From d0320f42e1f809341123960245b9d1bdbd364af0 Mon Sep 17 00:00:00 2001 From: saharshleo Date: Sun, 12 Jul 2020 21:19:32 +0530 Subject: [PATCH] v1.0 -- inference(4 seconds CPU) --- .gitignore | 3 + development/image.py | 407 +++++++++++++++++++++++ development/solve_image.py | 426 +++++++++++++++++++++++++ development/solver.py | 132 ++++++++ digit_classifier/recognize_digit.ipynb | 261 +++++++++++++++ run.py | 126 ++++++++ test_imgs/sudoku1.png | Bin 0 -> 8009 bytes test_imgs/sudoku10.jpg | Bin 0 -> 205669 bytes test_imgs/sudoku2.png | Bin 0 -> 10610 bytes test_imgs/sudoku3.jpg | Bin 0 -> 39175 bytes test_imgs/sudoku4.jpg | Bin 0 -> 230239 bytes test_imgs/sudoku5.jpg | Bin 0 -> 88070 bytes test_imgs/sudoku6.jpg | Bin 0 -> 2090019 bytes test_imgs/sudoku7.jpg | Bin 0 -> 180446 bytes test_imgs/sudoku8.jpg | Bin 0 -> 921015 bytes test_imgs/sudoku9.png | Bin 0 -> 123964 bytes utils/classify_digit.py | 59 ++++ utils/img_processing.py | 304 ++++++++++++++++++ utils/solve_sudoku.py | 77 +++++ 19 files changed, 1795 insertions(+) create mode 100644 .gitignore create mode 100644 development/image.py create mode 100644 development/solve_image.py create mode 100644 development/solver.py create mode 100644 digit_classifier/recognize_digit.ipynb create mode 100644 run.py create mode 100644 test_imgs/sudoku1.png create mode 100644 test_imgs/sudoku10.jpg create mode 100644 test_imgs/sudoku2.png create mode 100644 test_imgs/sudoku3.jpg create mode 100644 test_imgs/sudoku4.jpg create mode 100644 test_imgs/sudoku5.jpg create mode 100644 test_imgs/sudoku6.jpg create mode 100644 test_imgs/sudoku7.jpg create mode 100644 test_imgs/sudoku8.jpg create mode 100644 test_imgs/sudoku9.png create mode 100644 utils/classify_digit.py create mode 100644 utils/img_processing.py create mode 100644 utils/solve_sudoku.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..9019ed4 --- /dev/null +++ b/.gitignore @@ -0,0 +1,3 @@ +__pycache__/ +.ipynb_checkpoints/ +models/ diff --git a/development/image.py b/development/image.py new file mode 100644 index 0000000..e1676ae --- /dev/null +++ b/development/image.py @@ -0,0 +1,407 @@ +import cv2 +import numpy as np +import time +from scipy.spatial import distance as dist +import tensorflow as tf + +import pprint +from copy import deepcopy + +from solver import * + +# ================================================== +import warnings +warnings.filterwarnings('ignore') + +# Just disables the warning, doesn't enable AVX/FMA +import os +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' +# ================================================== + +def preprocess_image(img, blur_type): + gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + + if blur_type == 'gaussian' or blur_type == 'Gaussian': + blur = cv2.GaussianBlur(gray, (11, 11), 0) + # cv2.imshow('gaussian_blur', blur) + elif blur_type == 'median' or blur_type == 'Median': + blur = cv2.medianBlur(gray, 5) + # cv2.imshow('median_blur', blur) + elif blur_type == 'bilateral' or blur_type == 'Bilateral': + blur = cv2.bilateralFilter(gray, 9, 75, 75) + # cv2.imshow('bilateral_blur', blur) + + threshold = cv2.adaptiveThreshold(blur, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, \ + cv2.THRESH_BINARY, 11, 2) + + # Make edges and numbers white + negate = cv2.bitwise_not(threshold) + + # kernel = np.array([[0., 1., 0.], [1., 1., 1.], [0., 1., 0.]], dtype='uint8') + kernel = np.ones((3, 3), np.uint8) + dilation = cv2.dilate(negate, kernel, iterations = 1) + + opening = cv2.morphologyEx(negate, cv2.MORPH_OPEN, kernel) + + return dilation, opening + +def drawAllContours(processed_dilation): + contours, hierarchy = cv2.findContours(processed_dilation.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + + # Convert to RGB for drawing contours + processed = cv2.cvtColor(processed_dilation, cv2.COLOR_GRAY2RGB) + + all_contours_img = cv2.drawContours(processed.copy(), contours, -1, (0,255,0), 2) + # cv2.imshow('all_contours', all_contours_img) + + return all_contours_img, contours + +def drawExternalContours(processed_dilation): + ext_contours, hier = cv2.findContours(processed_dilation.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + + # Convert to RGB for drawing contours + processed = cv2.cvtColor(processed_dilation, cv2.COLOR_GRAY2RGB) + + external_contours_img = cv2.drawContours(processed.copy(), ext_contours, -1, (0,255,0), 2) + cv2.imshow('ext_contours', external_contours_img) + + return external_contours_img, ext_contours + +def find_corners_of_largest_contour(contours, img): + cnt = max(contours, key=cv2.contourArea) + + print("Contour Shape: {}".format(cnt.shape)) # N, 1, 2.......order_points require 2D Array + cnt = cnt.reshape((cnt.shape[0], 2)) # N x 2 + + # Order --> tl, tr, br, bl + corners = order_points_old(cnt) + # corners = order_points_quad(cnt) + + # determine the most extreme points along the contour + # extLeft = tuple(cnt[cnt[:, :, 0].argmin()][0]) + # extRight = tuple(cnt[cnt[:, :, 0].argmax()][0]) + # extTop = tuple(cnt[cnt[:, :, 1].argmin()][0]) + # extBot = tuple(cnt[cnt[:, :, 1].argmax()][0]) + # corners = np.array([extLeft, extRight, extTop, extBot]) + + return corners + +def order_points_old(pts): + # the top-left point will have the smallest sum, whereas + # the bottom-right point will have the largest sum + s = pts.sum(axis=1) + tl = pts[np.argmin(s)] + br = pts[np.argmax(s)] + + # now, compute the difference between the points (y-x), the + # top-right point will have the smallest difference, + # whereas the bottom-left will have the largest difference + diff = np.diff(pts, axis=1) + tr = pts[np.argmin(diff)] + bl = pts[np.argmax(diff)] + + # return the ordered coordinates + return np.array([tl, tr, br, bl], dtype="float32") + +def order_points_quad(pts): + # sort the points based on their x-coordinates + xSorted = pts[np.argsort(pts[:, 0]), :] + + # grab the left-most and right-most points from the sorted + # x-roodinate points + leftMost = xSorted[:2, :] + rightMost = xSorted[-1:-3:-1, :] + + # now, sort the left-most coordinates according to their + # y-coordinates so we can grab the top-left and bottom-left + # points, respectively + leftMost = leftMost[np.argsort(leftMost[:, 1]), :] + (tl, bl) = leftMost + + # now that we have the top-left coordinate, use it as an + # anchor to calculate the Euclidean distance between the + # top-left and right-most points; by the Pythagorean + # theorem, the point with the largest distance will be + # our bottom-right point + D = dist.cdist(tl[np.newaxis], rightMost, "euclidean")[0] + (br, tr) = rightMost[np.argsort(D)[::-1], :] + + # return the coordinates in top-left, top-right, + # bottom-right, and bottom-left order + return np.array([tl, tr, br, bl], dtype="float32") + +# get perspective transform matrix +def get_transform(pts): + (tl, tr, br, bl) = pts + + # compute the width of the new image, which will be the + # maximum distance between bottom-right and bottom-left + # x-coordiates or the top-right and top-left x-coordinates + widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2)) + widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2)) + maxWidth = max(int(widthA), int(widthB)) + + # compute the height of the new image, which will be the + # maximum distance between the top-right and bottom-right + # or the top-left and bottom-left + heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2)) + heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2)) + maxHeight = max(int(heightA), int(heightB)) + + # now that we have the dimensions