# https://www.globalsino.com/ICs/
# Find contours in an image, and their areas, coordinates, and contour centers


import cv2
import numpy as np
from scipy.spatial import distance

sample_img = cv2.imread(r"C:\GlobalSino2\ICs\images\4367c.jpg")

# convert to black and white color space
sample_img_grey = cv2.cvtColor(sample_img, cv2.COLOR_BGR2GRAY)
contours, hierarchy = cv2.findContours(sample_img_grey, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

#find center of image and draw it (blue circle)
image_center = np.asarray(sample_img_grey.shape) / 2
image_center = tuple(image_center.astype('int32'))
cv2.circle(sample_img, image_center, 3, (255, 100, 0), 2)

IndividualContour = []
for contour in contours:
    area = cv2.contourArea(contour)
    if area > 300:
        # find center of each contour
        M = cv2.moments(contour)
        if M["m00"] !=0:        
            center_X = int(M["m10"] / M["m00"])
            center_Y = int(M["m01"] / M["m00"])
        else:
            center_X = 0
            center_Y = 0        
        contour_center = (center_X, center_Y)

        # calculate distance to image_center
        distances_to_center = (distance.euclidean(image_center, contour_center))

        # save to a list of dictionaries
        IndividualContour.append({'contour': contour, 'center': contour_center, 'distance_to_center': distances_to_center})
        
        # draw each contour (red)
        cv2.drawContours(sample_img, [contour], 0, (0, 50, 255), 2)
        M = cv2.moments(contour)

        # draw center of contour (green)
        cv2.circle(sample_img, contour_center, 3, (100, 255, 0), 2)

# sort the IndividualContour
sorted_IndividualContour = sorted(IndividualContour, key=lambda i: i['distance_to_center'])

# find contour of closest building to center and draw it (blue)
center_building_contour = sorted_IndividualContour[0]['contour']
cv2.drawContours(sample_img, [center_building_contour], 0, (255, 0, 0), 2)

cv2.imshow("Image", sample_img)
cv2.waitKey(0)