【OpenCV文字识别】

一 数据展示

[

二 结果展示

代码：

链接: https://pan.baidu.com/s/1Dp-9WRmz_Pbxwv21oBOkWA 提取码: a8uq

# 导入工具包
import numpy as np
import argparse
import cv2
# --image ./images/receipt.jpg
# opencv4.7  cv2.findContours返还两个值。第一个是轮廓，第二个没用
# 设置参数
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

# 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
cnts = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[0]
cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:5]

# 遍历轮廓
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
      break

# 展示结果
print("STEP 2: 获取轮廓")
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)