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root_seg.py
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import streamlit as st
from streamlit_cropper import st_cropper
import requests
import cv2
import wget
import detectron2
from detectron2.utils.logger import setup_logger
setup_logger()
import PIL
import numpy as np
from detectron2.utils.visualizer import ColorMode
import os
from detectron2 import model_zoo
from detectron2.engine import DefaultPredictor
from detectron2.data.datasets import register_coco_instances
from detectron2.config import get_cfg
from detectron2.utils.visualizer import Visualizer
from detectron2.data import MetadataCatalog,DatasetCatalog
import numpy as np
from mongoengine import Document
from mongoengine import FileField
from mongoengine import *
import secrets
connect(host="mongodb+srv://adithya_admin:[email protected]/dental_diagnosis?retryWrites=true&w=majority")
class RootDetector(Document):
# by default collection_name is 'fs'
# you can use collection_name parameter to set the name of the collection
# use collection_name like this: FileField(collection_name='images')
_id = StringField(required=True)
description = StringField(required=True)
image_root = FileField()
def write():
st.set_option('deprecation.showfileUploaderEncoding', False)
st.title('Root Anatomy Prediction by A.Adithya Sherwood IX-E')
st.subheader('Disclaimer: Please check with your local specialized dentist, if you are in doubt please try atleast twice.')
confidence = st.slider(
'Please Select a Confidence Value',
0.1, 1.0, 0.05
)
uploaded_file_img = st.file_uploader("Choose an Input Image", type="png",accept_multiple_files=False)
crop_image = st.checkbox('Crop?')
if uploaded_file_img is not None:
file_random = secrets.token_hex(4)
o = int(np.random.randint(low=10301319,high=9987869996))
cfg = get_cfg()
cfg.MODEL.DEVICE='cpu'
cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x.yaml"))
register_coco_instances(f"tooth_segmentation_maskrcnn{o}",{},str(f"./annotations/instances_default.json"),str(f"./images"))
cfg.DATASETS.TRAIN = (f"tooth_segmentation_maskrcnn{o}",)
cfg.DATASETS.TEST = ()
cfg.DATASETS.NUM_WORKERS = 2
cfg.SOLVER.IMS_PER_BATCH = 2
cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 100 # faster, and good enough for this toy dataset (default: 512)
cfg.SOLVER.BASE_LR = 0.00025
cfg.SOLVER.MAX_ITER = 800
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 3
cfg.MODEL.WEIGHTS = './MASK_RCNN_ROOT_SEGMENTATION.pth' # path to the model we just trained
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = confidence # set a custom testing threshold
predictor = DefaultPredictor(cfg)
MetadataCatalog.get(f"tooth_segmentation_maskrcnn{o}").thing_classes = ["CShaped", "Normal"]
img_pil = PIL.Image.open(uploaded_file_img)
img_pil = img_pil.resize((512,512))
img_pil.save(f'input_mask{file_random}.png')
image_root_seg = open(f'input_mask{file_random}.png','rb')
root_form = RootDetector(_id = secrets.token_hex(4),description='Uploaded Root Detector Image',image_root=image_root_seg)
root_form.save()
if crop_image:
st.image(img_pil, caption='Uploaded Image.', use_column_width=True)
st.write("")
cropped_img = st_cropper(img_pil, realtime_update=True, box_color="#0000ff",
aspect_ratio=(1,1))
cropped_img.save(f'input_mask_cropped{file_random}.png')
st.write("")
st.image(cropped_img,caption='Cropped Image.', use_column_width=True)
image = cv2.imread(f'input_mask_cropped{file_random}.png')
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
#image = cv2.copyMakeBorder(image, 150, 150, 150, 150, cv2.BORDER_CONSTANT,value=[255,255,255])
output = predictor(image)
v = Visualizer(image[:, :, ::-1],
metadata=MetadataCatalog.get(f"tooth_segmentation_maskrcnn{o}"),
scale=2,
# remove the colors of unsegmented pixels. This option is only available for segmentation models
)
out = v.draw_instance_predictions(output["instances"].to("cpu"))
cv2.imwrite(f'output_MASK{file_random}.png',out.get_image()[:, :, ::-1])
out_i = PIL.Image.open(f'output_MASK{file_random}.png')
st.image(out_i, caption='Predicted Masks', use_column_width=True)
image_root_seg_out = open(f'output_MASK{file_random}.png','rb')
