshold.py | 18 ++++--- 8 files changed, 109 insertions(+), 87 deletions(-) diff --git a/bubble_analyser/Config.py b/bubble_analyser/Config.py index 7b58afa..9f6dbee 100644 --- a/bubble_analyser/Config.py +++ b/bubble_analyser/Config.py @@ -34,7 +34,7 @@ ) -class Config(BaseModel): +class Config(BaseModel): # type: ignore """A Pydantic model for storing and validating configuration parameters. The class contains parameters for image processing and analysis, such as diff --git a/bubble_analyser/background_subtraction_threshold.py b/bubble_analyser/background_subtraction_threshold.py index 7963111..350878e 100644 --- a/bubble_analyser/background_subtraction_threshold.py +++ b/bubble_analyser/background_subtraction_threshold.py @@ -1,56 +1,63 @@ +from typing import cast + import cv2 import numpy as np +from numpy import typing as npt -def convert_grayscale(image: np.ndarray) -> np.ndarray: +def convert_grayscale(image: npt.NDArray[np.int_]) -> npt.NDArray[np.int_]: """Converts an image to grayscale. Args: - image (np.ndarray): The input image to be converted. + image (npt.NDArray): The input image to be converted. Returns: - np.ndarray: The converted image in grayscale. + npt.NDArray: The converted image in grayscale. """ if len(image.shape) == 3: - image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + image = cast(npt.NDArray[np.int_], cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)) return image def background_subtraction( - target_img: np.ndarray, background_img: np.ndarray -) -> np.ndarray: + target_img: npt.NDArray[np.int_], background_img: npt.NDArray[np.int_] +) -> npt.NDArray[np.int_]: """Performs background subtraction on two images. Args: - target_img (np.ndarray): The target image where the objects of interest are located. - background_img (np.ndarray): The background image (without objects of interest). + target_img (npt.NDArray): The target image where the objects of interest are located. + background_img (npt.NDArray): The background image (without objects of interest). Returns: - np.ndarray: The difference image after background subtraction. + npt.NDArray: The difference image after background subtraction. """ difference_img = cv2.absdiff(target_img, background_img) - return difference_img + return cast(npt.NDArray[np.int_], difference_img) -def threshold(difference_img: np.ndarray, threshold_value: int) -> np.ndarray: +def threshold( + difference_img: npt.NDArray[np.int_], threshold_value: float +) -> npt.NDArray[np.bool_]: """Applies a binary threshold to the given difference image. Args: - difference_img (np.ndarray): The input difference image to be thresholded. + difference_img (npt.NDArray): The input difference image to be thresholded. threshold_value (int): The threshold value to apply to the difference image. Returns: - np.ndarray: The thresholded image. + npt.NDArray: The thresholded image. """ _, thresholded_img = cv2.threshold( difference_img, threshold_value, 255, cv2.THRESH_BINARY ) - return thresholded_img + return cast(npt.NDArray[np.bool_], thresholded_img) def background_subtraction_threshold( - target_img: np.ndarray, background_img: np.ndarray, threshold_value: int -) -> np.ndarray: + target_img: npt.NDArray[np.int_], + background_img: npt.NDArray[np.int_], + threshold_value: float, +) -> npt.NDArray[np.bool_]: """Perform background subtraction and apply thresholding. Args: diff --git a/bubble_analyser/calculate_circle_properties.py b/bubble_analyser/calculate_circle_properties.py index f176446..001233f 100644 --- a/bubble_analyser/calculate_circle_properties.py +++ b/bubble_analyser/calculate_circle_properties.py @@ -1,9 +1,10 @@ import numpy as np +from numpy import typing as npt from skimage import measure def calculate_circle_properties( - labels: np.ndarray, px2cm: float + labels: npt.NDArray[np.int_], px2cm: float ) -> list[dict[str, float]]: """Calculate geometric properties of labeled regions in an image. diff --git a/bubble_analyser/calculate_px2cm.py b/bubble_analyser/calculate_px2cm.py index bff2a0b..0284bc9 100644 --- a/bubble_analyser/calculate_px2cm.py +++ b/bubble_analyser/calculate_px2cm.py @@ -1,21 +1,24 @@ +from pathlib import Path +from typing import cast + import cv2 import numpy as np -from numpy.typing import NDArray +import numpy.typing as npt from .image_preprocess import load_image def resize_to_target_width( - image: NDArray[np.uint8], target_width: int = 1000 -) -> tuple[np.ndarray, float]: + image: npt.NDArray[np.int_], target_width: int = 1000 +) -> tuple[npt.NDArray[np.int_], float]: """Resizes an image to a specified target width while maintaining the aspect ratio. Args: - image (NDArray[np.uint8]): The input image to be resized. + image (npt.npt.NDArray[np.int_]): The input image to be resized. target_width (int, optional): The desired width of the resized image. Defaults to 1000. Returns: - NDArray[np.uint8]: The resized image. + npt.npt.NDArray[np.int_]: The resized image. scale_percent: The scaling percentage applied to the image during resizing. """ # Scale down the image to a width of 1000 pixels, keeping the aspect ratio the same @@ -27,9 +30,11 @@ def resize_to_target_width( image.shape[0] * scale_percent ) # Adjust the height to maintain the aspect ratio dim: tuple[int, int] = (width, height) # Define the new dimensions - image = cv2.resize(image, dim, interpolation=cv2.INTER_AREA) # Resize the image + image_resized = cv2.resize( + image, dim, interpolation=cv2.INTER_AREA + ) # Resize the image - return image, scale_percent + return cast(npt.NDArray[np.int_], image_resized), scale_percent def draw_line(event: int, x: int, y: int, flags: int, param: dict[str, object]) -> None: @@ -44,12 +49,9 @@ def draw_line(event: int, x: int, y: int, flags: int, param: dict[str, object]) flags: Any relevant flags passed by OpenCV. param: A dictionary containing reference points and drawing state. """ - refPt, drawing, img, img_copy = ( - param["refPt"], - param["drawing"], - param["img"], - param["img_copy"], - ) + refPt = cast(list[tuple[int, int]], param["refPt"]) + img = cast(npt.NDArray[np.uint8], param["img"]) + img_copy = cast(npt.NDArray[np.uint8], param["img_copy"]) if event == cv2.EVENT_LBUTTONDOWN: refPt.append((x, y)) @@ -68,7 +70,7 @@ def draw_line(event: int, x: int, y: int, flags: int, param: dict[str, object]) cv2.imshow("image", img) -def get_pixel_distance(img: NDArray[np.uint8]) -> float: +def get_pixel_distance(img: npt.NDArray[np.int_]) -> float: """Display the image and allow the user to draw a line representing 1 cm. This function uses OpenCV to display the image and capture the user input @@ -84,12 +86,12 @@ def get_pixel_distance(img: NDArray[np.uint8]) -> float: """ refPt: list[tuple[int, int]] = [] drawing: bool = False - img_copy: NDArray[np.uint8] = img.copy() + img_copy: npt.NDArray[np.int_] = img.copy() cv2.namedWindow("image") cv2.setMouseCallback( "image", - draw_line, + draw_line, # type: ignore {"refPt": refPt, "drawing": drawing, "img": img, "img_copy": img_copy}, ) @@ -135,7 +137,7 @@ def get_cm_per_pixel( return cm_per_pixel -def calculate_px2cm(image_path: str, img_resample: float) -> float: +def calculate_px2cm(image_path: Path, img_resample: float) -> float: """Calculates the conversion factor from pixels to centimeters. This function reads an image of a ruler, allows the user to draw a line corresponding to 1 cm on the ruler, and calculates the pixel-to-centimeter conversion factor. The diff --git a/bubble_analyser/default.py b/bubble_analyser/default.py index 80ce331..b3b8cf3 100644 --- a/bubble_analyser/default.py +++ b/bubble_analyser/default.py @@ -34,6 +34,7 @@ import matplotlib.pyplot as plt import numpy as np import toml as tomllib +from numpy import typing as npt from skimage import ( color, io, @@ -43,13 +44,15 @@ from .calculate_circle_properties import calculate_circle_properties from .calculate_px2cm import calculate_px2cm -from .Config import Config +from .config import Config from .image_preprocess import image_preprocess from .morphological_process import morphological_process from .threshold import threshold -def load_image(image_path: str, img_resample: float) -> tuple[np.ndarray, np.ndarray]: +def load_image( + image_path: str, img_resample: float +) -> tuple[npt.NDArray[np.int_], npt.NDArray[np.int_]]: """Read and preprocess the input image. This function loads an image from the specified path, resizes it according to the @@ -101,20 +104,16 @@ def load_toml(file_path: str) -> Config: Returns: A dictionary containing the configuration parameters from the TOML file. """ - # with open(file_path, "rb") as f: - # toml_data: dict[str, str | int | float] = tomllib.load(f) - # return toml_data - with open(file_path, "rb") as f: - toml_data = tomllib.load(f) + toml_data = tomllib.load(file_path) - return Config.Config(**toml_data) + return Config(**toml_data) def run_algorithm( - target_img: np.ndarray, - bknd_img: np.ndarray, - imgRGB: np.ndarray, - params: Config.Config, + target_img: npt.NDArray[np.int_], + bknd_img: npt.NDArray[np.int_], + imgRGB: npt.NDArray[np.int_], + params: Config, px2cm: float, threshold_value: float, ) -> None: @@ -158,8 +157,8 @@ def run_algorithm( # Apply thresholding and morphological processing plt.subplot(232) - imgThreshold = threshold(target_img, bknd_img, threshold_value) - imgThreshold = morphological_process(imgThreshold, element_size) + imgThreshold_ = threshold(target_img, bknd_img, threshold_value) + imgThreshold = morphological_process(imgThreshold_, element_size) plt.title("2. Thresh&morph process") plt.imshow(imgThreshold * 255, cmap="gray") @@ -179,7 +178,7 @@ def run_algorithm( # Apply connected component labeling plt.subplot(235) - distThresh = np.uint8(distThresh) + distThresh = distThresh.astype(np.uint8) _, labels = cv2.connectedComponents(distThresh) plt.title("5. Labels") plt.imshow(labels) @@ -187,8 +186,8 @@ def run_algorithm( # Apply watershed segmentation plt.figure() plt.subplot(121) - labels = np.int32(labels) - labels = cv2.watershed(imgRGB, labels) + labels = labels.astype(np.int32) + labels = cv2.watershed(imgRGB, labels).astype(np.int_) plt.title("6. Final graph after watershed") plt.imshow(labels) @@ -218,11 +217,11 @@ def default() -> None: params = load_toml("./bubble_analyser/config.toml") # Read path and image resample factor - ruler_img_path: str = params.ruler_img_path - target_img_path: str = params.target_img_path - bknd_img_path: str = params.background_img_path - img_resample_factor: float = params.resample - threshold_value: float = params.threshold_value + ruler_img_path = params.ruler_img_path + target_img_path = params.target_img_path + bknd_img_path = params.background_img_path + img_resample_factor = params.resample + threshold_value = params.threshold_value # Calculate the pixel to cm ratio px2cm = calculate_px2cm(ruler_img_path, img_resample_factor) diff --git a/bubble_analyser/image_preprocess.py b/bubble_analyser/image_preprocess.py index 6f6013d..b334596 100644 --- a/bubble_analyser/image_preprocess.py +++ b/bubble_analyser/image_preprocess.py @@ -1,5 +1,9 @@ +from pathlib import Path +from typing import cast + import cv2 import numpy as np +from numpy import typing as npt from skimage import ( color, io, @@ -7,14 +11,14 @@ ) -def load_image(image_path: str) -> np.ndarray: +def load_image(image_path: Path) -> npt.NDArray[np.int_]: """Read and preprocess the input image. Args: image_path (str): The file path of the image to load. Returns: - np.ndarray: The image read in ndarray format. + npt.NDArray: The image read in ndarray format. """ # Read the input image @@ -23,65 +27,69 @@ def load_image(image_path: str) -> np.ndarray: return img -def get_greyscale(image: np.ndarray) -> np.ndarray: +def get_greyscale(image: npt.NDArray[np.int_]) -> npt.NDArray[np.int_]: """Converts an image to grayscale if it is in RGB format. Args: - image (np.ndarray): The input image to be converted. + image (npt.NDArray): The input image to be converted. Returns: - np.ndarray: The grayscale image. + npt.NDArray: The grayscale image. """ if image.ndim > 2: image = color.rgb2gray(image) # Convert to grayscale if the image is in RGB return image -def get_RGB(image: np.ndarray) -> np.ndarray: +def get_RGB(image: npt.NDArray[np.int_]) -> npt.NDArray[np.int_]: """Converts an image from BGR color space to RGB color space. Args: - image (np.ndarray): The input image in BGR format. + image (npt.NDArray): The input image in BGR format. Returns: - np.ndarray: The converted image in RGB format. + npt.NDArray: The converted image in RGB format. """ imgRGB = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) - return imgRGB + return cast(npt.NDArray[np.int_], imgRGB) -def resize_for_RGB(image: np.ndarray, img_resample_factor: float) -> np.ndarray: +def resize_for_RGB( + image: npt.NDArray[np.int_], img_resample_factor: float +) -> npt.NDArray[np.int_]: """Resizes an image in RGB format based on the provided resampling factor using method provided by openCV. Args: - image (np.ndarray): The input image in RGB format. + image (npt.NDArray): The input image in RGB format. img_resample_factor (float): The factor by which the image will be resampled. Returns: - np.ndarray: The resized image in RGB format. + npt.NDArray: The resized image in RGB format. """ scale_percent = img_resample_factor * 100 # percent of original size width = int(image.shape[1] * scale_percent / 100) height = int(image.shape[0] * scale_percent / 100) img_resample_dimension = (width, height) - image = cv2.resize(image, img_resample_dimension, interpolation=cv2.INTER_AREA) - return image + image_resized = cv2.resize( + image, img_resample_dimension, interpolation=cv2.INTER_AREA + ) + return cast(npt.NDArray[np.int_], image_resized) def resize_for_original_image( - image: np.ndarray, img_resample_factor: float -) -> np.ndarray: + image: npt.NDArray[np.int_], img_resample_factor: float +) -> npt.NDArray[np.int_]: """Resizes an image based on the provided resampling factor for original image using method provided by skimage. Args: - image (np.ndarray): The input image to be resized. + image (npt.NDArray): The input image to be resized. img_resample_factor (float): The factor by which the image will be resampled. Returns: - np.ndarray: The resized image. + npt.NDArray: The resized image. """ image = transform.resize( image, @@ -95,8 +103,8 @@ def resize_for_original_image( def image_preprocess( - img_path: str, img_resample: float -) -> tuple[np.ndarray, np.ndarray]: + img_path: Path, img_resample: float +) -> tuple[npt.NDArray[np.int_], npt.NDArray[np.int_]]: """Preprocesses an image by loading it, converting it to 1. RGB, 2. grayscale and resizing it based on a provided resampling factor. The resized grayscale image (img) is for use in the following "default" watershed algorithm @@ -109,7 +117,7 @@ def image_preprocess( img_resample (float): The resampling factor to apply to the image. Returns: - tuple[np.ndarray, np.ndarray]: A tuple containing the resized grayscale image + tuple[npt.NDArray, npt.NDArray]: A tuple containing the resized grayscale image and the resized RGB image. """ image = load_image(img_path) diff --git a/bubble_analyser/morphological_process.py b/bubble_analyser/morphological_process.py index f1f49f7..24b65e8 100644 --- a/bubble_analyser/morphological_process.py +++ b/bubble_analyser/morphological_process.py @@ -1,4 +1,5 @@ import numpy as np +from numpy import typing as npt from scipy import ndimage from skimage import ( morphology, @@ -6,7 +7,9 @@ ) -def morphological_process(target_img: np.ndarray, element_size: int) -> np.ndarray: +def morphological_process( + target_img: npt.NDArray[np.bool_], element_size: int +) -> npt.NDArray[np.int_]: """Apply morphological operations to process the target image. This function performs a series of morphological operations on the input image, diff --git a/bubble_analyser/threshold.py b/bubble_analyser/threshold.py index db2c076..15864a3 100644 --- a/bubble_analyser/threshold.py +++ b/bubble_analyser/threshold.py @@ -1,5 +1,5 @@ import numpy as np -from numpy.typing import NDArray +from numpy import typing as npt from skimage import ( filters, ) @@ -7,7 +7,7 @@ from .background_subtraction_threshold import background_subtraction_threshold -def otsu_threshold(target_img: np.ndarray) -> np.ndarray: +def otsu_threshold(target_img: npt.NDArray[np.int_]) -> npt.NDArray[np.bool_]: """Apply Otsu's thresholding to the target image and return an inverted binary mask. This function takes a target image and a background image. It applies Otsu's thresholding @@ -23,7 +23,7 @@ def otsu_threshold(target_img: np.ndarray) -> np.ndarray: Returns: A binary (inverted) image where the foreground and background are swapped. """ - binary_image: NDArray[np.bool_] = target_img > filters.threshold_otsu( + binary_image = target_img > filters.threshold_otsu( target_img ) # Binary image using Otsu's thresholding return ~binary_image # Invert the binary image @@ -47,17 +47,19 @@ def select_threshold_method() -> str: def threshold( - target_img: np.ndarray, bknd_img: np.ndarray, threshold_value: float -) -> np.ndarray: + target_img: npt.NDArray[np.int_], + bknd_img: npt.NDArray[np.int_], + threshold_value: float, +) -> npt.NDArray[np.bool]: """Applies a threshold to the target image based on the selected method. Args: - target_img (np.ndarray): The target image to apply the threshold to. - bknd_img (np.ndarray): The background image used for background subtraction. + target_img (npt.NDArray): The target image to apply the threshold to. + bknd_img (npt.NDArray): The background image used for background subtraction. threshold_value (float): The threshold value used for background subtraction. Returns: - np.ndarray: The thresholded image. + npt.NDArray: The thresholded image. """ method = select_threshold_method() if method == "OTSU's method": From 3590ac3e82b8a4ddd1c53e603e6103f0949fe986 Mon Sep 17 00:00:00 2001 From: Diego Alonso Alvarez Date: Wed, 21 Aug 2024 14:17:36 +0100 Subject: [PATCH 19/23] Fix config file --- bubble_analyser/config.toml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/bubble_analyser/config.toml b/bubble_analyser/config.toml index 848f1a4..143bccc 100644 --- a/bubble_analyser/config.toml +++ b/bubble_analyser/config.toml @@ -17,13 +17,13 @@ Marker_size_range = [2, 30] # Images can be resampled to make processing faster resample = 0.5 -resample_range = [0.1, 1] +resample_range = [0.1, 1.0] # Reject abnormal bubbles from quantification. E>0.85 or S<0.9 Max_Eccentricity = 0.85 -Max_Eccentricity_range = [0.1, 1] +Max_Eccentricity_range = [0.1, 1.0] Min_Solidity = 0.9 -Min_Solidity_range = [0.1, 1] +Min_Solidity_range = [0.1, 1.0] # Also ignore too small bubbles (equivalent diameter in mm) min_size = 0.1 From b1b5b28bcd8a0b8a339528cc7ee3823d7c9b9d15 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Diego=20Alonso=20=C3=81lvarez?= <6095790+dalonsoa@users.noreply.github.com> Date: Thu, 22 Aug 2024 09:58:28 +0100 Subject: [PATCH 20/23] Rename Config.py to config.py --- bubble_analyser/{Config.py => config.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename bubble_analyser/{Config.py => config.py} (100%) diff --git a/bubble_analyser/Config.py b/bubble_analyser/config.py similarity index 100% rename from bubble_analyser/Config.py rename to bubble_analyser/config.py From 224e2fcb17608fbd283cda14a55a4e89e5143229 Mon Sep 17 00:00:00 2001 From: Yiyang Date: Sat, 31 Aug 2024 17:35:58 +0100 Subject: [PATCH 21/23] Fix issues of overlong lines and docstrings --- .../background_subtraction_threshold.py | 30 ++++++++- .../calculate_circle_properties.py | 44 +++++++++++-- bubble_analyser/calculate_px2cm.py | 62 +++++++++++++++---- bubble_analyser/default.py | 2 + bubble_analyser/image_preprocess.py | 52 +++++++++++++--- bubble_analyser/morphological_process.py | 22 ++++++- bubble_analyser/threshold.py | 39 ++++++++++-- pyproject.toml | 2 +- tests/calibration_files/config.toml | 10 +-- 9 files changed, 223 insertions(+), 40 deletions(-) diff --git a/bubble_analyser/background_subtraction_threshold.py b/bubble_analyser/background_subtraction_threshold.py index 350878e..12b84ac 100644 --- a/bubble_analyser/background_subtraction_threshold.py +++ b/bubble_analyser/background_subtraction_threshold.py @@ -1,3 +1,28 @@ +"""Background Subtraction and Thresholding: Isolate objects from backgrounds. + +This module provides tools for image preprocessing including grayscale conversion, +background subtraction, and thresholding. It is designed to handle images where objects +of interest need to be isolated from their backgrounds for further analysis. + +Functions: + convert_grayscale(image): Converts a color image to grayscale. + + background_subtraction(target_img, background_img): Subtracts the background image + from the target image to highlight differences. + + threshold(difference_img, threshold_value): Applies a binary threshold to an image + to create a binary mask. + + background_subtraction_threshold(target_img, background_img, threshold_value): + Combines background subtraction and thresholding to isolate objects of interest in + an image. + +These functions are used to preprocess images for applications such as object detection, +where isolating the changes between images or from a background is necessary. Each +function is designed to be modular, allowing them to be used independently or in +sequence depending on the requirements of the task. +""" + from typing import cast import cv2 @@ -25,8 +50,9 @@ def background_subtraction( """Performs background subtraction on two images. Args: - target_img (npt.NDArray): The target image where the objects