processing_pipeline.py

import warnings
warnings.filterwarnings('ignore')
import cv2, math
import numpy as np

def process(image, thresh_lower, thresh_upper):
    cv2.rectangle(image, (60, 60), (300, 300), (0, 255, 0), 4)
    roi = crop_img = image[70 : 300, 70 : 300]
    gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
    hsv = cv2.cvtColor(roi, cv2.COLOR_BGR2LUV)
    lower_red = np.array([0, 150, 50])
    upper_red = np.array([195, 255, 255])
    mask = cv2.inRange(hsv, lower_red, upper_red)
    blurred = cv2.GaussianBlur(gray, (35, 35), 0)
    _, thresh1 = cv2.threshold(blurred, thresh_lower, thresh_upper, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
    _, contours, hierarchy = cv2.findContours(thresh1.copy(),cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
    cnt = max(contours, key = lambda x: cv2.contourArea(x))
    area_of_contour = cv2.contourArea(cnt)
    x, y, w, h = cv2.boundingRect(cnt)
    roi_1 = roi.copy()
    roi_2 = roi.copy()
    cv2.rectangle(roi_1, (x, y), (x + w, y + h), (0, 0, 255), 1)
    hull = cv2.convexHull(cnt)
    drawing = np.zeros(roi_2.shape, np.uint8)
    cv2.drawContours(drawing, [cnt], 0, (0, 255, 0), 0)
    cv2.drawContours(drawing, [hull], 0, (0, 255, 255), 0)
    hull  = cv2.convexHull(cnt, returnPoints = False)
    defects = cv2.convexityDefects(cnt, hull)
    cv2.drawContours(thresh1, contours, -1, (0, 255, 0), 3)
    count_defects = 0
    for i in range(defects.shape[0]):
        s, e, f, d = defects[i, 0]
        start = tuple(cnt[s][0])
        end = tuple(cnt[e][0])
        far = tuple(cnt[f][0])
        a = math.sqrt((end[0] - start[0]) ** 2 + (end[1] - start[1]) ** 2)
        b = math.sqrt((far[0] - start[0]) ** 2 + (far[1] - start[1]) ** 2)
        c = math.sqrt((end[0] - far[0]) ** 2 + (end[1] - far[1]) ** 2)
        angle = math.acos((b ** 2 + c ** 2 - a ** 2) / (2 * b * c)) * 60
        cv2.circle(crop_img, far, 4, [0, 0, 255], -1)
        if angle <= 90:
            count_defects += 1
        cv2.line(crop_img, start, end, [0, 255, 0], 3)
    return roi_1, drawing, thresh1, crop_img, count_defects