of the new image, construct + # the set of destination points to obtain a "birds eye view", + # (i.e. top-down view) of the image, again specifying points + # in the top-left, top-right, bottom-right, and bottom-left + dst = np.array([ + [0, 0], + [maxWidth - 1, 0], + [maxWidth - 1, maxHeight - 1], + [0, maxHeight - 1]], dtype = "float32") + # compute the perspective transform matrix and then apply it + M = cv2.getPerspectiveTransform(pts, dst) + + return M, maxWidth, maxHeight + +def infer_grid(img): + divided_grid = [] + grid_img = img.copy() + height, width = grid_img.shape[:-1] + grid_width, grid_height = width / 9, height / 9 + for j in range(9): + for i in range(9): + topLeft = (int(i*grid_width), int(j*grid_height)) + bottomRight = (int((i+1)*grid_width), int((j+1)*grid_height)) + cv2.rectangle(grid_img, topLeft, bottomRight, (0, 0, 255), 2) + divided_grid.append((topLeft, bottomRight)) + + return grid_img, divided_grid + +def get_digits(warped, divided_grid, size_ocr=28, blur_type='gaussian', process='dilation'): + digits = [] + # processed_dilation, processed_opening = preprocess_image(warped, blur_type) + + # processed_img = processed_opening if process == 'opening' else processed_dilation + + for grid in divided_grid: + cell = warped[grid[0][1]:grid[1][1], grid[0][0]:grid[1][0]] + digits.append(extract_digit_from_cell(cell, size_ocr)) + + return digits + +def extract_digit_from_cell(cell, output_size): + height, width = cell.shape[:2] + margin = int(np.mean([height, width])/4.5) + bbox = find_largest_feature(cell.copy(), [margin, margin], [width-margin, height-margin]) + + digit = cell[int(bbox[0][1]):int(bbox[1][1]), int(bbox[0][0]):int(bbox[1][0])] + # scale and pad + w = bbox[1][0] - bbox[0][0] + h = bbox[1][1] - bbox[0][1] + + # Ignore any small bounding boxes + if w > 0 and h > 0 and (w * h) > 100 and len(digit) > 0: + return scale_and_centre(digit, output_size, 10) + else: + return np.zeros((output_size, output_size), np.uint8) + + return digit + +def find_largest_feature(cell, topLeft=None, bottomRight=None): + cell_copy = cell.copy() + # floodfill --> https://medium.com/@elvisdias/introduction-to-opencv-with-python-i-9fc72082f230 + height, width = cell_copy.shape[:2] + max_area = 0 + seed_point = (None, None) + + if topLeft == None: + topLeft = [0, 0] + + if bottomRight == None: + bottomRight = [width, height] + + for x in range(topLeft[0], bottomRight[0]): + for y in range(topLeft[1], bottomRight[1]): + # Only operate on light or white squares + if cell_copy[y, x] == 255 and x < width and y < height: + area = cv2.floodFill(cell_copy, None, (x, y), 64) # why 64 + if area[0] > max_area: # Gets the maximum bound area which should be the grid + max_area = area[0] + seed_point = (x, y) + + # Colour everything grey (compensates for features outside of our middle scanning range) + for x in range(width): + for y in range(height): + if x < width and y < height and cell_copy[y, x] == 255: + cv2.floodFill(cell_copy, None, (x, y), 64) + + mask = np.zeros((height + 2, width + 2), np.uint8) # Mask that is 2 pixels bigger than the image + + # Highlight the main feature + if all(seed_point): + cv2.floodFill(cell_copy, mask, seed_point, 255) + + top, bottom, left, right = height, 0, width, 0 + + for x in range(width): + for y in range(height): + if cell_copy[y, x] == 64: + cv2.floodFill(cell_copy, mask, (x, y), 0) + + # Find the bounding parameters + elif cell_copy[y, x] == 255: + top = y if y < top else top + bottom = y if y > bottom else bottom + left = x if x < left else left + right = x if x > right else right + + bbox = [[left, top], [right, bottom]] + + return np.array(bbox, dtype='float32') + +def scale_and_centre(img, size, margin=0, background=0): + h, w = img.shape[:2] + + def centre_pad(length): + """Handles centering for a given length that may be odd or even.""" + if length % 2 == 0: + side1 = int((size - length) / 2) + side2 = side1 + else: + side1 = int((size - length) / 2) + side2 = side1 + 1 + return side1, side2 + + def scale(r, x): + return int(r * x) + + if h > w: + t_pad = int(margin / 2) + b_pad = t_pad + ratio = (size - margin) / h + w, h = scale(ratio, w), scale(ratio, h) + l_pad, r_pad = centre_pad(w) + else: + l_pad = int(margin / 2) + r_pad = l_pad + ratio = (size - margin) / w + w, h = scale(ratio, w), scale(ratio, h) + t_pad, b_pad = centre_pad(h) + + img = cv2.resize(img, (w, h)) + img = cv2.copyMakeBorder(img, t_pad, b_pad, l_pad, r_pad, cv2.BORDER_CONSTANT, None, background) + return cv2.resize(img, (size, size)) + +# Driver Code +if __name__=="__main__": + orig_img = cv2.imread('test_imgs/sudoku9.png') + print("Image Shape: {}".format(orig_img.shape)) + + orig_img = cv2.resize(orig_img, (450, 450)) + cv2.imshow('original', orig_img) + + # GAUSSIAN BLUR + start = time.time() + processed_dilation, processed_opening = preprocess_image(orig_img, 'gaussian') + + # Choose img to proceed with - dilation / opening + processed_img = processed_dilation + + all_contours_img, contours = drawAllContours(processed_img) + external_contours_img, ext_contours = drawExternalContours(processed_img) + + corners_list = find_corners_of_largest_contour(ext_contours, processed_img) + print("Corners: {}".format(corners_list.tolist())) + for corner in corners_list: + cv2.circle(external_contours_img, tuple(corner), 5, (0, 0, 255), -1) + # cv2.imshow('corners', external_contours_img) + + transformation_matrix, width, height = get_transform(corners_list) + warped_orig = cv2.warpPerspective(orig_img, transformation_matrix, (width, height)) + warped_opening = cv2.warpPerspective(processed_opening, transformation_matrix, (width, height)) + warped_dilation = cv2.warpPerspective(processed_dilation, transformation_matrix, (width, height)) + print("Warped Shape: {}".format(warped_orig.shape)) + # cv2.imshow('warped_orig', warped_orig) + # cv2.imshow('warped_opening', warped_opening) + + grid_img, divided_grid = infer_grid(warped_orig) + cv2.imshow('grid_img', grid_img) + + extracted_digits = get_digits(warped_dilation, divided_grid, size_ocr=28, blur_type='gaussian', process='dilation') + print((extracted_digits[0].shape)) + + # Sudoku + real = [[0, 0, 0, 0, 5, 4, 7, 1, 2], + [2, 0, 0, 0, 0, 6, 3, 5, 8], + [0, 5, 0, 0, 0, 3, 9, 4, 6], + [0, 0, 0, 0, 3, 7, 0, 6, 0], + [0, 0, 7, 0, 0, 9, 1, 0, 0], + [0, 4, 0, 6, 8, 1, 0, 0, 0], + [0, 0, 0, 4, 0, 8, 0, 2, 0], + [9, 0, 8, 1, 0, 2, 0, 0, 5], + [4, 2, 6, 3, 7, 5, 8, 9, 1]] + + # hmmm + error_count = 0 + classified_digit = [[0 for j in range(9)] for i in range(9)] + i, j = 0, 0 + kernel = np.ones((3, 3), np.uint8) + model = tf.keras.models.load_model('models/loss_0298.model') + for img in extracted_digits: + # test = np.array(img, np.uint8) + test = (cv2.erode(img.copy(), kernel, iterations=1)) + tf.keras.utils.normalize(test, axis=1) + # test = test.reshape(28, 28, 1) + if not np.any(test): + j = (j+1)%9 + if j == 0: + i += 1 + continue + pred = model.predict([test.tolist()]) + classified_digit[i][j] = np.argmax(pred[0]) + if real[i][j] != classified_digit[i][j]: + error_count = error_count+1 + j = (j+1)%9 + if j == 0: + i += 1 + pprint.pprint(classified_digit) + print("Wrongly classified: {}".format(error_count)) + + kernel = np.ones((3,3),np.uint8) + with_border = [cv2.copyMakeBorder(cv2.erode(img.copy(),kernel,iterations=1), 1, 1, 1, 1, cv2.BORDER_CONSTANT, None, (255,255,255)) for img in extracted_digits] + rows = [] + for i in range(9): + row = np.concatenate(with_border[i * 9:((i + 1) * 9)], axis=1) + rows.append(row) + extracted_digits = np.concatenate(rows) + cv2.imshow('extracted_digits', extracted_digits) + + solved_grid = deepcopy(classified_digit) + if(solve_sudoku(solved_grid)): + print_sudoku(solved_grid) + else: + print("No Solution Exists!") + + # pprint.pprint(classified_digit) + digit_image = np.zeros((warped_orig.shape[1], warped_orig.shape[0], 3), np.uint8) + font = cv2.FONT_HERSHEY_SIMPLEX + color = (0, 255, 0) + font_scale = 1 + thickness = 2 + offset_x = (divided_grid[0][1][0] - divided_grid[0][0][0])//3 + offset_y = (divided_grid[0][1][1] - divided_grid[0][0][1])//3 + for i in range(9): + for j in range(9): + if classified_digit[i][j] == 0 and solved_grid[i][j] != 0: + label = str(solved_grid[i][j]) + digit_image = cv2.putText(digit_image, label, (divided_grid[i*9+j][0][0]+offset_x, divided_grid[i*9+j][1][1]-offset_y), font, font_scale, color, thickness, cv2.LINE_AA) + cv2.imshow('solution', digit_image) + + final_img = np.zeros((orig_img.shape[1], orig_img.shape[0]), np.uint8) + final_img = cv2.warpPerspective(digit_image, transformation_matrix, (orig_img.shape[1], orig_img.shape[0]), final_img, cv2.WARP_INVERSE_MAP) + final_img = cv2.add(orig_img, final_img) + cv2.imshow('final', final_img) + + print("Inference Time: {} seconds".format(time.time() - start)) + + cv2.waitKey(0) + cv2.destroyAllWindows() \ No newline at end of file diff --git a/development/solve_image.py b/development/solve_image.py new file mode 100644 index 0000000..f2fa84b --- /dev/null +++ b/development/solve_image.py @@ -0,0 +1,426 @@ +import cv2 +import numpy as np +import time +from scipy.spatial import distance as dist +import tensorflow as tf + +import warnings +warnings.filterwarnings('ignore') + +# Just disables the warning, doesn't enable AVX/FMA +import os +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' + +def preprocess_image(img, blur_type): + gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + + if blur_type == 'gaussian' or blur_type == 'Gaussian': + blur = cv2.GaussianBlur(gray, (11, 11), 0) + # cv2.imshow('gaussian_blur', blur) + elif blur_type == 'median' or blur_type == 'Median': + blur = cv2.medianBlur(gray, 5) + # cv2.imshow('median_blur', blur) + elif blur_type == 'bilateral' or blur_type == 'Bilateral': + blur = cv2.bilateralFilter(gray, 9, 75, 75) + # cv2.imshow('bilateral_blur', blur) + + threshold = cv2.adaptiveThreshold(blur, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, \ + cv2.THRESH_BINARY, 11, 2) + + # Make edges and numbers white + negate = cv2.bitwise_not(threshold) + + kernel = np.array([[0., 1., 0.], [1., 1., 1.], [0., 1., 0.]], dtype='uint8') + # kernel = np.ones((3, 3), np.uint8) + dilation = cv2.dilate(negate, kernel, iterations = 1) + + opening = cv2.morphologyEx(negate, cv2.MORPH_OPEN, kernel) + + return dilation, opening + +def drawAllContours(processed_dilation): + contours, hierarchy = cv2.findContours(processed_dilation.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + + # Convert to RGB for drawing contours + processed = cv2.cvtColor(processed_dilation, cv2.COLOR_GRAY2RGB) + + all_contours_img = cv2.drawContours(processed.copy(), contours, -1, (0,255,0), 2) + # cv2.imshow('all_contours', all_contours_img) + + return all_contours_img, contours + +def drawExternalContours(processed_dilation): + ext_contours, hier = cv2.findContours(processed_dilation.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + + # Convert to RGB for drawing contours + processed = cv2.cvtColor(processed_dilation, cv2.COLOR_GRAY2RGB) + + external_contours_img = cv2.drawContours(processed.copy(), ext_contours, -1, (0,255,0), 2) + # cv2.imshow('ext_contours', external_contours_img) + + return external_contours_img, ext_contours + +def find_corners_of_largest_contour(contours, img): + cnt = max(contours, key=cv2.contourArea) + + print("Contour Shape: {}".format(cnt.shape)) # N, 1, 2.......order_points require 2D Array + cnt = cnt.reshape((cnt.shape[0], 2)) # N x 2 + + # Order --> tl, tr, br, bl + corners = order_points_old(cnt) + # corners = order_points_quad(cnt) + + # determine the most extreme points along the contour + # extLeft = tuple(cnt[cnt[:, :, 0].argmin()][0]) + # extRight = tuple(cnt[cnt[:, :, 0].argmax()][0]) + # extTop = tuple(cnt[cnt[:, :, 1].argmin()][0]) + # extBot = tuple(cnt[cnt[:, :, 1].argmax()][0]) + # corners = np.array([extLeft, extRight, extTop, extBot]) + + return corners + +def order_points_old(pts): + # the top-left point will have the smallest sum, whereas + # the bottom-right point will have the largest sum + s = pts.sum(axis=1) + tl = pts[np.argmin(s)] + br = pts[np.argmax(s)] + + # now, compute the difference between the points (y-x), the + # top-right point will have the smallest difference, + # whereas the bottom-left will have the largest difference + diff = np.diff(pts, axis=1) + tr = pts[np.argmin(diff)] + bl = pts[np.argmax(diff)] + + # return the ordered coordinates + return np.array([tl, tr, br, bl], dtype="float32") + +def order_points_quad(pts): + # sort the points based on their x-coordinates + xSorted = pts[np.argsort(pts[:, 0]), :] + + # grab the left-most and right-most points from the sorted + # x-roodinate points + leftMost = xSorted[:2, :] + rightMost = xSorted[-1:-3:-1, :] + + # now, sort the left-most coordinates according to their + # y-coordinates so we can grab the top-left and bottom-left + # points, respectively + leftMost = leftMost[np.argsort(leftMost[:, 1]), :] + (tl, bl) = leftMost + + # now that we have the top-left coordinate, use it as an + # anchor to calculate the Euclidean distance between the + # top-left and right-most points; by the Pythagorean + # theorem, the point with the largest distance will be + # our bottom-right point + D = dist.cdist(tl[np.newaxis], rightMost, "euclidean")[0] + (br, tr) = rightMost[np.argsort(D)[::-1], :] + + # return the coordinates in top-left, top-right, + # bottom-right, and bottom-left order + return np.array([tl, tr, br, bl], dtype="float32") + +# get perspective transform matrix +def get_transform(pts): + (tl, tr, br, bl) = pts + + # compute the width of the new image, which will be the + # maximum distance between bottom-right and bottom-left + # x-coordiates or the top-right and top-left x-coordinates + widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2)) + widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2)) + maxWidth = max(int(widthA), int(widthB)) + + # compute the height of the new image, which will be the + # maximum distance between the top-right and bottom-right + # or the top-left and bottom-left + heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2)) + heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2)) + maxHeight = max(int(heightA), int(heightB)) + + # now that we have the dimensions of the new image, construct + # the set of destination points to obtain a "birds eye view", + # (i.e. top-down view) of the image, again specifying points + # in the top-left, top-right, bottom-right, and bottom-left + dst = np.array([ + [0, 0], + [maxWidth - 1, 0], + [maxWidth - 1, maxHeight - 1], + [0, maxHeight - 1]], dtype = "float32") + # compute the perspective transform matrix and then apply it + M = cv2.getPerspectiveTransform(pts, dst) + + return M, maxWidth, maxHeight + +def infer_grid(img): + divided_grid = [] + grid_img = img.copy() + height, width = grid_img.shape[:-1] + grid_width, grid_height = width / 9, height / 9 + for j in range(9): + for i in range(9): + topLeft = (int(i*grid_width), int(j*grid_height)) + bottomRight = (int((i+1)*grid_width), int((j+1)*grid_height)) + cv2.rectangle(grid_img, topLeft, bottomRight, (0, 0, 255), 2) + divided_grid.append((topLeft, bottomRight)) + + return grid_img, divided_grid + +def get_digits(warped, divided_grid, size_ocr=28, blur_type='gaussian', process='dilation'): + digits = [] + processed_dilation, processed_opening = preprocess_image(warped, blur_type) + + processed_img = processed_opening if process == 'opening' else processed_dilation + + for grid in divided_grid: + cell = processed_img[grid[0][1]:grid[1][1], grid[0][0]:grid[1][0]] + digits.append(extract_digit_from_cell(cell, size_ocr)) + + return digits + +def extract_digit_from_cell(cell, output_size): + height, width = cell.shape[:2] + margin = int(np.mean([height, width])/3.5) + bbox = find_largest_feature(cell.copy(), [margin, margin], [width-margin, height-margin]) + + digit = cell[int(bbox[0][1]):int(bbox[1][1]), int(bbox[0][0]):int(bbox[1][0])] + # scale and pad + w = bbox[1][0] - bbox[0][0] + h = bbox[1][1] - bbox[0][1] + + # Ignore any small bounding boxes + if w > 0 and h > 0 and (w * h) > 100 and len(digit) > 0: + return scale_and_centre(digit, output_size, 4) + else: + return np.zeros((output_size, output_size), np.uint8) + + return digit + +def find_largest_feature(cell, topLeft=None, bottomRight=None): + cell_copy = cell.copy() + # floodfill --> https://medium.com/@elvisdias/introduction-to-opencv-with-python-i-9fc72082f230 + height, width = cell_copy.shape[:2] + max_area = 0 + seed_point = (None, None) + + if topLeft == None: + topLeft = [0, 0] + + if bottomRight == None: + bottomRight = [width, height] + + for x in range(topLeft[0], bottomRight[0]): + for y in range(topLeft[1], bottomRight[1]): + # Only operate on light or white squares + if cell_copy[y, x] == 255 and x < width and y < height: + area = cv2.floodFill(cell_copy, None, (x, y), 64) # why 64 + if area[0] > max_area: # Gets the maximum bound area which should be the grid + max_area = area[0] + seed_point = (x, y) + + # Colour everything grey (compensates for features outside of our middle scanning range) + for x in range(width): + for y in range(height): + if x < width and y < height and cell_copy[y, x] == 255: + cv2.floodFill(cell_copy, None, (x, y), 64) + + mask = np.zeros((height + 2, width + 2), np.uint8) # Mask that is 2 pixels bigger than the image + + # Highlight the main feature + if all(seed_point): + cv2.floodFill(cell_copy, mask, seed_point, 255) + + top, bottom, left, right = height, 0, width, 0 + + for x in range(width): + for y in range(height): + if cell_copy[y, x] == 64: + cv2.floodFill(cell_copy, mask, (x, y), 0) + + # Find the bounding parameters + elif cell_copy[y, x] == 255: + top = y if y < top else top + bottom = y if y > bottom else bottom + left = x if x < left else left + right = x if x > right else right + + bbox = [[left, top], [right, bottom]] + + return np.array(bbox, dtype='float32') + +def scale_and_centre(img, size, margin=0, background=0): + h, w = img.shape[:2] + + def centre_pad(length): + """Handles centering for a given length that may be odd or even.""" + if length % 2 == 0: + side1 = int((size - length) / 2) + side2 = side1 + else: + side1 = int((size - length) / 2) + side2 = side1 + 1 + return side1, side2 + + def scale(r, x): + return int(r * x) + + if h > w: + t_pad = int(margin / 2) + b_pad = t_pad + ratio = (size - margin) / h + w, h = scale(ratio, w), scale(ratio, h) + l_pad, r_pad = centre_pad(w) + else: + l_pad = int(margin / 2) + r_pad = l_pad + ratio = (size - margin) / w + w, h = scale(ratio, w), scale(ratio, h) + t_pad, b_pad = centre_pad(h) + + img = cv2.resize(img, (w, h)) + img = cv2.copyMakeBorder(img, t_pad, b_pad, l_pad, r_pad, cv2.BORDER_CONSTANT, None, background) + return cv2.resize(img, (size, size)) + +# Driver Code +if __name__=="__main__": + orig_img = cv2.imread('imgs/sudoku9.png') + print("Image Shape: {}".format(orig_img.shape)) + + orig_img = cv2.resize(orig_img, (640, 480)) + # cv2.imshow('original', orig_img) + + # GAUSSIAN BLUR + start = time.time() + processed_dilation, processed_opening = preprocess_image(orig_img, 'gaussian') + # cv2.imshow('processed_dilation', processed_dilation) + # cv2.imshow('processed_opening', processed_opening) + + # Choose img to proceed with - dilation / opening + processed_img = processed_dilation + + all_contours_img, contours = drawAllContours(processed_img) + external_contours_img, ext_contours = drawExternalContours(processed_img) + + # Do contour Approx for box + + corners_list = find_corners_of_largest_contour(ext_contours, processed_img) + print("Corners: {}".format(corners_list.tolist())) + for corner in corners_list: + cv2.circle(external_contours_img, tuple(corner), 5, (0, 0, 255), -1) + # cv2.imshow('corners', external_contours_img) + + transformation_matrix, width, height = get_transform(corners_list) + warped_orig = cv2.warpPerspective(orig_img, transformation_matrix, (width, height)) + warped_opening = cv2.warpPerspective(processed_opening, transformation_matrix, (width, height)) + print("Warped Shape: {}".format(warped_orig.shape)) + # cv2.imshow('warped_orig', warped_orig) + # cv2.imshow('warped_opening', warped_opening) + + grid_img, divided_grid = infer_grid(warped_orig) + cv2.imshow('grid_img', grid_img) + + extracted_digits = get_digits(warped_orig, divided_grid, size_ocr=28, blur_type='gaussian', process='dilation') + + # kernel = np.ones((3, 3), np.uint8) + # x_test = [(cv2.erode(img.copy(), kernel, iterations=1)).tolist() for img in extracted_digits] + # print(all(x_test[0])) + # model = tf.keras.models.load_model('classify_digit.model') + # predictions = model.predict(x_test) + # classified_digit = [np.argmax(p) for p in predictions] + # print(classified_digit) + + # hmmm + classified_digit = [] + kernel = np.ones((3, 3), np.uint8) + model = tf.keras.models.load_model('classify_digit.model') + # count = 0 + for img in extracted_digits: + # test = np.array(img, np.uint8) + test = (cv2.erode(img.copy(), kernel, iterations=1)) + # test = (cv2.morphologyEx(img.copy(), cv2.MORPH_OPEN, kernel)) + # cv2.imshow('dig + {}'.format(count), test) + # count += 1 + tf.keras.utils.normalize(test, axis=1) + if not np.any(test): + classified_digit.append(-1) + continue + pred = model.predict([test.tolist()]) + classified_digit.append(np.argmax(pred[0])) + # print(classified_digit) + for row in range(9): + for col in range(9): + print(classified_digit[row*9+col], end=" ") + print() + + kernel = np.ones((3,3),np.uint8) + with_border = [cv2.copyMakeBorder(cv2.erode(img.copy(),kernel,iterations=1), 1, 1, 1, 1, cv2.BORDER_CONSTANT, None, (255,255,255)) for img in extracted_digits] + rows = [] + for i in range(9): + row = np.concatenate(with_border[i * 9:((i + 1) * 9)], axis=1) + rows.append(row) + extracted_digits = np.concatenate(rows) + cv2.imshow('extracted_digits', extracted_digits) + # print("Extracted_Digits Shape: {}".format(extracted_digits.shape)) + + # print("Extra: {}".format(extracted_digits)) + # x_train = tf.keras.utils.normalize(extracted_digits, axis=1) + # print("Extra: {}".format(x_train)) + + + # Predict.... + # model = tf.keras.models.load_model('classify_digit.model') + # predictions = model.predict(extracted_digits) + # classified_digit = [] + # for p in predictions: + # classified_digit.append(np.argmax(p)) + # print(classified_digit) + + # kernel = np.ones((3, 3), np.uint8) + # rows = [] + # for grid in divided_grid: + # cell = warped_opening[grid[0][1]:grid[1][1], grid[0][0]:grid[1][0]] + # height, width = cell.shape[:2] + # margin = int(np.mean([height, width])/3.5) + # # bbox = find_largest_feature(cell.copy(), [margin, margin], [width-margin, height-margin]) + # row = cv2.morphologyEx(cell[margin:(height-margin), margin:(width-margin)], cv2.MORPH_CLOSE, kernel) + # cv2.imshow('row', row) + # rows.append(row) + + # cv2.imshow('whatt', np.concatenate(rows)) + + ''' + # digits = [] + # warped_opening = cv2.warpPerspective(processed_opening, transformation_matrix, (width, height)) + # warped_opening = cv2.bitwise_not(warped_opening, warped_opening) + # for (tl, br) in divided_grid: + # digit = pytesseract.image_to_string(warped_opening[tl[1]:br[1], tl[0]:br[0]], lang='eng', config='--psm 10 --oem 3 -c tessedit_char_whitelist=0123456789') + # digits.append(digit) + # # (tl, br) = divided_grid[-2] + # # warped_opening = cv2.warpPerspective(processed_opening, transformation_matrix, (width, height)) + # # warped_opening = cv2.bitwise_not(warped_opening, warped_opening) + # # cv2.imshow('digit', warped_opening[tl[1]:br[1], tl[0]:br[0]]) + # # digit = pytesseract.image_to_string(warped_opening[tl[1]:br[1], tl[0]:br[0]], lang='eng', config='--psm 10 --oem 3 -c tessedit_char_whitelist=0123456789') + # # digits.append(digit) + # print(digits) + ''' + + print("Gaussian: {} seconds".format(time.time() - start)) + + + # # BILATERAL BLUR + # start = time.time() + # processed_bilateral_dilation, processed_bilateral_opening = preprocess_image(orig_img, 'bilateral') + # cv2.imshow('processed_bilateral_dilation', processed_bilateral_dilation) + # print("Bilateral: {} seconds".format(time.time() - start)) + + # # MEDIAN BLUR + # start = time.time() + # processed_median_dilation, processed_median_opening = preprocess_image(orig_img, 'median') + # cv2.imshow('processed_median_dilation', processed_median_dilation) + # print("Median: {} seconds".format(time.time() - start)) + + cv2.waitKey(0) + cv2.destroyAllWindows() \ No newline at end of file diff --git a/development/solver.py b/development/solver.py new file mode 100644 index 0000000..40aaf76 --- /dev/null +++ b/development/solver.py @@ -0,0 +1,132 @@ +# http://pi.math.cornell.edu/~mec/Summer2009/Mahmood/Symmetry.html +# https://www.geeksforgeeks.org/sudoku-backtracking-7/ + +# A Backtracking program in Python to solve Sudoku problem +import time + +N = 9 # Size of grid + +def print_sudoku(arr): + for i in range(N): + for j in range(N): + print(arr[i][j], end=" ") + print ('') + + +# Function to Find the entry in the Grid that is still not used +# 'l' is a list variable that has been passed from the solve_sudoku function +# to keep track of incrementation of Rows and Columns +def find_empty_location(arr, l): + for row in range(9): + for col in range(9): + if(arr[row][col]== 0): + l[0]= row + l[1]= col + return True + return False + +# Returns a boolean which indicates whether any assigned entry +# in the specified row matches the given number. +def used_in_row(arr, row, num): + for i in range(9): + if(arr[row][i] == num): + return True + return False + +# Returns a boolean which indicates whether any assigned entry +# in the specified column matches the given number. +def used_in_col(arr, col, num): + for i in range(9): + if(arr[i][col] == num): + return True + return False + +# Returns a boolean which indicates whether any assigned entry +# within the specified 3x3 box matches the given number +def used_in_box(arr, row, col, num): + for i in range(3): + for j in range(3): + if(arr[i + row][j + col] == num): + return True + return False + +# Checks whether it will be legal to assign num to the given row, col +# Returns a boolean which indicates whether it will be legal to assign +# num to the given row, col location. +def check_location_is_safe(arr, row, col, num): + + # Check if 'num' is not already placed in current row, + # current column and current 3x3 box + return not used_in_row(arr, row, num) and not used_in_col(arr, col, num) and not used_in_box(arr, row - row % 3, col - col % 3, num) + +# Takes a partially filled-in grid and attempts to assign values to +# all unassigned locations in such a way to meet the requirements +# for Sudoku solution (non-duplication across rows, columns, and boxes) +def solve_sudoku(arr): + + # 'l' is a list variable that keeps the record of row and col in find_empty_location Function + l =[0, 0] + + # If there is no unassigned location, we are done + if(not find_empty_location(arr, l)): + return True + + # Assigning list values to row and col that we got from the above Function + row = l[0] + col = l[1] + + # consider digits 1 to 9 + for num in range(1, 10): + + # if looks promising + if(check_location_is_safe(arr, row, col, num)): + + # make tentative assignment + arr[row][col]= num + + # return, if success, ya ! + if(solve_sudoku(arr)): + return True + + # failure, unmake & try again + arr[row][col] = 0 + + # this triggers backtracking + return False + +# Driver main function +if __name__=="__main__": + + # creating a 2D array for the grid + grid =[[0 for x in range(9)]for y in range(9)] + + # assigning values to the grid + grid =[[8, 0, 0, 0, 0, 0, 0, 0, 0], + [0, 0, 3, 6, 0, 0, 0, 0, 0], + [0, 7, 0, 0, 9, 0, 2, 0, 0], + [0, 5, 0, 0, 0, 7, 0, 0, 0], + [0, 0, 0, 0, 4, 5, 7, 0, 0], + [0, 0, 0, 1, 0, 0, 0, 3, 0], + [0, 0, 1, 0, 0, 0, 0, 6, 8], + [0, 0, 8, 5, 0, 0, 0, 1, 0], + [0, 9, 0, 0, 0, 0, 4, 0, 0]] + + # DAUNTING PUZZLE + # grid =[[0, 0, 0, 0, 0, 6, 0, 0, 0], + # [0, 0, 5, 9, 0, 0, 0, 0, 8], + # [2, 0, 0, 0, 0, 8, 0, 0, 0], + # [0, 4, 5, 0, 0, 0, 0, 0, 0], + # [0, 0, 3, 0, 0, 0, 0, 0, 0], + # [0, 0, 6, 0, 0, 3, 0, 5, 4], + # [0, 0, 0, 3, 2, 5, 0, 0, 6], + # [0, 0, 0, 0, 0, 0, 0, 0, 0], + # [0, 0, 0, 0, 0, 0, 0, 0, 0]] + + start_time = time.time() + # if success print the grid + if(solve_sudoku(grid)): + print_sudoku(grid) + else: + print("No solution exists") + + print("Solved in: {}seconds".format(time.time() - start_time)) diff --git a/digit_classifier/recognize_digit.ipynb b/digit_classifier/recognize_digit.ipynb new file mode 100644 index 0000000..c9a4880 --- /dev/null +++ b/digit_classifier/recognize_digit.ipynb @@ -0,0 +1,261 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.2.0\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import tensorflow as tf\n", + "from tensorflow.keras.callbacks import LearningRateScheduler\n", + "\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "print(tf.__version__)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(60000, 28, 28)\n", + "(10000, 28, 28)\n" + ] + } + ], + "source": [ + "# load data\n", + "mnist = tf.keras.datasets.mnist\n", + "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n", + "print(x_train.shape)\n", + "print(x_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# normalize data\n", + "x_train = tf.keras.utils.normalize(x_train, axis=1)\n", + "x_test = tf.keras.utils.normalize(x_test, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [], + "source": [ + "# Callback for stop training after threshold accuracy\n", + "ACCURACY_THRESHOLD = 0.996\n", + "class CustomCallback(tf.keras.callbacks.Callback):\n", + " def on_epoch_end(self, epoch, logs={}):\n", + " if(logs.get('accuracy') > ACCURACY_THRESHOLD and logs.get('loss') < 0.003):\n", + " print(\"\\nReached %2.2f%% accuracy, stopping training!