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project2.py
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import cv2
import numpy as np
##########################
imgWidth = 480
imgHeight = 640
##########################
cap = cv2.VideoCapture(0)
cap.set(3, imgWidth)
cap.set(4, imgHeight)
cap.set(10, 150)
def preProcessing(img):
"""
对图像进行预处理
:param img:
:return:
"""
imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
imgBlur = cv2.GaussianBlur(imgGray, (5, 5), 1)
imgCanny = cv2.Canny(imgBlur, 200, 200)
kernel = np.ones((5, 5))
imgDila = cv2.dilate(imgCanny, kernel, iterations=2)
imgThres = cv2.erode(imgDila, kernel, iterations=1) # imgErode
return imgThres
def getContours(img):
"""
对预处理后的图像，获取文档轮廓
:param img:
:return:
"""
biggest = np.array([])
maxArea = 0
contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE) # 图片，检索方法，cv2.RETR_EXTERNAL 检索极端外部轮廓, 不需要近似
for cnt in contours:
area = cv2.contourArea(cnt)
if area > 5000: # 忽略噪声影响 形状面积大于500
# cv2.drawContours(imgContour, cnt, -1, (255, 0, 0), 3) # contourIdx=-1 代表全部contour
# 计算曲线长度，长度可以帮助我们得到近似边缘的拐角
# perimeter 周长
peri = cv2.arcLength(cnt, True) # curve closed 边缘 True
# print(peri)
# 近似拐角点 进行多边形逼近，得到多边形的角点
approx = cv2.approxPolyDP(cnt, 0.02 * peri, True) # 拐角点
# print(len(approx)) # 拐角点个数 len(approx)
if area > maxArea and len(approx) == 4:
biggest = approx
maxArea = area
cv2.drawContours(imgContour, biggest, -1, (255, 0, 0), 20)
# print(biggest)
return biggest
def reOrder(myPoints):
"""
对得到的四个角点，进行重排序，[[0, 0], [imgWidth, 0], [0, imgHeight], [imgWidth, imgHeight]]
:param myPoints: 四个角点
:return:
"""
myPoints = myPoints.reshape((4, 2))
myPointsNew = np.zeros((4, 1, 2), dtype=np.int32)
# 求和，得到最小和最大的
add = myPoints.sum(axis=1)
# print("add", add)
myPointsNew[0] = myPoints[np.argmin(add)]
myPointsNew[3] = myPoints[np.argmax(add)]
# 相减计算差值 ，后减前
diff = np.diff(myPoints, axis=1)
myPointsNew[1] = myPoints[np.argmin(diff)]
myPointsNew[2] = myPoints[np.argmax(diff)]
return myPointsNew
# print("myPointNew", myPointsNew)
def getWrap(img, biggest):
"""
利用得到的角点，进行透视变换
:param img:
:param biggest:
:return:
"""
biggest = reOrder(biggest)
# 简单理解，这地方返回的四个点，不是按照顺序返回的，需要重定位，知道那个在左右，那个在上下
pts1 = np.float32(biggest)
# [0,0]加起来是四个中最小的，[width,height]加起来是四个中最大的，[width,0]相减（后减前）是负数，[0,height]相减是正数
pts2 = np.float32([[0, 0], [imgWidth, 0], [0, imgHeight], [imgWidth, imgHeight]])
matrix = cv2.getPerspectiveTransform(pts1, pts2) # 使用getPerspectiveTransform()得到转换矩阵
imgOutput = cv2.warpPerspective(img, matrix, (imgWidth, imgHeight)) # 使用warpPerspective()进行透视变换
# 因为结果图还是存在边框多余，裁剪结果图，去掉四边10个像素，
imgCropped = imgOutput[20:imgOutput.shape[0] - 10, 20:imgOutput.shape[1] - 10]
imgCropped = cv2.resize(imgCropped, (imgWidth, imgHeight))
return imgCropped
# 图片的堆叠，合并
def stackImages(scale,imgArray):
rows = len(imgArray)
cols = len(imgArray[0])
# & 输出一个 rows * cols 的矩阵（imgArray）
# print(rows,cols)
# & 判断imgArray[0] 是不是一个list
rowsAvailable = isinstance(imgArray[0], list)
# & imgArray[][] 是什么意思呢？
# & imgArray[0][0]就是指[0,0]的那个图片（我们把图片集分为二维矩阵，第一行、第一列的那个就是第一个图片）
# & 而shape[1]就是width，shape[0]是height，shape[2]是
width = imgArray[0][0].shape[1]
height = imgArray[0][0].shape[0]
# & 例如，我们可以展示一下是什么含义
# cv2.imshow("img", imgArray[0][1])
if rowsAvailable:
for x in range (0, rows):
for y in range(0, cols):
# & 判断图像与后面那个图像的形状是否一致，若一致则进行等比例放缩；否则，先resize为一致，后进行放缩
if imgArray[x][y].shape[:2] == imgArray[0][0].shape [:2]:
imgArray[x][y] = cv2.resize(imgArray[x][y], (0, 0), None, scale, scale)
else:
imgArray[x][y] = cv2.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]), None, scale, scale)
# & 如果是灰度图，则变成RGB图像（为了弄成一样的图像）
if len(imgArray[x][y].shape) == 2: imgArray[x][y]= cv2.cvtColor( imgArray[x][y], cv2.COLOR_GRAY2BGR)
# & 设置零矩阵
imageBlank = np.zeros((height, width, 3), np.uint8)
hor = [imageBlank]*rows
hor_con = [imageBlank]*rows
for x in range(0, rows):
hor[x] = np.hstack(imgArray[x])
ver = np.vstack(hor)
# & 如果不是一组照片，则仅仅进行放缩 or 灰度转化为RGB
else:
for x in range(0, rows):
if imgArray[x].shape[:2] == imgArray[0].shape[:2]:
imgArray[x] = cv2.resize(imgArray[x], (0, 0), None, scale, scale)
else:
imgArray[x] = cv2.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None,scale, scale)
if len(imgArray[x].shape) == 2: imgArray[x] = cv2.cvtColor(imgArray[x], cv2.COLOR_GRAY2BGR)
hor= np.hstack(imgArray)
ver = hor
return ver
while True:
# success, img = cap.read()
img = cv2.imread("./Resources/1.jpg")
img = cv2.resize(img, (imgWidth, imgHeight))
imgContour = img.copy()
imgThres = preProcessing(img) # 预处理
biggest = getContours(imgThres) # 获取contours轮廓，四个角点
# print(biggest)
# 堆叠图片
# 如果摄像头么欸有检测到具有四个角的文档，则不会对图片进行透视变换处理
if biggest.size != 0:
# 透视变换
imgWrapped = getWrap(img, biggest)
imgArray = ([img, imgThres],
[imgContour, imgWrapped])
else:
imgArray = ([img, imgThres],
[img, img])
imgStack = stackImages(0.6, imgArray)
cv2.imshow("Result", imgStack)
if cv2.waitKey(1) & 0xFF == ord('q'):
break