of interest are located. - background_img (npt.NDArray): The background image (without objects of interest). + target_img (npt.NDArray): The target image where the objects of interest are + located. + background_img (npt.NDArray): The background image (without object of interest). Returns: npt.NDArray: The difference image after background subtraction. diff --git a/bubble_analyser/calculate_circle_properties.py b/bubble_analyser/calculate_circle_properties.py index 001233f..9272c0a 100644 --- a/bubble_analyser/calculate_circle_properties.py +++ b/bubble_analyser/calculate_circle_properties.py @@ -1,3 +1,35 @@ +"""Calculate Circle Properties. + +This module contains functions for calculating geometric properties of regions +identified in an image. It is particularly focused on regions that are labeled in terms +of their circularity attributes. + +The `calculate_circle_properties` function evaluates the geometric features of labeled +regions within an image, which have been identified as separate entities, often through +a segmentation process. It measures various properties related to the shape and size of +the regions, adjusted to real-world dimensions using a provided pixel-to-centimeter +conversion ratio. + +Function: + calculate_circle_properties(labels, px2cm): Computes area, equivalent diameter, + eccentricity, solidity, and circularity for each labeled region. + +Each computed property is defined as follows: +- Area: Total area of the region converted from pixels to square centimeters. +- Equivalent diameter: Diameter of a circle with the equivalent area as the region, + provided in centimeters. +- Eccentricity: Measure of the deviation of the region from a perfect circle, where + 0 indicates a perfect circle and values closer to 1 indicate elongated shapes. +- Solidity: Ratio of the region's area to the area of its convex hull, indicating + the compactness of the shape. +- Circularity: A value that describes how closely the shape of the region approaches + that of a perfect circle, calculated from the area and the perimeter. + +The function returns a list of dictionaries, with each dictionary holding the properties +for a specific region, facilitating easy access and manipulation of these metrics in +subsequent analysis or reporting stages. +""" + import numpy as np from numpy import typing as npt from skimage import measure @@ -10,8 +42,8 @@ def calculate_circle_properties( This function computes various properties that describe the "circularity" of regions within the labeled image, such as area, equivalent diameter, eccentricity, solidity, - and circularity. These properties are calculated in centimeters based on the provided - pixel-to-centimeter ratio. + and circularity. These properties are calculated in centimeters based on the + provided pixel-to-centimeter ratio. Args: labels: A labeled image where each distinct region (or "circle") is represented @@ -22,14 +54,14 @@ def calculate_circle_properties( Returns: A list of dictionaries, each containing the following properties for a region: - area: The area of the region in square centimeters. - - equivalent_diameter: The diameter of a circle with the same area as the region, - in centimeters. + - equivalent_diameter: The diameter of a circle with the same area as the region + , in centimeters. - eccentricity: The eccentricity of the ellipse that has the same second-moments as the region. - solidity: The proportion of the pixels in the convex hull that are also in the region. - - circularity: A measure of how close the shape is to a perfect circle, calculated - using the perimeter and area. + - circularity: A measure of how close the shape is to a perfect circle, + calculated using the perimeter and area. """ properties = measure.regionprops(labels) circle_properties = [] diff --git a/bubble_analyser/calculate_px2cm.py b/bubble_analyser/calculate_px2cm.py index 0284bc9..f0ad7fa 100644 --- a/bubble_analyser/calculate_px2cm.py +++ b/bubble_analyser/calculate_px2cm.py @@ -1,3 +1,36 @@ +"""Bubble Analyser: Image Processing for Circular Feature Detection. + +This module provides a suite of tools for image manipulation and measurement calibration +using computer vision techniques. It includes functions to resize images, draw on images +interactively, and calculate real-world measurements from pixels. + +The functions in this module utilize OpenCV and NumPy to perform tasks such as image +resizing, interactive line drawing for measurement marking, pixel distance calculations, +and conversionfrom pixel measurements to real-world units (e.g., centimeters). These +capabilities are particularly useful in applications where precision in spatial +measurements is required, such as in quality control, materials science, and medical +imaging. + +Key Functions: +- resize_to_target_width(image, target_width): Resizes an image to a specified target + width while maintaining the aspect ratio. +- draw_line(event, x, y, flags, param): A callback function that allows interactive line + drawing on an image displayed in an OpenCV window. +- get_pixel_distance(img): Displays an image and allows the user to draw a line, then + calculates the pixel distance between the endpoints of the line. +- get_cm_per_pixel(pixel_distance, scale_percent, img_resample): Calculates the + conversion ratio from pixels to centimeters, taking into account any image resizing + that has been