基于Opencv与tesseract 实现的文本识别

# 导入工具包 import numpy as np import argparse import cv2 # 设置参数 ap = argparse.ArgumentParser() ap.add_argument("-i", "--image",default="images/page.jpg", help = "Path to the image to be scanned") args = vars(ap.parse_args()) def order_points(pts): # 一共4个坐标点 rect = np.zeros((4, 2), dtype = "float32") # 按顺序找到对应坐标0123分别是 左上，右上，右下，左下 # 计算左上，右下 s = pts.sum(axis = 1) rect[0] = pts[np.argmin(s)] rect[2] = pts[np.argmax(s)] # 计算右上和左下 diff = np.diff(pts, axis = 1) rect[1] = pts[np.argmin(diff)] rect[3] = pts[np.argmax(diff)] return rect def four_point_transform(image, pts): # 获取输入坐标点 rect = order_points(pts) (tl, tr, br, bl) = rect # 计算输入的w和h值 widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2)) widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2)) maxWidth = max(int(widthA), int(widthB)) heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2)) heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2)) maxHeight = max(int(heightA), int(heightB)) # 变换后对应坐标位置 dst = np.array([ [0, 0], [maxWidth - 1, 0], [maxWidth - 1, maxHeight - 1], [0, maxHeight - 1]], dtype = "float32") # 计算变换矩阵 M = cv2.getPerspectiveTransform(rect, dst) warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight)) # 返回变换后结果 return warped def resize(image, width=None, height=None, inter=cv2.INTER_AREA): dim = None (h, w) = image.shape[:2] if width is None and height is None: return image if width is None: r = height / float(h) dim = (int(w * r), height) else: r = width / float(w) dim = (width, int(h * r)) resized = cv2.resize(image, dim, interpolation=inter) return resized if __name__ == '__main__': # 读取输入 image = cv2.imread(args["image"]) #坐标也会相同变化 ratio = image.shape[0] / 500.0 orig = image.copy() image = resize(orig, height = 500) # 预处理 gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) gray = cv2.GaussianBlur(gray, (5, 5), 0) edged = cv2.Canny(gray, 75, 200) # 展示预处理结果 print("STEP 1: 边缘检测") cv2.imshow("Image", image) cv2.imshow("Edged", edged) cv2.waitKey(0) cv2.destroyAllWindows() # 轮廓检测 opencv3 # cnts = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[1][0] # cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:5] # 使用OpenCV 4.x的方式来调用findContours contours, hierarchy = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) # 确保轮廓是适当的数据类型 cnts = [np.array(cnt, dtype='float32') for cnt in contours] # 排序并选择最大的5个轮廓 cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:5] # 遍历轮廓 screenCnt = None for c in cnts: # 计算轮廓近似 peri = cv2.arcLength(c, True) # C表示输入的点集 # epsilon表示从原始轮廓到近似轮廓的最大距离，它是一个准确度参数 # True表示封闭的 approx = cv2.approxPolyDP(c, 0.02 * peri, True) # 4个点的时候就拿出来 if len(approx) == 4: screenCnt = approx.astype(int) break if screenCnt is not None: # 展示结果 print("STEP 2: 获取轮廓") # print(screenCnt) cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 2) cv2.imshow("Outline", image) cv2.waitKey(0) cv2.destroyAllWindows() # 透视变换 warped = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio) # 二值处理 warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY) ref = cv2.threshold(warped, 100, 255, cv2.THRESH_BINARY)[1] cv2.imwrite('scan.jpg', ref) # 展示结果 print("STEP 3: 变换") cv2.imshow("Original", resize(orig, height = 650)) cv2.imshow("Scanned", resize(ref, height = 650)) cv2.waitKey(0)