commit 84db6cfc9449ade0aa5c681d179d2845655c0ed5
Author: maochaoying <925670706@qq.com>
Date: Thu Jul 20 18:20:07 2023 +0800
胶带平行检测
diff --git a/.DS_Store b/.DS_Store
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diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml
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diff --git a/.idea/workspace.xml b/.idea/workspace.xml
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diff --git a/app.py b/app.py
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+++ b/app.py
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+# coding=utf-8
+import cv2
+import numpy as np
+import math
+
+# 修改图像的对比度,coefficent>0, <1降低对比度,>1提升对比度 建议0-2
+def change_contrast(img, coefficent):
+ imggray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+ m = cv2.mean(img)[0]
+ graynew = m + coefficent * (imggray - m)
+ img1 = np.zeros(img.shape, np.float32)
+ k = np.divide(graynew, imggray, out=np.zeros_like(graynew), where=imggray != 0)
+ img1[:, :, 0] = img[:, :, 0] * k
+ img1[:, :, 1] = img[:, :, 1] * k
+ img1[:, :, 2] = img[:, :, 2] * k
+ img1[img1 > 255] = 255
+ img1[img1 < 0] = 0
+ return img1.astype(np.uint8)
+
+# 修改图像的亮度，brightness取值0～2 <1表示变暗 >1表示变亮
+def change_brightness(img, brightness):
+ [averB, averG, averR] = np.array(cv2.mean(img))[:-1] / 3
+ k = np.ones((img.shape))
+ k[:, :, 0] *= averB
+ k[:, :, 1] *= averG
+ k[:, :, 2] *= averR
+ img = img + (brightness - 1) * k
+ img[img > 255] = 255
+ img[img < 0] = 0
+ return img.astype(np.uint8)
+
+# 对图像二值化并取中值滤波
+def threshold_img(image):
+ gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+ retval, dst = cv2.threshold(gray, 0, 255, cv2.THRESH_OTSU)
+ img_median = cv2.medianBlur(dst, 3)
+ return img_median
+
+# 腐蚀图像
+def corrosion_img(image):
+ # 腐蚀操作会把裂痕也消除
+ kernel = np.ones((9, 9), dtype=np.uint8)
+ opening = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel, 1)
+ return opening
+
+# 找出最右侧的近似竖直直线 通过直线与水平的角度判断是否倾斜 大于容错率的竖直 小于容错率的倾斜
+def lines_img(image):
+ slopes = []
+ yErrorRange = 3
+ xErrorRange = 3
+ minLength = 20
+ # 创建一个LSD对象
+ lsd = cv2.createLineSegmentDetector(0)
+ # 执行检测结果
+ dlines = lsd.detect(image)
+ # 绘制检测结果
+ for dline in dlines[0]:
+ x0 = int(round(dline[0][0]))
+ y0 = int(round(dline[0][1]))
+ x1 = int(round(dline[0][2]))
+ y1 = int(round(dline[0][3]))
+ length = math.hypot(x1 - x0, y1 - y0)
+ if (x0 > 605 and x1 > 605):
+ # print((x0, y0), (x1, y1))
+ # 需要根据角坐标排除 横向线，针对纵向线判断是否有倾斜
+ # 过滤横线
+ if not (abs(x1 - x0) > xErrorRange and abs(y1 - y0) < yErrorRange):
+ # 过滤线段长度
+ if not (length < minLength):
+ if x1 - x0 != 0:
+ k = abs(y1 - y0) / abs(x1 - x0)
+ angle = math.atan(k) * 180 / math.pi
+ slopes.append(angle)
+ print(angle)
+ # cv2.line(img0, (x0, y0), (x1,y1), (0,255,0), 1, cv2.LINE_AA)
+ # 有小于85度的就证明胶带倾斜了
+ threshold = 85
+ res = any(x < threshold for x in slopes)
+ if res:
+ print("胶带倾斜")
+ else:
+ print("胶带正确")
+
+
+def detectionImg():
+ # 读取输入图片
+ img = cv2.imread("img/a.png")
+ l = 0
+ c = 34
+ # 亮度 -1~1
+ imgBrightness = change_brightness(img, float(l - 50) / float(50))
+ # 对比度 0~2
+ img0 = change_contrast(imgBrightness, c / 50)
+ thresholdImg = threshold_img(img0)
+ corrosionImg = corrosion_img(thresholdImg)
+ lines_img(corrosionImg)
+ # 显示并保存结果
+ cv2.imshow("gray", corrosionImg)
+ cv2.imshow("lines", img0)
+ cv2.waitKey(0)
+ cv2.destroyAllWindows()
+
+
+if __name__ == '__main__':
+ detectionImg()
+
+
+
diff --git a/img/.DS_Store b/img/.DS_Store
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diff --git a/img/1.pic.jpg b/img/1.pic.jpg
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diff --git a/img/a.png b/img/a.png
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diff --git a/img/lie.jpg b/img/lie.jpg
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