python-opencv 计算工件的缺陷面积

python-opencv 计算工件的缺陷面积

1. 寻找工件轮廓，并计算其面积
2. 寻找工件的最小外接矩形，并计算其面积
3. 计算轮廓面积和矩形面积之差。

工件如下图所示：

import imutils
import cv2
import numpy as np

image = cv2.imread("111.bmp")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# blurred = cv2.GaussianBlur(gray, (5, 5), 0)
thresh = cv2.threshold(gray, 60, 255, cv2.THRESH_BINARY)[1]

cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)

# 遍历轮廓集
for c in cnts:
    M = cv2.moments(c)
    area = cv2.contourArea(c)
    if area <1000000:
        continue

    # 在图像上绘制轮廓
    cv2.drawContours(image, [c], -1, (0, 255, 0), 2)
    print("area:",area)

    # 寻找轮廓的最小外接矩形
    # x, y, w, h = cv2.boundingRect(c)
    # cv2.rectangle(image,(x, y), (x + w, y + h), (0, 0, 255), 2)

    # 获取最小外接矩阵，中心点坐标，宽高，旋转角度
    rect = cv2.minAreaRect(c)
    # 获取矩形四个顶点，浮点型
    box = cv2.boxPoints(rect)
    box = np.int0(box)
    print(box)

    left_point_x   = np.min(box[:, 0])
    right_point_x  = np.max(box[:, 0])
    top_point_y    = np.max(box[:, 1])
    bottom_point_y = np.min(box[:, 1])

    left_point_y   = sorted(box[:, 1][np.where(box[:, 0] == left_point_x)])[-1]
    right_point_y  = sorted(box[:, 1][np.where(box[:, 0] == right_point_x)])[0]
    top_point_x    = sorted(box[:, 0][np.where(box[:, 1] == top_point_y)])[-1]
    bottom_point_x = sorted(box[:, 0][np.where(box[:, 1] == bottom_point_y)])[0]

    vertices = [[left_point_x, left_point_y],[bottom_point_x, bottom_point_y],
                [right_point_x, right_point_y], [top_point_x, top_point_y]]

    print(vertices)

    cv2.circle(image, (left_point_x, left_point_y), 20, (255, 255, 255), -1)
    cv2.circle(image, (bottom_point_x, bottom_point_y), 20, (255, 0, 0), -1)
    cv2.circle(image, (top_point_x, top_point_y), 20, (0, 255, 0), -1)
    cv2.circle(image, (right_point_x, right_point_y), 20, (0, 0, 255), -1)

    # 计算矩形的最长边和最短边
    long_size = np.sqrt(np.math.pow((left_point_x-bottom_point_x),2) + np.math.pow((left_point_y-bottom_point_y),2))
    short_size = np.sqrt(np.math.pow((left_point_x-top_point_x),2) + np.math.pow((left_point_y-top_point_y),2))
    print("矩形的面积：",long_size*short_size)
    print("轮廓的面积：",area)
    poor_area = (long_size * short_size - area)/area
    print("缺陷面积占轮廓面积的比例：",poor_area*100,"%")