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detect.py
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detect.py
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import cv2
import numpy as np
import glob
import matplotlib.pyplot as plt
def compare_images(images):
plt.figure(figsize=(10,5))
columns = len(images)
for index, img in enumerate(images, 1):
plt.subplot(1 ,columns, index)
plt.axis('off')
plt.imshow(img)
plt.tight_layout()
plt.show()
def binarize(gray_img):
_, thresh = cv2.threshold(gray_img, 127, 255, 0)
return thresh
def find_contours(thresh):
contours, _ = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
sorted_contours = sorted(contours, key=cv2.contourArea, reverse=True)
largest_contours = sorted_contours[:10]
return largest_contours
if __name__ == '__main__':
files = glob.glob('Additional_test_data/small/*')
img = cv2.imread(files[30])
imgray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
blurred_image = cv2.GaussianBlur(imgray, (5, 5), 0)
binary_img = binarize(blurred_image)
contours = find_contours(binary_img)
height, width = img.shape[:2]
img_empty = np.zeros((height, width, 3))
cv2.drawContours(img_empty, contours, -1, (0, 255, 0), 3)
compare_images([img, img_empty, binary_img])