applied. +- calculate_px2cm(image_path, img_resample): Orchestrates the process of loading an + image, resizing it, allowing the user to mark a measurement, and calculating a + pixel-to-centimeter conversion factor. + +Each function is designed to be modular, allowing for flexible integration into broader +image processing and analysis workflows. The module facilitates the extraction of +quantitative data from images, which can be critical for applications requiring detailed +spatial analysis. +""" + from pathlib import Path from typing import cast @@ -15,7 +48,8 @@ def resize_to_target_width( Args: image (npt.npt.NDArray[np.int_]): The input image to be resized. - target_width (int, optional): The desired width of the resized image. Defaults to 1000. + target_width (int, optional): The desired width of the resized image. Defaults + to 1000. Returns: npt.npt.NDArray[np.int_]: The resized image. @@ -40,10 +74,12 @@ def resize_to_target_width( def draw_line(event: int, x: int, y: int, flags: int, param: dict[str, object]) -> None: """Callback function to draw a line on the image. - This function is used as a mouse callback to allow the user to draw a line on the image. + This function is used as a mouse callback to allow the user to draw a line on the + image. Args: - event: The type of mouse event (e.g., left button down, mouse move, left button up). + event: The type of mouse event (e.g., left button down, mouse move, left button + up). x: The x-coordinate of the mouse event. y: The y-coordinate of the mouse event. flags: Any relevant flags passed by OpenCV. @@ -125,7 +161,8 @@ def get_cm_per_pixel( Args: pixel_distance: The distance in pixels between the two drawn points. scale_percent: The scaling percentage applied to the image during resizing. - img_resample: The resampling factor applied to the original target and background image. + img_resample: The resampling factor applied to the original target and + background image. Returns: The conversion factor in centimeters per pixel, corrected for the resampling @@ -139,18 +176,21 @@ def get_cm_per_pixel( def calculate_px2cm(image_path: Path, img_resample: float) -> float: """Calculates the conversion factor from pixels to centimeters. - This function reads an image of a ruler, allows the user to draw a line corresponding - to 1 cm on the ruler, and calculates the pixel-to-centimeter conversion factor. The - image is scaled down for easier interaction, but the final calculation accounts for - this scaling as well as the resample factor for target and background images to - ensure accuracy relative to the original image size. + + This function reads an image of a ruler, allows the user to draw a line + correspondingnto 1 cm on the ruler, and calculates the pixel-to-centimeter + conversion factor. The image is scaled down for easier interaction, but the final + calculation accounts forthis scaling as well as the resample factor for target and + background images to ensure accuracy relative to the original image size. Args: image_path (str): The path to the image file. - img_resample (float): The resampling factor applied to the original target and background image. + img_resample (float): The resampling factor applied to the original target and + background image. Returns: - float: The conversion factor in centimeters per pixel, corrected for the resampling applied to the original image. + float: The conversion factor in centimeters per pixel, corrected for the + resampling applied to the original image. """ image = load_image(image_path) image, scale_percent = resize_to_target_width(image) diff --git a/bubble_analyser/default.py b/bubble_analyser/default.py index b3b8cf3..0b7874c 100644 --- a/bubble_analyser/default.py +++ b/bubble_analyser/default.py @@ -243,3 +243,5 @@ def default() -> None: # Output the image that eliminate the bubbles being filtered out # Table and Histogram # Let user modify the parameters in UI + +# Merge default branchx diff --git a/bubble_analyser/image_preprocess.py b/bubble_analyser/image_preprocess.py index b334596..005fb6c 100644 --- a/bubble_analyser/image_preprocess.py +++ b/bubble_analyser/image_preprocess.py @@ -1,3 +1,37 @@ +"""Image Preprocessing Functions. + +This module provides a collection of functions for image loading, color space conversion +, and resizing. It supports the preprocessing steps required for image analysis tasks, +especially in contexts where images need to be adapted for algorithmic processing and +visualization. + +Key Functions: +- load_image(image_path): Loads an image from a specified path into a NumPy array. +- get_greyscale(image): Converts an RGB image to grayscale, facilitating algorithms + that require single-channel input. +- get_RGB(image): Converts an image from BGR (common in OpenCV) to RGB format, suitable + for consistent image display and processing. +- resize_for_RGB(image, img_resample_factor): Resizes an RGB image according to a + specified resampling factor, typically used to reduce the image size for faster + processing without losing significant detail. +- resize_for_original_image(image, img_resample_factor): Similar to resize_for_RGB but + uses skimage's transform for resizing, providing a high-quality downsampling suitable + for analytical purposes. +- image_preprocess(img_path, img_resample): Orchestrates the loading, converting, and + resizing of an image. It outputs both a grayscale