Python OpenCV4趣味应用系列---缺陷检测

今天来一个缺陷检测的实例，如下是原图，第二个和第三个黑色部件有缺陷

思路:

①提取OK部件轮廓做model

②遍历部件轮廓，做差分，形态学处理

③结果判断绘制

上代码(含注释）：

import cv2
import numpy as np
# 获取模板ROI
def get_template(binary, boxes):
    x, y, w, h = boxes[0]
    roi = binary[y:y+h, x:x+w]
    return roi
def detect_defect(binary, boxes, tpl):
    global src
    height, width = tpl.shape
    index = 1
    defect_rois = []
    # 发现缺失
    for x, y, w, h in boxes:
        roi = binary[y:y + h, x:x + w]
        roi = cv2.resize(roi, (width, height))
        mask = cv2.subtract(tpl, roi)
        se = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5), (-1, -1))
        mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, se)
        ret, mask = cv2.threshold(mask, 0, 255, cv2.THRESH_BINARY)
        mask, contours, hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        color_mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)
        cv2.drawContours(color_mask, contours, -1, (0, 255, 255), -1, 8)
        mask_roi = src[y:y + height, x:x + width]
        cv2.bitwise_or(color_mask, mask_roi, mask_roi, mask = mask)
        count = 0
        for row in range(height):
            for col in range(width):
                pv = mask[row, col]
                if pv == 255:
                    count += 1
        if count > 0:
            defect_rois.append([x, y, w, h])
        else:
            cv2.rectangle(src, (x, y), (x+w, y+h), (0, 255, 0), 2, 8, 0)
            cv2.putText(src, "OK", (x, y - 2), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
        cv2.imwrite("mask%d.png"%index, mask)
        index += 1
    return defect_rois
global src
src = cv2.imread("1.jpg")
cv2.imshow("input", src)
# 图像二值化
gray = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
ret, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)
# 形态学开运算
se = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3), (-1, -1))
binary = cv2.morphologyEx(binary, cv2.MORPH_OPEN, se)
cv2.imshow("binary", binary)
# 轮廓提取
out, contours, hierarchy = cv2.findContours(binary, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
height, width = src.shape[:2]
rects = []
# 轮廓筛选
for c in range(len(contours)):
    # 轮廓外接矩形
    x, y, w, h = cv2.boundingRect(contours[c])
    # 轮廓面积
    area = cv2.contourArea(contours[c])
    if h > (height//2):
        continue
    if area < 150:
        continue
    rects.append([x, y, w, h])
# 排序轮廓(按照rect中y值排序也就是纵坐标从小到大)
rects = sorted(rects, key = lambda x:x[1])
for i in range(len(rects)):
    print(rects[i])
# 获取模板ROI
template = get_template(binary, rects)
cv2.imshow("template", template)
# 填充边缘
for c in range(len(contours)):
    x, y, w, h = cv2.boundingRect(contours[c])
    area = cv2.contourArea(contours[c])
    if h > (height//2):
        continue
    if area < 150:
        continue
    cv2.drawContours(binary, contours, c, (0), 2, 8)
# 检测缺陷
defect_boxes = detect_defect(binary, rects, template)
for dx, dy, dw, dh in defect_boxes:
    cv2.rectangle(src, (dx, dy), (dx + dw, dy + dh), (0, 0, 255), 2, 8, 0)
    cv2.putText(src, "NG", (dx, dy-2), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2)
index = 1
for dx, dy, dw, dh in rects:
    cv2.putText(src, "No.%d"%index, (dx-60, dy+30), cv2.FONT_HERSHEY_SIMPLEX, .7, (255, 0, 255), 2)
    index += 1
cv2.imshow("result", src)
cv2.imwrite("binary2.png", src)
cv2.waitKey(0)
cv2.destroyAllWindows()

其中76,77行说明：

# 排序轮廓(按照rect中y值排序也就是纵坐标从小到大)
rects = sorted(rects, key = lambda x:x[1]

最终检测效果(红色标注NG部件，黄色标注缺陷点)：

文章来源:OpenCV与AI深度学习 ，作者Color Space