\"%(ACCURACY_THRESHOLD*100))\n", + " self.model.stop_training = True\n", + " \n", + "callbacks = CustomCallback()\n", + "\n", + "es = tf.keras.callbacks.EarlyStopping(monitor='val_loss', mode='auto', patience=10, verbose=1, restore_best_weights=True)\n", + "\n", + "# Set a learning rate annealer\n", + "annealer = LearningRateScheduler(lambda x: 1e-3 * 0.9 ** x)" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/6\n", + "1875/1875 [==============================] - 17s 9ms/step - loss: 0.2079 - accuracy: 0.9360 - val_loss: 0.1044 - val_accuracy: 0.9672\n", + "Epoch 2/6\n", + "1875/1875 [==============================] - 21s 11ms/step - loss: 0.0881 - accuracy: 0.9718 - val_loss: 0.0845 - val_accuracy: 0.9755\n", + "Epoch 3/6\n", + "1875/1875 [==============================] - 21s 11ms/step - loss: 0.0599 - accuracy: 0.9815 - val_loss: 0.0852 - val_accuracy: 0.9751\n", + "Epoch 4/6\n", + "1875/1875 [==============================] - 26s 14ms/step - loss: 0.0452 - accuracy: 0.9856 - val_loss: 0.0945 - val_accuracy: 0.9750\n", + "Epoch 5/6\n", + "1875/1875 [==============================] - 24s 13ms/step - loss: 0.0342 - accuracy: 0.9892 - val_loss: 0.0776 - val_accuracy: 0.9798\n", + "Epoch 6/6\n", + "1875/1875 [==============================] - 25s 13ms/step - loss: 0.0288 - accuracy: 0.9907 - val_loss: 0.0864 - val_accuracy: 0.9780\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# model\n", + "model = tf.keras.models.Sequential()\n", + "model.add(tf.keras.layers.Flatten())\n", + "model.add(tf.keras.layers.Dense(512, activation=tf.nn.relu))\n", + "model.add(tf.keras.layers.Dense(256, activation=tf.nn.relu))\n", + "model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))\n", + "model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax))\n", + "\n", + "model.compile(optimizer='adam',\n", + " loss='sparse_categorical_crossentropy',\n", + " metrics=['accuracy'])\n", + "\n", + "model.fit(x_train, y_train, epochs=6, validation_data=(x_test, y_test))" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "313/313 [==============================] - 1s 4ms/step - loss: 0.0864 - accuracy: 0.9780\n", + "0.08640706539154053\n", + "0.9779999852180481\n" + ] + } + ], + "source": [ + "val_loss, val_acc = model.evaluate(x_test, y_test)\n", + "print(val_loss)\n", + "print(val_acc)" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Assets written to: models/digit_classifier.model/assets\n" + ] + } + ], + "source": [ + "# Save the model\n", + "model.save('models/digit_classifier.model')" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [], + "source": [ + "new_model = tf.keras.models.load_model('models/digit_classifier.model')" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Make predictions\n", + "predictions = new_model.predict(x_test)\n", + "\n", + "test_example = 500\n", + "print(np.argmax(predictions[test_example]))\n", + "plt.imshow(x_test[test_example], cmap=plt.cm.binary)\n", + "plt.show" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".cv", + "language": "python", + "name": ".cv" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/run.py b/run.py new file mode 100644 index 0000000..4994c23 --- /dev/null +++ b/run.py @@ -0,0 +1,126 @@ +import cv2 +import numpy as np +import tensorflow as tf +import argparse +import time +import pprint +from tabulate import tabulate +from copy import deepcopy + +from utils.img_processing import Extract_Digits +from utils.classify_digit import Classify_Digit +from utils.solve_sudoku import Solve_Sudoku + +# =======IGNORE WARNINGS============================ +import warnings +warnings.filterwarnings('ignore') + +# Just disables the warning, doesn't enable AVX/FMA +import os +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' +# ================================================== + +def arg_parse(): + ''' Parse command line arguments ''' + parser = argparse.ArgumentParser(description='Sudoku Solver Module') + parser.add_argument("--image", dest = "image", help = "path to image", default = "test_imgs/sudoku9.png", type = str) + parser.add_argument("--model", dest = "model", help = "path to model", default = "models/loss_0298.model", type = str) + parser.add_argument("--process", dest = "process", help = "Dilation or Opening for grid extraction", default = "dilation") + parser.add_argument("--resize", dest = "resize", help = "(width, height) to resize to", default = (450, 450), type = tuple) + parser.add_argument("--size", dest = "size", help = "input size to ocr model", default = 28, type = int) + parser.add_argument("--margin", dest = "margin", help = "margin for extracted digits", default = 10, type = int) + parser.add_argument("--grid_size", dest = "sudoku_size", help = "grid size of sudoku default is 9", default = 9, type = int) + parser.add_argument("--show", dest = "show", help = "bool whether to show journey of image", default = False, type = bool) + + return parser.parse_args() + + +# Driver Code +if __name__ == "__main__": + + # Initialize command line arguments + args = arg_parse() + orig_img = cv2.imread(args.image) + model_path = args.model + process = args.process + resize = args.resize + size_ocr = args.size + margin = args.margin + sudoku_size = args.sudoku_size + show_journey = args.show + + # Start + start_time = time.time() + + img_obj = Extract_Digits(resize, size_ocr, show_journey) + + # Preprocess :--: resize --> gray --> blur --> threshold --> negate --> dilation/opening + processed_img = img_obj.preprocess_image(orig_img, process=process) + preprocessing_time = time.time() + + # External contours :--: assumption that biggest contour correspond to sudoku puzzle + ext_contours = img_obj.draw_external_contours(processed_img) + draw_contour_time = time.time() + + # Infer corners from points in largest contour + corners = img_obj.find_corners_of_largest_contour(ext_contours, img_obj.resized_img) + find_corner_time = time.time() + + # Perspective transformation based on corners + transformation_matrix, width, height = img_obj.get_transform(corners) + transformation_time = time.time() + + # Divide warped image into 9*9 cells :--: Need some other logic for this step + divided_grid = img_obj.infer_grid(img_obj.warped_resized) + grid_inference_time = time.time() + + # Apply floodfill in mid portion of cell to extract only digit + extracted_digits = img_obj.get_digits(img_obj.warped_processed, divided_grid, margin=margin) + digit_extraction_time = time.time() + + classifier_obj = Classify_Digit(model_path, show_journey=show_journey) + + # Classify digit present in cell + classifier_obj.classify(extracted_digits) + pprint.pprint(classifier_obj.classified_digits) + digit_classification_time = time.time() + + solver_obj = Solve_Sudoku(sudoku_size) + + # Solve sudoku using backtracking + solved_sudoku = solver_obj.solve_sudoku(deepcopy(classifier_obj.classified_digits)) + solving_time = time.time() + + if(solved_sudoku): + solver_obj.print_sudoku(solved_sudoku) + + # Show solution on original image + final_img = img_obj.draw_with_solution(orig_img.copy(), solved_sudoku, classifier_obj.classified_digits, divided_grid, transformation_matrix) + cv2.imshow('original', orig_img) + cv2.imshow('solved', final_img) + + else: + print("No Solution exists!!!") + + end_time = time.time() + + cv2.waitKey(0) + cv2.destroyAllWindows() + + # when show_journey is False, then only its true inference time + # because of cv2.waitKey + if not show_journey: + print("\nSUMMARY:") + table = [ + ["Preprocessing", preprocessing_time - start_time], + ["External Contours", draw_contour_time - preprocessing_time], + ["Finding Corners from Contour", find_corner_time - draw_contour_time], + ["Perspecive Transformation", transformation_time - find_corner_time], + ["Grid Inference", grid_inference_time - transformation_time], + ["Digit Extraction", digit_extraction_time - grid_inference_time], + ["Digit Classification", digit_classification_time - digit_extraction_time], + ["Solve Sudoku", solving_time - digit_classification_time], + ["Drawing on Image (if solved)", end_time - solving_time], + ["Inference Time", end_time - 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zLVU%AAFcFcg})=o3SUT&lh5r-<)D($dU=I^wesKV{<ll15g++tX>Ler(v?ParB%@u zk`EBV?msHcKO!PCPk;Pso)Al`;fm?KPw`&V<>hsrt}ZSv&lM#4&5W~)vopoX>Dlqg z>CwUQ;r`J+!3^!