Contour detection with adaptive gaussian thresholding

contour_adaptive.py

import cv2
import numpy as np
image = cv2.imread('images_09_03/Lot1_RT.jpeg', 1)
img_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# apply adaptive gaussian thresholding
thresh = cv2.adaptiveThreshold(img_gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,11,2)

cv2.imshow('primary', image)
cv2.imshow('gray', img_gray)
cv2.imshow('binary', thresh)

# detect the contours on the binary image using CHAIN_APPROX_SIMPLE and mode RETR_EXTERNAL vs _TREE
contours, hierarchy = cv2.findContours(image=thresh, mode=cv2.RETR_EXTERNAL, method=cv2.CHAIN_APPROX_SIMPLE)

# draw contours on the original image
image_copy = image.copy()
cv2.drawContours(image=image_copy, contours=contours, contourIdx=-1, color=(0, 255, 0), thickness=2,
                 lineType=cv2.LINE_AA)

# see the results
cv2.imshow('Outlined', image_copy)

cv2.waitKey(0)
cv2.imwrite('images_09_03/Lot1_RT_outlined.jpeg', image_copy)
cv2.destroyAllWindows()