scan.py:
# 導入工具包 import numpy as np import argparse import cv2 # 設置參數 ap = argparse.ArgumentParser() ap.add_argument("-i", "--image", required = True, help = "Path to the image to be scanned") args = vars(ap.parse_args()) def order_points(pts): # 一共4個坐標點 rect = np.zeros((4, 2), dtype = "float32") # 按順序找到對應坐標0123分別是 左上，右上，右下，左下 # 計算左上，右下 s = pts.sum(axis = 1) rect[0] = pts[np.argmin(s)] rect[2] = pts[np.argmax(s)] # 計算右上和左下 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): # 獲取輸入坐標點 rect = order_points(pts) (tl, tr, br, bl) = rect # 計算輸入的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)) # 變換后對應坐標位置 dst = np.array([ [0, 0], [maxWidth - 1, 0], [maxWidth - 1, maxHeight - 1], [0, maxHeight - 1]], dtype = "float32") # 計算變換矩陣 M = cv2.getPerspectiveTransform(rect, dst) warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight)) # 返回變換后結果 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 # 讀取輸入 image = cv2.imread(args["image"]) #坐標也會相同變化 ratio = image.shape[0] / 500.0 orig = image.copy() image = resize(orig, height = 500) # 預處理 gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) gray = cv2.GaussianBlur(gray, (5, 5), 0) edged = cv2.Canny(gray, 75, 200) # 展示預處理結果 print("STEP 1: 邊緣檢測") cv2.imshow("Image", image) cv2.imshow("Edged", edged) cv2.waitKey(0) cv2.destroyAllWindows() # 輪廓檢測 cnts = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[1] cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:5] # 遍歷輪廓 for c in cnts: # 計算輪廓近似 peri = cv2.arcLength(c, True) # C表示輸入的點集 # epsilon表示從原始輪廓到近似輪廓的最大距離，它是一個准確度參數 # True表示封閉的 approx = cv2.approxPolyDP(c, 0.02 * peri, True) # 4個點的時候就拿出來 if len(approx) == 4: screenCnt = approx break # 展示結果 print("STEP 2: 獲取輪廓") cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 2) cv2.imshow("Outline", image) cv2.waitKey(0) cv2.destroyAllWindows() # 透視變換 warped = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio) # 二值處理 warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY) ref = cv2.threshold(warped, 100, 255, cv2.THRESH_BINARY)[1] cv2.imwrite('scan.jpg', ref) # 展示結果 print("STEP 3: 變換") cv2.imshow("Original", resize(orig, height = 650)) cv2.imshow("Scanned", resize(ref, height = 650)) cv2.waitKey(0)
效果：
利用tesseract工具識別出字符：
# https://digi.bib.uni-mannheim.de/tesseract/ # 配置環境變量如E:\Program Files (x86)\Tesseract-OCR # tesseract -v進行測試 # tesseract XXX.png 得到結果 # pip install pytesseract # anaconda lib site-packges pytesseract pytesseract.py # tesseract_cmd 修改為絕對路徑即可 from PIL import Image import pytesseract import cv2 import os preprocess = 'blur' #thresh image = cv2.imread('scan.jpg') gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) if preprocess == "thresh": gray = cv2.threshold(gray, 0, 255,cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1] if preprocess == "blur": gray = cv2.medianBlur(gray, 3) filename = "{}.png".format(os.getpid()) cv2.imwrite(filename, gray) text = pytesseract.image_to_string(Image.open(filename)) print(text) os.remove(filename) cv2.imshow("Image", image) cv2.imshow("Output", gray) cv2.waitKey(0)
效果：
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