(9条消息) OCR预处理：矫正图片中的文本信息（opencv）
1.首先导入工程所需要的三方包，这里需要opencv、numpy、math
import cv2import numpy as npimport math2.读取图片
img = cv2.imread(file_path)3.图片去噪
img_c = cv2.fastNlMeansDenoisingColored(img, None, 10, 10, 7, 21)4.处理成灰度图
gray = cv2.cvtColor(img_c, cv2.COLOR_BGR2GRAY)5.Sobel算子，x方向求梯度,主要用于获得数字图像的一阶梯度，常见的应用和物理意义是边缘检测。（可以根据需求选择算子）
sobel = cv2.Sobel(gray, cv2.CV_8U, 1, 0, ksize=3)6.二值化
ret, binary = cv2.threshold(sobel, 0, 255, cv2.THRESH_OTSU + cv2.THRESH_BINARY)7.霍夫直线
hufu = binary.astype(np.uint8) lines = cv2.HoughLinesP(hufu, 1, np.pi / 180, 30, minLineLength=40, maxLineGap=100) # 在图像上展示霍夫直线描出的直线 # for line in lines: # cv2.line(img, (line[0][0], line[0][1]), (line[0][2], line[0][3]), (0, 0, 255), 2)8.求出所有直线斜率，求出众数（考虑误差），这一步会产生个bug，因为这些直线是利用霍夫直线找到的，所以说需要根据实际场景来设置霍夫直线的相关参数，不然会对图片中的线性非常敏感，比如条形码
k_dict = {} k = 0 for line in lines: if line[0][2] - line[0][0] == 0: continue print(line[0][3],line[0][1],line[0][2],line[0][0]) k = (line[0][3] - line[0][1]) / (line[0][2] - line[0][0]) # α = atan(k) * 180 / PI k = math.atan(k) * 180 / np.pi if len(k_dict.keys()) == 0 : k_dict[k] = 1 else: flag = False for item in k_dict.keys(): if abs(item - k) < 2: flag = True k_dict[item] += 1 break if not flag: k_dict[k] = 1 must_k_num = 0 must_key = 0 for item in k_dict.keys(): if k_dict[item] > must_k_num: must_k_num = k_dict[item] must_key = item print(must_key)9.旋转图像，在旋转图像之前需要对图片进行填充防止旋转后边角溢出（这一步可以根据角度和勾股定理来计算精准的填充大小），利用仿射变换来旋转图像
#旋转图像 h, w = img.shape[:2] add_w = int((((w*w + h*h) ** 0.5) - w)/2) add_h = int((((w*w + h*h) ** 0.5) - h)/2) print(add_w,add_h) img = cv2.copyMakeBorder(img,add_h,add_h,add_w,add_w, cv2.BORDER_CONSTANT,value=[0,0,0]) h, w = img.shape[:2] center = (w//2, h//2) M = cv2.getRotationMatrix2D(center, must_key, 1.0) rotated = cv2.warpAffine(img, M, (w, h), flags=cv2.INTER_CUBIC) cv2.imwrite(file_path,rotated) cv2.imshow("rotated", rotated) cv2.waitKey(0)全部代码在这！！！！！！
import cv2import numpy as npimport mathdef rotate(file_path): img = cv2.imread(file_path) #去噪 img_c = cv2.fastNlMeansDenoisingColored(img, None, 10, 10, 7, 21) # 灰度图 gray = cv2.cvtColor(img_c, cv2.COLOR_BGR2GRAY) # cv2.imshow("gray", gray) # Sobel算子，x方向求梯度,主要用于获得数字图像的一阶梯度，常见的应用和物理意义是边缘检测。 sobel = cv2.Sobel(gray, cv2.CV_8U, 1, 0, ksize=3) # 二值化 ret, binary = cv2.threshold(sobel, 0, 255, cv2.THRESH_OTSU + cv2.THRESH_BINARY) # 霍夫直线 hufu = binary.astype(np.uint8) lines = cv2.HoughLinesP(hufu, 1, np.pi / 180, 30, minLineLength=40, maxLineGap=100) # for line in lines: # cv2.line(img, (line[0][0], line[0][1]), (line[0][2], line[0][3]), (0, 0, 255), 2) k_dict = {} k = 0 #求出所有直线斜率，求出众数（考虑误差） for line in lines: if line[0][2] - line[0][0] == 0: continue print(line[0][3],line[0][1],line[0][2],line[0][0]) k = (line[0][3] - line[0][1]) / (line[0][2] - line[0][0]) # α = atan(k) * 180 / PI k = math.atan(k) * 180 / np.pi if len(k_dict.keys()) == 0 : k_dict[k] = 1 else: flag = False for item in k_dict.keys(): if abs(item - k) < 2: flag = True k_dict[item] += 1 break if not flag: k_dict[k] = 1 must_k_num = 0 must_key = 0 for item in k_dict.keys(): if k_dict[item] > must_k_num: must_k_num = k_dict[item] must_key = item print(must_key) #旋转图像 h, w = img.shape[:2] add_w = int((((w*w + h*h) ** 0.5) - w)/2) add_h = int((((w*w + h*h) ** 0.5) - h)/2) print(add_w,add_h) img = cv2.copyMakeBorder(img,add_h,add_h,add_w,add_w, cv2.BORDER_CONSTANT,value=[0,0,0]) h, w = img.shape[:2] center = (w//2, h//2) M = cv2.getRotationMatrix2D(center, must_key, 1.0) rotated = cv2.warpAffine(img, M, (w, h), flags=cv2.INTER_CUBIC) cv2.imwrite(file_path,rotated) cv2.imshow("rotated", rotated) cv2.waitKey(0)谢谢大家观看！
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