~Z0~Mu?QCwsGlOJ?nRs4Zhc^FETdV8;e+q0t4z@-jbpQYW07*qo IM6N<$f`1^00ssI2 literal 0 HcmV?d00001 diff --git a/utils/classify_digit.py b/utils/classify_digit.py new file mode 100644 index 0000000..6eab385 --- /dev/null +++ b/utils/classify_digit.py @@ -0,0 +1,59 @@ +import cv2 +import numpy as np +import tensorflow as tf + +class Classify_Digit: + def __init__(self, model_path, kernel_size=3, show_journey=False): + self.model = tf.keras.models.load_model(model_path) + self.kernel = np.ones((kernel_size,kernel_size), np.uint8) + self.show_journey = show_journey + self.classified_digits = None + + def classify(self, extracted_digits): + # processed_digits = [(tf.keras.utils.normalize(cv2.erode(img.copy(), self.kernel, iterations=1), axis=1)).tolist() for img in extracted_digits] + + # # Doesn't handle all black case + # # predictions_distribution = self.model.predict(processed_digits) + # # predicted_digits = [np.argmax(np.array(digit)) for digit in predictions_distribution] + # # print(predicted_digits) + + # self.classified_digits = [0 if not np.any(np.array(digit)) else np.argmax(self.model.predict([digit])) for digit in processed_digits] + # self.classified_digits = np.reshape(np.array(self.classified_digits), (9, 9)) + + classified_digit = [[0 for j in range(9)] for i in range(9)] + i, j = 0, 0 + + if self.show_journey: + rows, row = [], [] + + for img in extracted_digits: + test = (cv2.erode(img.copy(), self.kernel, iterations=1)) + + if self.show_journey: + row.append(cv2.copyMakeBorder(test.copy(), 1, 1, 1, 1, cv2.BORDER_CONSTANT, None, (255,255,255))) + + tf.keras.utils.normalize(test, axis=1) + if not np.any(test): + j = (j+1)%9 + if j == 0: + i += 1 + if self.show_journey: + rows.append(np.concatenate(row, axis=1)) + row = [] + continue + pred = self.model.predict([test.tolist()]) + classified_digit[i][j] = np.argmax(pred[0]) + j = (j+1) % 9 + if j == 0: + i += 1 + if self.show_journey: + rows.append(np.concatenate(row, axis=1)) + row = [] + + self.classified_digits = classified_digit + + if self.show_journey: + cv2.imshow('extracted_digits', np.concatenate(rows)) + + cv2.waitKey(0) + cv2.destroyAllWindows() \ No newline at end of file diff --git a/utils/img_processing.py b/utils/img_processing.py new file mode 100644 index 0000000..1f01968 --- /dev/null +++ b/utils/img_processing.py @@ -0,0 +1,304 @@ +import cv2 +import numpy as np +from scipy.spatial import distance as dist + +class Extract_Digits: + def __init__(self, resize=(450, 450), size_ocr=28, show_journey=False, kernel_size=3): + assert(kernel_size%2 != 0) + self.kernel = np.ones((kernel_size, kernel_size), np.uint8) + self.resize = resize + self.size_ocr = size_ocr + self.show_journey = show_journey + self.resized_img = None + self.processed_img = None + self.warped_processed = None + self.warped_resized = None + + def preprocess_image(self, image, process='dilation'): + ''' return processed image ''' + self.resized_img = cv2.resize(image, self.resize) + + gray = cv2.cvtColor(self.resized_img, cv2.COLOR_BGR2GRAY) + blur = cv2.GaussianBlur(gray, (11, 11), 0) + threshold = cv2.adaptiveThreshold(blur, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, \ + cv2.THRESH_BINARY, 11, 2) + + negate = cv2.bitwise_not(threshold) + if process == 'dilation': + self.processed_img = cv2.dilate(negate, self.kernel, iterations = 1) + elif process == 'opening': + self.processed_img = cv2.morphologyEx(negate, cv2.MORPH_OPEN, self.kernel) + + if self.show_journey: + cv2.imshow('resized_image', self.resized_img) + cv2.imshow('gray', gray) + cv2.imshow('blur', blur) + cv2.imshow('threshold',threshold) + cv2.imshow('negate', negate) + cv2.imshow('processed_img', self.processed_img) + + cv2.waitKey(0) + cv2.destroyAllWindows() + + return self.processed_img + + def draw_external_contours(self, image): + ext_contours, _ = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + + if self.show_journey: + # Convert to BGR for drawing contours + processed = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR) + external_contours_img = cv2.drawContours(processed, ext_contours, -1, (0,255,0), 2) + + cv2.imshow('external_contours', external_contours_img) + + cv2.waitKey(0) + cv2.destroyAllWindows() + + return ext_contours + + def find_corners_of_largest_contour(self, contours, image): + largest_contour = max(contours, key=cv2.contourArea) + # print("Contour Shape: {}".format(largest_contour.shape)) # N, 1, 2 + largest_contour = largest_contour.reshape((largest_contour.shape[0], 2)) # N x 2 + + # Order --> topLeft, topRight, bottomRight, bottomLeft + corners = self.order_points_old(largest_contour) + # print("Corners: {}".format(corners.tolist())) + + if self.show_journey: + copy = image.copy() + for corner in corners: + cv2.circle(copy, tuple(corner), 5, (0, 0, 255), -1) + + cv2.imshow('corners', copy) + + cv2.waitKey(0) + cv2.destroyAllWindows() + + return corners + + def order_points_old(self, pts): + ''' + from list of points return ordered corners (tl, tr, br, bl) + Doesn't work when sum/difference is same for corners + ''' + # the top-left point will have the smallest sum, whereas + # the bottom-right point will have the largest sum + s = pts.sum(axis=1) + tl = pts[np.argmin(s)] + br = pts[np.argmax(s)] + + # now, compute the difference between the points (y-x), the + # top-right point will have the smallest difference, + # whereas the bottom-left will have the largest difference + diff = np.diff(pts, axis=1) + tr = pts[np.argmin(diff)] + bl = pts[np.argmax(diff)] + + # return the ordered coordinates + return np.array([tl, tr, br, bl], dtype="float32") + + def get_transform(self, pts): + (tl, tr, br, bl) = pts + + # compute the width of the new image, which will be the + # maximum distance between bottom-right and bottom-left + # x-coordiates or the top-right and top-left x-coordinates + widthA = dist.euclidean(br, bl) + widthB = dist.euclidean(tr, tl) + maxWidth = max(int(widthA), int(widthB)) + + # compute the height of the new image, which will be the + # maximum distance between the top-right and bottom-right + # or the top-left and bottom-left + heightA = dist.euclidean(tr, br) + heightB = dist.euclidean(bl, tl) + maxHeight = max(int(heightA), int(heightB)) + + # now that we have the dimensions of the new image, construct + # the set of destination points to obtain a "birds eye view", + # (i.e. top-down view) of the image, again specifying points + # in the top-left, top-right, bottom-right, and bottom-left + dst = np.array([ + [0, 0], + [maxWidth - 1, 0], + [maxWidth - 1, maxHeight - 1], + [0, maxHeight - 1]], dtype = "float32") + # compute the perspective transform matrix and then apply it + M = cv2.getPerspectiveTransform(pts, dst) + + self.warped_processed = cv2.warpPerspective(self.processed_img, M, (maxWidth, maxHeight)) + self.warped_resized = cv2.warpPerspective(self.resized_img, M, (maxWidth, maxHeight)) + + if self.show_journey: + cv2.imshow('warped_processed', self.warped_processed) + cv2.imshow('warped_resized', self.warped_resized) + + cv2.waitKey(0) + cv2.destroyAllWindows() + + return