Python3 skimage - 计算图片数量

如何解决Python3 skimage - 计算图片数量

我有一个图像，我正在使用 skimage 尝试检测：

页面中实际有多少张图片 - 我希望它“计数”5
找到每个图像的角 - 所以如果它比我们的 maxCorners 数 5 多 5*4=20
在每个角之间绘制直线并“遮罩”5 个图像中的每一个

现在我得到的只是正在读取的图像，填充漏洞，仅此而已 - 其他方面的指导？

from scipy import ndimage as nd
import imageio
from skimage import io,filters
from scipy import ndimage
import matplotlib.pyplot as plt

filename = "C:\\Users\\Tony\\Pictures\\img807.tif"

im = io.imread(filename,as_gray=True)
val = filters.threshold_otsu(im)
drops = ndimage.binary_fill_holes(im < val)
plt.imshow(drops,cmap='gray')
plt.show()

我试过查看这些资源：

特别是最后一个角点检测...

这是原图：

真实来源链接（12 小时）：https://u.pcloud.link/publink/show?code=XZDONYXZVkUNFT2qcEFk4nFYYnx7d8swzaD7

解决方法

这个Answer是解决这个问题的关键。

坐标：

[[(38,11),(251,364)],[(254,62),(592,266)],312),

(592,518)],[(46,456),(247,797)],[(346,557),(526,797)]]

import numpy as np
import matplotlib.pyplot as plt
import cv2
import itertools

#====================================================
img = cv2.imread('input.jpg',0)
blur = cv2.blur(img,(3,3))

blur[blur>225] = 0

sobelx = cv2.Sobel(blur,cv2.CV_64F,1,ksize=5)
sobely = cv2.Sobel(blur,ksize=5)
sobel = np.sqrt( sobelx**2 + sobely**2)

sobel = (255 * sobel)/(sobel.max() - sobel.min())
sobel = sobel.astype(np.uint8)
sobel[sobel<20] = 0
sobel[sobel>20] = 255
#====================================================

_,thresh = cv2.threshold(blur,127,255,1)
thresh = thresh + sobel

median = cv2.medianBlur(thresh,3)

gray_scale = median.copy()

image = np.stack([img,img,img],axis=2)

img_bin = cv2.Canny(gray_scale,50,110)
dil_kernel = np.ones((3,3),np.uint8)
img_bin=cv2.dilate(img_bin,dil_kernel,iterations=1)

line_min_width = 7

kernal_h = np.ones((2,line_min_width),np.uint8)
img_bin_h = cv2.morphologyEx(img_bin,cv2.MORPH_OPEN,kernal_h)

kernal_v = np.ones((line_min_width,1),np.uint8)
img_bin_v = cv2.morphologyEx(img_bin,kernal_v)

img_bin_final=img_bin_h|img_bin_v
final_kernel = np.ones((3,np.uint8)
img_bin_final=cv2.dilate(img_bin_final,final_kernel,iterations=1)

_,_,stats,_ = cv2.connectedComponentsWithStats(~img_bin_final,connectivity=8,ltype=cv2.CV_32S)

coords = []
### 1 and 0 and the background and residue connected components whihc we do not require
for x,y,w,h,area in stats[2:]:
    if area>15000:
        coords.append([(x,y),(x+w,y+h)])

def bb_intersection(coords,boxA,boxB):
    # determine the (x,y)-coordinates of the intersection rectangle
    xA = max(boxA[0][0],boxB[0][0])
    yA = max(boxA[0][1],boxB[0][1])
    xB = min(boxA[1][0],boxB[1][0])
    yB = min(boxA[1][1],boxB[1][1])
    # compute the area of intersection rectangle
    interArea = max(0,xB - xA + 1) * max(0,yB - yA + 1)
    # compute the area of both rectangles
    boxAArea = (boxA[1][0] - boxA[0][0] + 1) * (boxA[1][1] - boxA[0][1] + 1)
    boxBArea = (boxB[1][0] - boxB[0][0] + 1) * (boxB[1][1] - boxB[0][1] + 1)

    if(interArea == boxAArea):
        coords.remove(boxA)
    elif(interArea == boxBArea):
        coords.remove(boxB)
#
for boxa,boxb in itertools.combinations(coords,2):
    bb_intersection(coords,boxa,boxb)

for coord in coords:
    cv2.rectangle(image,coord[0],coord[1],(0,0),1)

print(coords)

plt.imshow(image)
plt.title("There are {} images".format(len(coords)))
plt.axis('off')
plt.show()

编辑：

这个答案不是通用的解决方案，必须相应地调整参数，对于原始图像更改此块，代码将完美运行：

img = cv2.imread('input.tif',0)

img = cv2.resize(img,(605,830))

blur = img.copy()

blur[blur>225] = 0

sobelx = cv2.Sobel(blur,ksize=3)
sobely = cv2.Sobel(blur,ksize=3)
sobel = np.sqrt( sobelx**2 + sobely**2)

sobel = (255 * sobel)/(sobel.max() - sobel.min())
sobel = sobel.astype(np.uint8)
sobel[sobel<40] = 0
sobel[sobel>40] = 255

坐标：

[[(31,16),(240,[(253,56),(600,265)],309),

(605,520)],[(40,(248,803)],[(347,(534,803)]]

Python3 skimage - 计算图片数量

如何解决Python3 skimage - 计算图片数量

解决方法

编辑：

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