import cv2
import numpy as np

# import image
image = cv2.imread('Snickers_0.bmp')
cv2.imshow('original', image)

# creating kernel
kernel = np.ones((20, 20), np.uint8)

# convert image into grayscale
grayscale_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.imshow('gray', grayscale_image)

# binary image
ret, thresh = cv2.threshold(grayscale_image, 70, 255, cv2.THRESH_BINARY)
cv2.imshow('threshold', thresh)

# performing morphological transformation - closing

closing = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
cv2.imshow('morphological closing', closing)

ctrs, hier = cv2.findContours(closing.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# sort contours
sorted_ctrs = sorted(ctrs, key=lambda ctr: cv2.boundingRect(ctr)[0])

for i, ctr in enumerate(sorted_ctrs):
    # Get bounding box
    x, y, w, h = cv2.boundingRect(ctr)
    x -= 10
    y -= 10
    w += 10
    h += 10

    if w <= 30 or h <= 30:
        continue

    print("x min: {}", x)
    print("y min: {}", y)
    print("x max: {}", x+w)
    print("y max: {}", y+h)

    # Getting ROI
    roi = image[y:y + h, x:x + w]

    cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)


cv2.imshow('marked areas', image)
cv2.waitKey(0)
'''
# binary
ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY_INV)
cv2.imshow('threshold', thresh)

thresh = cv2.bitwise_not(thresh)

cv2.imshow('inverted threshold', thresh)

ctrs, hier = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# sort contours
sorted_ctrs = sorted(ctrs, key=lambda ctr: cv2.boundingRect(ctr)[0])

for i, ctr in enumerate(sorted_ctrs):
    # Get bounding box
    x, y, w, h = cv2.boundingRect(ctr)

    # Getting ROI
    roi = image[y:y + h, x:x + w]

    # show ROI
    # cv2.imshow('segment no:'+str(i),roi)
    cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)

    if w > 15 and h > 15:
        cv2.imwrite('C:\\Users\\PC\\Desktop\\output\\{}.png'.format(i), roi)

cv2.imshow('marked areas', image)
cv2.waitKey(0)
'''