在Pycharm中用OpenCV的imutils模块矫正图片

在OpenCV中有一个imutils模块在图形变换中很是方便，下面就是用该模块对图片进行仿射变换的代码。

 1 from imutils import perspective  2 from skimage.filters import threshold_local  3 import cv2  4 import imutils  5 # 边缘扫描
 6 image = cv2.imread("C:\\Users\\lenovo\\Pictures\\t1.png")  7 ratio = image.shape[0] / 500.0# 比例
 8 orig = image.copy()  9 image = imutils.resize(image, height = 500) 10 # 灰度转换及边缘查找
11 gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 12 gray = cv2.GaussianBlur(gray, (5, 5), 0) 13 edged = cv2.Canny(gray, 75, 200)                          # 边缘检测
14 # 只保留轮廓
15 cnts = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)#经过边缘图像找到轮廓
16 cnts = cnts[0] if imutils.is_cv2() else cnts[1] 17 # 用以区分OpenCV2.4和OpenCV3
18 cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:5] # 保留最大轮廓
19 for c in cnts: 20     peri = cv2.arcLength(c, True) 21     approx = cv2.approxPolyDP(c, 0.02 * peri, True)            # 轮廓点
22     if len(approx) == 4:                                       # 代表找到四个轮廓点
23         screenCnt = approx 24         break
25 # 转为鸟瞰图
26 warped = perspective.four_point_transform(orig, screenCnt.reshape(4, 2) * ratio) 27 warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)              # 灰度转换
28 T = threshold_local(warped, 11, offset = 10, method = "gaussian") 29 warped = (warped > T).astype("uint8") * 255
30 cv2.imshow("Original", imutils.resize(orig, height = 650)) 31 cv2.imshow("Scanned", imutils.resize(warped, height = 650)) 32 cv2.waitKey(0)

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变换后的图片以下图所示