root_form_out = RootDetector(_id = secrets.token_hex(4),description='Predicted Root Detector Image',image_root=image_root_seg_out)
root_form_out.save()
else:
st.image(img_pil, caption='Uploaded Image.', use_column_width=True)
st.write("")
cropped_img = st_cropper(img_pil, realtime_update=True, box_color="#0000ff",
aspect_ratio=None)
cropped_img.save(f'input_mask_cropped{file_random}.png')
st.write("")
st.image(cropped_img,caption='Cropped Image.', use_column_width=True)
image = cv2.imread(f'input_mask_cropped{file_random}.png')
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
#image = cv2.copyMakeBorder(image, 150, 150, 150, 150, cv2.BORDER_CONSTANT,value=[255,255,255])
output = predictor(image)
v = Visualizer(image[:, :, ::-1],
metadata=MetadataCatalog.get(f"tooth_segmentation_maskrcnn{o}"),
scale=2,
# remove the colors of unsegmented pixels. This option is only available for segmentation models
)
out = v.draw_instance_predictions(output["instances"].to("cpu"))
cv2.imwrite(f'output_MASK{file_random}.png',out.get_image()[:, :, ::-1])
out_i = PIL.Image.open(f'output_MASK{file_random}.png')
st.image(out_i, caption='Predicted Masks', use_column_width=True)
image_root_seg_out = open(f'output_MASK{file_random}.png','rb')
root_form_out = RootDetector(_id = secrets.token_hex(4),description='Predicted Root Detector Image',image_root=image_root_seg_out)
root_form_out.save()
"""def write():
st.set_option('deprecation.showfileUploaderEncoding', False)
st.title('Root Anatomy Prediction by A.Adithya Sherwood IX-E')
st.subheader('Disclaimer: Please check with your local specialized dentist, if you are in doubt please try atleast twice.')
confidence = st.slider(
'Please Select a Confidence Value',
0.1, 1.0, 0.05
)
option = st.selectbox(
'Please Select A Model',
('Mask RCNN v1', 'Mask RCNN v2'))
add_border = st.checkbox('Add Border? Sometimes this helps in better performance',value=False)
uploaded_file_img = st.file_uploader("Choose an Input Image", type="png",accept_multiple_files=False)
if uploaded_file_img is not None:
o = int(np.random.randint(low=10301319,high=9987869996))
cfg = get_cfg()
cfg.MODEL.DEVICE='cpu'
cfg.merge_from_file(model_zoo.get_config_file('COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml'))
register_coco_instances(f"tooth_segmentation_maskrcnn{o}",{},str(f"./annotations/instances_default.json"),str(f"./images"))
cfg.DATASETS.TRAIN = (f"tooth_segmentation_maskrcnn{o}",)
cfg.DATASETS.TEST = ()
cfg.DATASETS.NUM_WORKERS = 2
cfg.SOLVER.IMS_PER_BATCH = 2
cfg.SOLVER.BASE_LR = 0.00025
cfg.SOLVER.MAX_ITER = 350
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 2
cfg.MODEL.WEIGHTS = './old mask rcnn.pth' # path to the model we just trained
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = confidence # set a custom testing threshold
predictor = DefaultPredictor(cfg)
MetadataCatalog.get(f"tooth_segmentation_maskrcnn{o}").thing_classes = ["CShaped", "Normal"]
if add_border == True and option == 'Mask RCNN v1':
cfg.MODEL.WEIGHTS = './old mask rcnn.pth'
img_pil = PIL.Image.open(uploaded_file_img)
img_pil = img_pil.resize((512,512))
img_pil.save('input.png')
st.image(img_pil, caption='Uploaded Image.', use_column_width=True)
st.write("")
image = cv2.imread('input.png')
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.copyMakeBorder(image, 150, 150, 150, 150, cv2.BORDER_CONSTANT,value=[255,255,255])
output = predictor(image)
v = Visualizer(image[:, :, ::-1],
metadata=MetadataCatalog.get(f"tooth_segmentation_maskrcnn{o}"),
scale=2,
# remove the colors of unsegmented pixels. This option is only available for segmentation models
)
out = v.draw_instance_predictions(output["instances"].to("cpu"))
cv2.imwrite('output.png',out.get_image()[:, :, ::-1])
out_i = PIL.Image.open('output.png')
st.image(out_i, caption='Predicted Masks', use_column_width=True)
elif add_border == False and option == 'Mask RCNN v1':
cfg.MODEL.WEIGHTS = './old mask rcnn.pth'
img_pil = PIL.Image.open(uploaded_file_img)
img_pil = img_pil.resize((512,512))
img_pil.save('input.png')
st.image(img_pil, caption='Uploaded Image.', use_column_width=True)
st.write("")
image = cv2.imread('input.png')
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
