版权声明:本文为博主原创文章,未经博主允许不得转载。 https://blog.csdn.net/yunxinan/article/details/83214177
#第一部分
import numpy as np
def rad(x):
return x * np.pi / 180
img = cv2.imread("C:/Users/Administrator/Desktop/1010test/21.jpg")
#cv2.imshow("original", img)
img = cv2.copyMakeBorder(img, 200, 200, 200, 200, cv2.BORDER_CONSTANT, 0)
w, h = img.shape[0:2]
anglex =-10
angley = 0
anglez = 0
fov = 42
while 1:
# 镜头与图像间的距离,21为半可视角,算z的距离是为了保证在此可视角度下恰好显示整幅图像
z = np.sqrt(w ** 2 + h ** 2) / 2 / np.tan(rad(fov / 2))
# 齐次变换矩阵
rx = np.array([[1, 0, 0, 0],
[0, np.cos(rad(anglex)), -np.sin(rad(anglex)), 0],
[0, -np.sin(rad(anglex)), np.cos(rad(anglex)), 0, ],
[0, 0, 0, 1]], np.float32)
ry = np.array([[np.cos(rad(angley)), 0, np.sin(rad(angley)), 0],
[0, 1, 0, 0],
[-np.sin(rad(angley)), 0, np.cos(rad(angley)), 0, ],
[0, 0, 0, 1]], np.float32)
rz = np.array([[np.cos(rad(anglez)), np.sin(rad(anglez)), 0, 0],
[-np.sin(rad(anglez)), np.cos(rad(anglez)), 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]], np.float32)
r = rx.dot(ry).dot(rz)
# 四对点的生成
pcenter = np.array([h / 2, w / 2, 0, 0], np.float32)
p1 = np.array([0, 0, 0, 0], np.float32) - pcenter
p2 = np.array([w, 0, 0, 0], np.float32) - pcenter
p3 = np.array([0, h, 0, 0], np.float32) - pcenter
p4 = np.array([w, h, 0, 0], np.float32) - pcenter
dst1 = r.dot(p1)
dst2 = r.dot(p2)
dst3 = r.dot(p3)
dst4 = r.dot(p4)
list_dst = [dst1, dst2, dst3, dst4]
org = np.array([[0, 0],
[w, 0],
[0, h],
[w, h]], np.float32)
dst = np.zeros((4, 2), np.float32)
# 投影至成像平面
for i in range(4):
dst[i, 0] = list_dst[i][0] * z / (z - list_dst[i][2]) + pcenter[0]
dst[i, 1] = list_dst[i][1] * z / (z - list_dst[i][2]) + pcenter[1]
warpR = cv2.getPerspectiveTransform(org, dst)
result = cv2.warpPerspective(img, warpR, (h, w))
cv2.imshow("result", result)
c = cv2.waitKey(0)
# anglex += 3 #auto rotate
# anglez += 1 #auto rotate
# angley += 2 #auto rotate
# 键盘控制
if 27 == c: # Esc quit
break;
if c == ord('w'):
anglex += 1
if c == ord('s'):
anglex -= 1
if c == ord('a'):
angley += 1
# dx=0
if c == ord('d'):
angley -= 1
if c == ord('u'):
anglez += 1
if c == ord('p'):
anglez -= 1
if c == ord('t'):
fov += 1
if c == ord('r'):
fov -= 1
if c == ord(' '):
anglex = angley = anglez = 0
if c == ord('q'):
print("==============")
print('旋转矩阵:\n', r)
print("angle alpha: ", anglex, 'angle beta: ', angley, "dz: ", anglez, ": ", z)
cv2.waitKey(0)
cv2.destroyAllWindows()
#第二部分
import cv2
import numpy as np
def get_image(path):
#获取图片
img=cv2.imread(path)
gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
return img, gray
def Gaussian_Blur(gray):
# 高斯去噪
blurred = cv2.GaussianBlur(gray, (9, 9),0)
return blurred
def Sobel_gradient(blurred):
# 索比尔算子来计算x、y方向梯度
gradX = cv2.Sobel(blurred, ddepth=cv2.CV_32F, dx=1, dy=0)
gradY = cv2.Sobel(blurred, ddepth=cv2.CV_32F, dx=0, dy=1)
gradient = cv2.subtract(gradX, gradY)
gradient = cv2.convertScaleAbs(gradient)
return gradX, gradY, gradient
def Thresh_and_blur(gradient):
blurred = cv2.GaussianBlur(gradient, (9, 9),0)
(_, thresh) = cv2.threshold(blurred, 90, 255, cv2.THRESH_BINARY)
return thresh
def image_morphology(thresh):
# 建立一个椭圆核函数
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (25, 25))
# 执行图像形态学, 细节直接查文档,很简单
closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
closed = cv2.erode(closed, None, iterations=4)
closed = cv2.dilate(closed, None, iterations=4)
return closed
def findcnts_and_box_point(closed):
# 这里opencv3返回的是三个参数
(_, cnts, _) = cv2.findContours(closed.copy(),
cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
c = sorted(cnts, key=cv2.contourArea, reverse=True)[0]
# compute the rotated bounding box of the largest contour
rect = cv2.minAreaRect(c)
box = np.int0(cv2.boxPoints(rect))
return box
def drawcnts_and_cut(original_img, box):
# 因为这个函数有极强的破坏性,所有需要在img.copy()上画
# draw a bounding box arounded the detected barcode and display the image
draw_img = cv2.drawContours(original_img.copy(), [box], -1, (0, 0, 255), 3)