version for processing and an RGB + version for visualization. + +These functions are designed to be modular and can be combined in different ways +depending on the specific requirements of the image processing task at hand. For example +, in a typical workflow for image analysis, an image might be loaded, converted to +grayscale for analysis, and also kept in RGB for result visualization. + +Usage: +These utilities are particularly useful in applications like computer vision and digital +image processing where preprocessing steps are crucial for subsequent analysis, such as +object detection, pattern recognition, and more. +""" + from pathlib import Path from typing import cast @@ -57,8 +91,7 @@ def get_RGB(image: npt.NDArray[np.int_]) -> npt.NDArray[np.int_]: def resize_for_RGB( image: npt.NDArray[np.int_], img_resample_factor: float ) -> npt.NDArray[np.int_]: - """Resizes an image in RGB format based on the provided resampling factor using method - provided by openCV. + """Resizes an image in RGB format based on the provided resampling factor. Args: image (npt.NDArray): The input image in RGB format. @@ -81,8 +114,7 @@ def resize_for_RGB( def resize_for_original_image( image: npt.NDArray[np.int_], img_resample_factor: float ) -> npt.NDArray[np.int_]: - """Resizes an image based on the provided resampling factor for original image using method - provided by skimage. + """Resizes an image based on the provided resampling factor for original image. Args: image (npt.NDArray): The input image to be resized. @@ -105,12 +137,12 @@ def resize_for_original_image( def image_preprocess( img_path: Path, img_resample: float ) -> tuple[npt.NDArray[np.int_], npt.NDArray[np.int_]]: - """Preprocesses an image by loading it, converting it to 1. RGB, 2. grayscale - and resizing it based on a provided resampling factor. - The resized grayscale image (img) is for use in the following "default" watershed algorithm - as a target image. And the RGB image (imgRGB) is for the visualization of the results. - They are resized in different ways for different use cases (the output format of the - methods are differnt). + """Load an image, resizing it based on a provided resampling factor. + + The resized grayscale image (img) is for use in the following "default" watershed + algorithm as a target image. And the RGB image (imgRGB) is for the visualization of + the results. They are resized in different ways for different use cases (the output + format of the methods are differnt). Args: img_path (str): The file path of the image to preprocess. diff --git a/bubble_analyser/morphological_process.py b/bubble_analyser/morphological_process.py index 24b65e8..e726869 100644 --- a/bubble_analyser/morphological_process.py +++ b/bubble_analyser/morphological_process.py @@ -1,3 +1,22 @@ +"""Morphological Processing Function for filling holes and clear borders. + +This module includes functions for advanced image processing using morphological +operations tailored to for filling holes and clear borders for further analysis. It +specifically focuses on refining the binary masks generated during image segmentation +processes. + +Function: +- morphological_process(target_img, element_size): Enhances a binary image by + applying morphological operations such as closing, hole filling, and border clearing. + +This function is particularly useful in contexts where binary images derived from +thresholding or other segmentation methods contain noise, small holes, or artifacts +that can interfere with further analysis. By using operations like closing to connect +nearby regions, filling holes to ensure that objects are solid, and clearing borders +to remove partial objects, this function prepares images for more reliable and robust +analysis. +""" + import numpy as np from numpy import typing as npt from scipy import ndimage @@ -19,7 +38,8 @@ def morphological_process( Args: target_img: A binary image (numpy array) where the regions of interest are typically in white (True) and the background in black (False). - se: A structuring element used for morphological closing, typically a disk-shaped array. + element_size: A structuring element used for morphological closing, typically a + disk-shaped array. Returns: diff --git a/bubble_analyser/threshold.py b/bubble_analyser/threshold.py index 15864a3..40ee358 100644 --- a/bubble_analyser/threshold.py +++ b/bubble_analyser/threshold.py @@ -1,3 +1,32 @@ +"""Main Thresholding Functions. + +This module provides functions for applying various thresholding techniques to images, +aimed at segmenting objects from their backgrounds. It includes implementations of +Otsu's method and a customizable thresholding approach that involves background +subtraction. + +Functions: +- otsu_threshold(target_img): Applies Otsu's method to a grayscale image to create + a binary mask, which is then inverted for consistency with other processing steps. +- select_threshold_method(): Provides an interactive prompt for the user to select + a thresholding method from available options, enhancing flexibility in choosing + the appropriate method for different scenarios. +- threshold(target_img, bknd_img, threshold_value): Applies the chosen threshold method + to the target image, supporting either Otsu's method or a custom threshold based on + background subtraction, determined by user input. + +These thresholding functions are adaptable to