#image = cv2.copyMakeBorder(image, 150, 150, 150, 150, cv2.BORDER_CONSTANT,value=[255,255,255])
output = predictor(image)
v = Visualizer(image[:, :, ::-1],
metadata=MetadataCatalog.get(f"tooth_segmentation_maskrcnn{o}"),
scale=2,
# remove the colors of unsegmented pixels. This option is only available for segmentation models
)
out = v.draw_instance_predictions(output["instances"].to("cpu"))
cv2.imwrite('output.png',out.get_image()[:, :, ::-1])
out_i = PIL.Image.open('output.png')
st.image(out_i, caption='Predicted Masks', use_column_width=True)
elif add_border == True and option == 'Mask RCNN v2':
cfg.MODEL.WEIGHTS = './new mask rcnn.pth'
img_pil = PIL.Image.open(uploaded_file_img)
img_pil = img_pil.resize((512,512))
img_pil.save('input.png')
st.image(img_pil, caption='Uploaded Image.', use_column_width=True)
st.write("")
image = cv2.imread('input.png')
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.copyMakeBorder(image, 150, 150, 150, 150, cv2.BORDER_CONSTANT,value=[255,255,255])
output = predictor(image)
v = Visualizer(image[:, :, ::-1],
metadata=MetadataCatalog.get(f"tooth_segmentation_maskrcnn{o}"),
scale=2,
# remove the colors of unsegmented pixels. This option is only available for segmentation models
)
out = v.draw_instance_predictions(output["instances"].to("cpu"))
cv2.imwrite('output.png',out.get_image()[:, :, ::-1])
out_i = PIL.Image.open('output.png')
st.image(out_i, caption='Predicted Masks', use_column_width=True)
elif add_border == True and option == 'Mask RCNN v2':
cfg.MODEL.WEIGHTS = './new mask rcnn.pth'
img_pil = PIL.Image.open(uploaded_file_img)
img_pil = img_pil.resize((512,512))
img_pil.save('input.png')
st.image(img_pil, caption='Uploaded Image.', use_column_width=True)
st.write("")
image = cv2.imread('input.png')
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.copyMakeBorder(image, 150, 150, 150, 150, cv2.BORDER_CONSTANT,value=[255,255,255])
output = predictor(image)
v = Visualizer(image[:, :, ::-1],
metadata=MetadataCatalog.get(f"tooth_segmentation_maskrcnn{o}"),
scale=2,
# remove the colors of unsegmented pixels. This option is only available for segmentation models
)
out = v.draw_instance_predictions(output["instances"].to("cpu"))
cv2.imwrite('output.png',out.get_image()[:, :, ::-1])
out_i = PIL.Image.open('output.png')
st.image(out_i, caption='Predicted Masks', use_column_width=True)
elif add_border == True and option == 'Mask RCNN v2':
cfg.MODEL.WEIGHTS = './new mask rcnn.pth'
img_pil = PIL.Image.open(uploaded_file_img)
img_pil = img_pil.resize((512,512))
img_pil.save('input.png')
st.image(img_pil, caption='Uploaded Image.', use_column_width=True)
st.write("")
image = cv2.imread('input.png')
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.copyMakeBorder(image, 150, 150, 150, 150, cv2.BORDER_CONSTANT,value=[255,255,255])
output = predictor(image)
v = Visualizer(image[:, :, ::-1],
metadata=MetadataCatalog.get(f"tooth_segmentation_maskrcnn{o}"),
scale=2,
# remove the colors of unsegmented pixels. This option is only available for segmentation models
)
out = v.draw_instance_predictions(output["instances"].to("cpu"))
cv2.imwrite('output.png',out.get_image()[:, :, ::-1])
out_i = PIL.Image.open('output.png')
st.image(out_i, caption='Predicted Masks', use_column_width=True)
elif add_border == False and option == 'Mask RCNN v2':
cfg.MODEL.WEIGHTS = './new mask rcnn.pth'
img_pil = PIL.Image.open(uploaded_file_img)
img_pil = img_pil.resize((512,512))
img_pil.save('input.png')
st.image(img_pil, caption='Uploaded Image.', use_column_width=True)
st.write("")
image = cv2.imread('input.png')
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
#image = cv2.copyMakeBorder(image, 150, 150, 150, 150, cv2.BORDER_CONSTANT,value=[255,255,255])
output = predictor(image)
v = Visualizer(image[:, :, ::-1],
metadata=MetadataCatalog.get(f"tooth_segmentation_maskrcnn{o}"),
scale=2,
# remove the colors of unsegmented pixels. This option is only available for segmentation models
)
out = v.draw_instance_predictions(output["instances"].to("cpu"))
cv2.imwrite('output.png',out.get_image()[:, :, ::-1])
out_i = PIL.Image.open('output.png')
st.image(out_i, caption='Predicted Masks', use_column_width=True)
"""