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测试图片自取
page.jpg
import cv2
import argparse
import numpy as np
import matplotlib.pyplot as plt
def cv_show(name, img):cv2.imshow(name, img)cv2.waitKey(0)cv2.destroyAllWindows()def plt_show(img):b, g, r = cv2.split(img)res = cv2.merge([r, g, b])plt.imshow(res)
# 设置args参数，这里我们直接用字典表示
args = {'image': 'page.jpg'}
# 读取输入
image = cv2.imread(args['image'])
plt_show(image)
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 imageif width is None:r = height / float(h)dim = (int(w*r), height)else:r = width / float(w)dim = (wigth, int(h*r))resized = cv2.resize(image, dim, interpolation=inter)return resized
# 图像resize
ratio = image.shape[0] / 500.0
orig = image.copy()
image = resize(orig, height=500)
plt_show(image)
#灰度化和滤波操作不必多说，进行边缘检测的目的是为下一步的轮廓检测做准备# 灰度化、滤波、边缘检测
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")
plt_show(image)plt.imshow(edged, cmap='gray')
#使用cv2.findContours()函数进行轮廓检测，并进行轮廓排序
cnts = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[1]
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:5]
#因为这些轮廓可能只是一些离散的点，或者是一些不规则图形，这里我们将轮廓近似为矩形
# 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# epslion表示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 = approxbreak
# 展示结果
print("STEP 2: 获取轮廓")
cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 2)
plt_show(image)
#透视与二值变换 这里我们定义了four_point_transform()函数
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(两点之间的距离公式)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 warpeddef 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、左下diff = np.diff(pts, axis=1)rect[1] = pts[np.argmin(diff)]rect[3] = pts[np.argmax(diff)]return rect
# 透视变换
warped = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio)
plt_show(warped)
# 二值处理
warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)ref = cv2.threshold(warped, 100, 255, cv2.THRESH_BINARY)[1]
plt.imshow(warped, cmap='gray')