various use cases, from basic academic +projects to complex industrial applications requiring robust foreground-background +segmentation. They are particularly useful in workflows that require pre-processing +before detailed image analysis, such as feature detection or object classification. + +Usage: +The functions can be directly called with appropriate parameters, with `threshold` +function allowing for dynamic method selection based on runtime decisions. This +design ensures that users can select the most suitable thresholding technique +based on the specific characteristics of the images they are working with. +""" + import numpy as np from numpy import typing as npt from skimage import ( @@ -10,10 +39,11 @@ def otsu_threshold(target_img: npt.NDArray[np.int_]) -> npt.NDArray[np.bool_]: """Apply Otsu's thresholding to the target image and return an inverted binary mask. - This function takes a target image and a background image. It applies Otsu's thresholding - to the target image to create a binary mask where the foreground objects are separated - from the background. The binary mask is then inverted, so the foreground becomes the - background and vice versa, the inversion is for easier processing in following morphologcal process. + This function takes a target image and a background image. It applies Otsu's + thresholding to the target image to create a binary mask where the foreground + objects are separatedfrom the background. The binary mask is then inverted, so + the foreground becomes the background and vice versa, the inversion is for easier + processing in following morphologcal process. Args: target_img: A grayscale image (2D array) representing the target image. @@ -31,6 +61,7 @@ def otsu_threshold(target_img: npt.NDArray[np.int_]) -> npt.NDArray[np.bool_]: def select_threshold_method() -> str: """Selects a threshold method from a list of available options. + Prompts the user to choose a threshold method from the list of options. The options are displayed with their corresponding numbers, and the user is asked to input the number of their chosen method. diff --git a/pyproject.toml b/pyproject.toml index 67be9cf..a35802e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -11,13 +11,13 @@ authors = [ python = "^3.10" matplotlib = "^3.9.1.post1" toml = "^0.10.2" -opencv-python = "^4.10.0.84" numpy = "^2.0.1" scikit-image = "^0.24.0" scipy = "^1.14.0" pydantic = "^2.8.2" pathlib = "^1.0.1" typing-extensions = "^4.12.2" +opencv-python = "^4.10.0.84" [tool.poetry.group.docs.dependencies] mkdocs = "^1.6.0" diff --git a/tests/calibration_files/config.toml b/tests/calibration_files/config.toml index 1ab4b08..18bed01 100644 --- a/tests/calibration_files/config.toml +++ b/tests/calibration_files/config.toml @@ -1,5 +1,5 @@ -# Config file for bubble analyser - -[image_resolution] -value = 183.06589 -units = "px/mm" +# Config file for bubble analyser + +[image_resolution] +value = 183.06589 +units = "px/mm" From e64d76162d4bdb3564405daff93be32be0d772d9 Mon Sep 17 00:00:00 2001 From: Yiyang Date: Sat, 31 Aug 2024 19:18:30 +0100 Subject: [PATCH 22/23] Little modification to make the codes looks a bit more clean --- bubble_analyser/config.toml | 2 +- bubble_analyser/default.py | 5 +++-- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/bubble_analyser/config.toml b/bubble_analyser/config.toml index 143bccc..3b48e61 100644 --- a/bubble_analyser/config.toml +++ b/bubble_analyser/config.toml @@ -39,7 +39,7 @@ target_img_path = "./tests/sample_images/03.jpg" background_img_path = "./tests/calibration_files/Background.png" # Threshold value for background subtraction -threshold_value = 0.1 +threshold_value = 0.3 # Ruler image ruler_img_path = "./tests/calibration_files/Ruler.png" diff --git a/bubble_analyser/default.py b/bubble_analyser/default.py index 0b7874c..3242eae 100644 --- a/bubble_analyser/default.py +++ b/bubble_analyser/default.py @@ -182,6 +182,7 @@ def run_algorithm( _, labels = cv2.connectedComponents(distThresh) plt.title("5. Labels") plt.imshow(labels) + plt.savefig("plot_1.png") # Apply watershed segmentation plt.figure() @@ -193,6 +194,7 @@ def run_algorithm( # Display the images plt.show() + plt.savefig("plot_2.png") # Calculate and print the circle properties circle_properties = calculate_circle_properties(labels, px2cm) @@ -243,5 +245,4 @@ def default() -> None: # Output the image that eliminate the bubbles being filtered out # Table and Histogram # Let user modify the parameters in UI - -# Merge default branchx +# Merge default branch From 420bedfd0a1ba55dd3ff80d090efe0fa30d27dcf Mon Sep 17 00:00:00 2001 From: Yiyang Date: Sat, 31 Aug 2024 19:21:09 +0100 Subject: [PATCH 23/23] Delete unnecessary commands --- bubble_analyser/default.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/bubble_analyser/default.py b/bubble_analyser/default.py index 3242eae..6fba838 100644 --- a/bubble_analyser/default.py +++ b/bubble_analyser/default.py @@ -182,7 +182,6 @@ def run_algorithm( _, labels = cv2.connectedComponents(distThresh) plt.title("5. Labels") plt.imshow(labels) - plt.savefig("plot_1.png") # Apply watershed segmentation plt.figure() @@ -194,7 +193,6 @@ def run_algorithm( # Display the images plt.show() - plt.savefig("plot_2.png") # Calculate and print the circle properties circle_properties = calculate_circle_properties(labels, px2cm)

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