M, maxWidth, maxHeight + + def infer_grid(self, image): + divided_grid = [] + grid_img = image.copy() + height, width = grid_img.shape[:-1] + grid_width, grid_height = width / 9, height / 9 + for j in range(9): + for i in range(9): + topLeft = (int(i*grid_width), int(j*grid_height)) + bottomRight = (int((i+1)*grid_width), int((j+1)*grid_height)) + cv2.rectangle(grid_img, topLeft, bottomRight, (0, 0, 255), 2) + divided_grid.append((topLeft, bottomRight)) + + if self.show_journey: + cv2.imshow('grid', grid_img) + + cv2.waitKey(0) + cv2.destroyAllWindows() + + return divided_grid + + def get_digits(self, warped, divided_grid, margin=10): + digits = [] + + for grid in divided_grid: + cell = warped[grid[0][1]:grid[1][1], grid[0][0]:grid[1][0]] + digits.append(self.extract_digit_from_cell(cell, self.size_ocr, margin)) + + return digits + + def extract_digit_from_cell(self, cell, output_size, margin): + height, width = cell.shape[:2] + margin = int(np.mean([height, width])/4.5) + bbox = self.find_largest_feature(cell, [margin, margin], [width-margin, height-margin]) + + digit = cell[int(bbox[0][1]):int(bbox[1][1]), int(bbox[0][0]):int(bbox[1][0])] + # scale and pad + w = bbox[1][0] - bbox[0][0] + h = bbox[1][1] - bbox[0][1] + + # Ignore any small bounding boxes + if w > 0 and h > 0 and (w * h) > 100 and len(digit) > 0: + return self.scale_and_centre(digit, output_size, margin) + else: + return np.zeros((output_size, output_size), np.uint8) + + return digit + + def find_largest_feature(self, cell, topLeft=None, bottomRight=None): + cell_copy = cell.copy() + + height, width = cell_copy.shape[:2] + max_area = 0 + seed_point = (None, None) + + if topLeft == None: + topLeft = [0, 0] + + if bottomRight == None: + bottomRight = [width, height] + + for x in range(topLeft[0], bottomRight[0]): + for y in range(topLeft[1], bottomRight[1]): + # Get largest white contour while changing all white pixels to gray + if cell_copy[y, x] == 255 and x < width and y < height: + area = cv2.floodFill(cell_copy, None, (x, y), 64) + if area[0] > max_area: # Gets the maximum bound area which should be the grid + max_area = area[0] + seed_point = (x, y) + + # Colour everything grey (compensates for features outside of our middle scanning range) + for x in range(width): + for y in range(height): + if x < width and y < height and cell_copy[y, x] == 255: + cv2.floodFill(cell_copy, None, (x, y), 64) + + mask = np.zeros((height + 2, width + 2), np.uint8) # Mask that is 2 pixels bigger than the image + + # Draw the main feature + if all(seed_point): + cv2.floodFill(cell_copy, mask, seed_point, 255) + + top, bottom, left, right = height, 0, width, 0 + + for x in range(width): + for y in range(height): + if cell_copy[y, x] == 64: + cv2.floodFill(cell_copy, mask, (x, y), 0) + + # Find the bounding parameters + elif cell_copy[y, x] == 255: + top = y if y < top else top + bottom = y if y > bottom else bottom + left = x if x < left else left + right = x if x > right else right + + bbox = [[left, top], [right, bottom]] + + return np.array(bbox, dtype='float32') + + def scale_and_centre(self, image, size, margin=10, background=0): + h, w = image.shape[:2] + + def centre_pad(length): + ''' Handles centering for a given length that may be odd or even ''' + if length % 2 == 0: + side1 = int((size - length) / 2) + side2 = side1 + else: + side1 = int((size - length) / 2) + side2 = side1 + 1 + return side1, side2 + + def scale(r, x): + return int(r * x) + + if h > w: + t_pad = int(margin / 2) + b_pad = t_pad + ratio = (size - margin) / h + w, h = scale(ratio, w), scale(ratio, h) + l_pad, r_pad = centre_pad(w) + else: + l_pad = int(margin / 2) + r_pad = l_pad + ratio = (size - margin) / w + w, h = scale(ratio, w), scale(ratio, h) + t_pad, b_pad = centre_pad(h) + + image = cv2.resize(image, (w, h)) + image = cv2.copyMakeBorder(image, t_pad, b_pad, l_pad, r_pad, cv2.BORDER_CONSTANT, None, background) + + return cv2.resize(image, (size, size)) + + + def draw_with_solution(self, orig_img, solved_sudoku, unsolved_sudoku, divided_grid, transformation_matrix): + digit_img = np.zeros((self.warped_resized.shape[1], self.warped_resized.shape[0], 3), np.uint8) + font = cv2.FONT_HERSHEY_SIMPLEX + color = (0, 255, 0) + font_scale = 1 + thickness = 2 + + offset_x = (divided_grid[0][1][0] - divided_grid[0][0][0])//3 + offset_y = (divided_grid[0][1][1] - divided_grid[0][0][1])//3 + + for i in range(len(solved_sudoku)): + for j in range(len(solved_sudoku)): + if unsolved_sudoku[i][j] == 0 and solved_sudoku[i][j] != 0: + label = str(solved_sudoku[i][j]) + digit_img = cv2.putText(digit_img, label, (divided_grid[i*9+j][0][0]+offset_x, divided_grid[i*9+j][1][1]-offset_y), font, font_scale, color, thickness, cv2.LINE_AA) + + final_img = np.zeros((self.resize[1], self.resize[0]), np.uint8) + final_img = cv2.warpPerspective(digit_img, transformation_matrix, (self.resize[1], self.resize[0]), final_img, cv2.WARP_INVERSE_MAP) + final_img = cv2.add(final_img, self.resized_img) + final_img = cv2.resize(final_img, (orig_img.shape[1], orig_img.shape[0])) + + if self.show_journey: + cv2.imshow('digit_image', digit_img) + + cv2.waitKey(0) + cv2.destroyAllWindows() + + return final_img \ No newline at end of file diff --git a/utils/solve_sudoku.py b/utils/solve_sudoku.py new file mode 100644 index 0000000..b66d365 --- /dev/null +++ b/utils/solve_sudoku.py @@ -0,0 +1,77 @@ +''' Solves sudoku using backtracking ''' +class Solve_Sudoku: + def __init__(self, sudoku_size=9): + assert((sudoku_size**0.5) - int(sudoku_size**0.5) == 0) + self.N = sudoku_size + self.box_size = int(self.N**0.5) + + def print_sudoku(self, sudoku): + print("\nSolved!!!") + for i in range(self.N): + for j in range(self.N): + print(sudoku[i][j], end=" ") + print ('') + + def find_empty_location(self, sudoku, pos): + for row in range(self.N): + for col in range(self.N): + if(sudoku[row][col] == 0): + pos[0], pos[1] = row, col + return True + return False + + def used_in_row(self, sudoku, row, digit): + for i in range(self.N): + if(sudoku[row][i] == digit): + return True + return False + + def used_in_col(self, sudoku, col, digit): + for i in range(self.N): + if(sudoku[i][col] == digit): + return True + return False + + def used_in_box(self, sudoku, row, col, digit): + for i in range(self.box_size): + for j in range(self.box_size): + if(sudoku[i + row][j + col] == digit): + return True + return False + + + def check_location_is_safe(self, sudoku, row, col, digit): + # Check if 'digit' is not already placed in current row, + # current column and current 3x3 box + return not self.used_in_row(sudoku, row, digit) and not self.used_in_col(sudoku, col, digit) and not self.used_in_box(sudoku, row - row % 3, col - col % 3, digit) + + def solve_sudoku(self, sudoku): + # 'pos' keeps the record of location of empty cell + pos =[0, 0] + + # If there is no unassigned location, we are done + if(not self.find_empty_location(sudoku, pos)): + return sudoku + + # Assigning list values to row and col that we got from the above Function + row = pos[0] + col = pos[1] + + # consider digits 1 to self.N + for digit in range(1, self.N + 1): + + # if looks promising + if(self.check_location_is_safe(sudoku, row, col, digit)): + + # make tentative assignment + sudoku[row][col]= digit + + # return, if success, ya ! + if(self.solve_sudoku(sudoku)): + return sudoku + + # failure, unmake & try again + sudoku[row][col] = 0 + + # this triggers backtracking + return False \ No newline at end of file