Xs = [i[0] for i in box]
Ys = [i[1] for i in box]
x1 = min(Xs)
x2 = max(Xs)
y1 = min(Ys)
y2 = max(Ys)
hight = y2 - y1
width = x2 - x1
crop_img = original_img[y1:y1+hight, x1:x1+width]
return draw_img, crop_img
def walk():
img_path = r'C:/Users/Administrator/Desktop/1010test/3.png'
save_path = r'C:/Users/Administrator/Desktop/1010test/3_save.png'
original_img, gray = get_image(img_path)
blurred = Gaussian_Blur(gray)
gradX, gradY, gradient = Sobel_gradient(blurred)
thresh = Thresh_and_blur(gradient)
closed = image_morphology(thresh)
box = findcnts_and_box_point(closed)
draw_img, crop_img = drawcnts_and_cut(original_img,box)
# 暴力一点,把它们都显示出来看看
cv2.imshow('original_img', original_img)
cv2.imshow('blurred', blurred)
cv2.imshow('gradX', gradX)
cv2.imshow('gradY', gradY)
cv2.imshow('final', gradient)
cv2.imshow('thresh', thresh)
cv2.imshow('closed', closed)
cv2.imshow('draw_img', draw_img)
cv2.imshow('crop_img', crop_img)
cv2.waitKey(20171219)
cv2.imwrite(save_path, crop_img)
walk()
#第三部分
from PIL import Image
def ResizeImage(filein,fileout,width,height,type):
img = Image.open(filein)
out = img.resize((width,height),Image.ANTIALIAS)
out.save(fileout,type)
if __name__ =="__main__":
filein = r'C:/Users/Administrator/Desktop/1010test/15_dst.jpg'
fileout = r'C:/Users/Administrator/Desktop/1010test/15_new.jpg'
width = 440
height = 300
type = 'png'
ResizeImage(filein, fileout, width, height, type)
#第四部分
from PIL import Image
# 1
im = Image.open('D:/STRPIC/6.jpg')
img_size = im.size
print("证号{}".format(img_size))
left = 120
upper = 16
right = 520
lower = 57
region = im.crop((left,upper,right,lower))
region.save("C:/Users/Administrator/Desktop/test/Crop_t1.jpg")
# 2
img_size = im.size
print("姓名{}".format(img_size))
left = 180
upper = 75
right = 485
lower = 115
region = im.crop((left,upper,right,lower))
region.save("C:/Users/Administrator/Desktop/test/Crop_t2.jpg")
# 3
img_size = im.size
print("性别{}".format(img_size))
left = 30
upper = 114
right = 600
lower = 150
region = im.crop((left,upper,right,lower))
region.save("C:/Users/Administrator/Desktop/test/Crop_t3.jpg")
# 4
img_size = im.size
print("住址{}".format(img_size))
left = 30
upper = 165
right = 600
lower = 200
region = im.crop((left,upper,right,lower))
region.save("C:/Users/Administrator/Desktop/test/Crop_t4.jpg")
# 5
img_size = im.size
print("出生日期{}".format(img_size))
left = 162
upper = 245
right = 450
lower = 290
region = im.crop((left,upper,right,lower))
region.save("C:/Users/Administrator/Desktop/test/Crop_t5.jpg")
# 6
img_size = im.size
print("初次领证日期{}".format(img_size))
left = 162
upper = 295
right = 450
lower = 340
region = im.crop((left,upper,right,lower))
region.save("C:/Users/Administrator/Desktop/test/Crop_t6.jpg")
# 7
img_size = im.size
print("准驾车型{}".format(img_size))
left = 162
upper = 345
right = 450
lower = 400
region = im.crop((left,upper,right,lower))
region.save("C:/Users/Administrator/Desktop/test/Crop_t7.jpg")
# 8
img_size = im.size
print("有效日期{}".format(img_size))
left = 30
upper = 390
right = 450
lower = 435
region = im.crop((left,upper,right,lower))
region.save("C:/Users/Administrator/Desktop/test/Crop_t8.jpg")
master R-cnn
http://www.sohu.com/a/228409487_129720
http://www.ijiandao.com/2b/baijia/95468.html
https://blog.csdn.net/u011974639/article/details/79806893
https://blog.csdn.net/weixin_42615068/article/details/82466454
https://blog.csdn.net/amusi1994/article/details/82356417
https://blog.csdn.net/hw5226349/article/details/81906882
https://blog.csdn.net/u011974639/article/details/79595179
http://www.sohu.com/a/245563314_651893
非常重要
https://blog.csdn.net/xiao__run/article/details/80393016?utm_source=blogxgwz3
https://github.com/sfzhang15/RefineDet
https://github.com/msracver/Relation-Networks-for-Object-Detection
https://github.com/msracver/Relation-Networks-for-Object-Detection