如何调用矢量化滑动窗口切片上的函数？

Question

我正在尝试将滑动窗口搜索矢量化以进行目标检测。到目前为止，我已经能够使用numpy广播将我的主图像分割成窗口大小的片段，这些片段存储在如下所示的变量all_windows中。我已经验证了实际值是否匹配，所以到目前为止我还满意。在

下一部分是我遇到麻烦的地方。我想在调用patchCleanNPredict()函数时索引到all_windows数组中，这样我就可以以类似的矢量化格式将每个窗口传递到函数中。在

我试图创建一个名为new_indx的数组，它将包含2d数组中的切片索引，例如（[0,0]、[1,0]、[2,0]…），但是遇到了问题。在

我希望每个窗口都有一组置信值。下面的代码在Python3.5中工作。提前感谢您的任何帮助/建议。在

import numpy as np

def patchCleanNPredict(patch):
    # patch = cv2.resize()# shrink patches with opencv resize function
    patch = np.resize(patch.flatten(),(1,np.shape(patch.flatten())[0])) # flatten the patch
    print('patch: ',patch.shape) 
    # confidence = predict(patch) # fake function showing prediction intent
    return # confidence


window = (30,46)# window dimensions
strideY = 10
strideX = 10

img = np.random.randint(0,245,(640,480)) # image that is being sliced by the windows

indx = np.arange(0,img.shape[0]-window[1],strideY)[:,None]+np.arange(window[1])
vertical_windows = img[indx]
print(vertical_windows.shape) # returns (60,46,480)


vertical_windows = np.transpose(vertical_windows,(0,2,1))
indx = np.arange(0,vertical_windows.shape[1]-window[0],strideX)[:,None]+np.arange(window[0])
all_windows = vertical_windows[0:vertical_windows.shape[0],indx]
all_windows = np.transpose(all_windows,(1,0,3,2))

print(all_windows.shape) # returns (45,60,46,30)


data_patch_size = (int(window[0]/2),int(window[1]/2)) # size the windows will be shrunk to

single_patch = all_windows[0,0,:,:]
patchCleanNPredict(single_patch) # prints the flattened patch size (1,1380)

new_indx = (1,1) # should this be an array of indices? 
patchCleanNPredict(all_windows[new_indx,:,:]) ## this is where I'm having trouble

Answer 1

为了以矢量化的方式计算所有窗口上的一个函数，最后我不得不用np.转置让一切都能正确播出。下面的代码可以工作，并且有for循环来显示并确认图像窗口没有被乱码/混淆。他们将被删除/评论为全速运行。在

一个小小的免责声明：我认为在2D矩阵上滑动窗口必须有更干净的实现，但是由于我找不到任何一个例子，下面的例子可能会对其他人有所帮助。另外，一些频繁的重新排列和调整大小的问题可能可以通过对广播语法的更透彻的理解来清理。在

import numpy as np
import cv2


def Predict(flattened_patches):
    # taking the mean of the flattened windows and then returning the
    # index of the row (window) with the highest mean, a predicter would have the same syntax
    results = flattened_patches.mean(1) 
    max_index = results.argmax() 
    return results, max_index

##      image and sliding window setup             -
AR = 1.45 # choose an aspect ratio to maintain throughout scaling steps
win_h = 200 # window height
win_w = int(win_h/AR) # window width
window = (win_w,win_h)# window dimensions
strideY = 100
strideX = 100

data_patch_size = (30,46) # size the windows will be shrunk to for object detection

img = cv2.imread('picture6.png') # load an image to slide over

cv2.namedWindow('image',cv2.WINDOW_NORMAL) 
cv2.resizeWindow("image",int(img.shape[1]/2),int(img.shape[0]/2)) # shrink the image viewing window if you have large images

img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
##      end of, image and sliding window setup           

##      sliding window vectorization steps              
num_vert_windows = len(np.arange(0,img.shape[0]-window[1],strideY)) # number of vertical windows that will be created
indx = np.arange(0,img.shape[0]-window[1],strideY)[:,None]+np.arange(window[1]) # index that will be broadcasted across image
vertical_windows = img[indx] # array of windows win_h tall and the full width of the image

vertical_windows = np.transpose(vertical_windows,(0,2,1)) # transpose to prep for broadcasting
num_horz_windows = len(np.arange(0,vertical_windows.shape[1]-window[0],strideX)) # number of horizontal windows that will be created
indx = np.arange(0,vertical_windows.shape[1]-window[0],strideX)[:,None]+np.arange(window[0]) # index for broadcasting across vertical windows
all_windows = vertical_windows[0:vertical_windows.shape[0],indx] # array of all the windows
##      end of, sliding window vectorization             

##    - The below code rearranges and flattens the windows into a single matrix of pixels in columns and each window
##    - in a row which makes evaluating a function over every window in a vectorized manner easier

total_windows = num_vert_windows*num_horz_windows

all_windows = np.transpose(all_windows,(3,2,1,0)) # rearrange for resizing and intuitive indexing

print('all_windows shape as stored in 2d matrix:', all_windows.shape)
for i in range(all_windows.shape[2]): # display windows for visual confirmation
    for j in range(all_windows.shape[3]):
        cv2.imshow('image',all_windows[:,:,i,j])
        cv2.waitKey(100)

all_windows = np.resize(all_windows,(win_h,win_w,total_windows))
print('all_windows shape after folding into 1d vector:', all_windows.shape)
for i in range(all_windows.shape[2]): # display windows for visual confirmation
    cv2.imshow('image',all_windows[:,:,i])
    cv2.waitKey(100)

# shrinking all the windows down to the size needed for object detect predictions
small_windows = cv2.resize(all_windows[:,:,0:all_windows.shape[2]],data_patch_size,0,0,cv2.INTER_AREA)
print('all_windows shape after shrinking to evaluation size:',small_windows.shape)
for i in range(small_windows.shape[2]): # display windows for vis. conf.
    cv2.imshow('image',small_windows[:,:,i])
    cv2.waitKey(100)

# flattening and rearranging the window data so that the pixels are in columns and each window is a row
flat_windows = np.resize(small_windows,(data_patch_size[0]*data_patch_size[1],total_windows))
flat_windows = np.transpose(flat_windows)
print('shape of the window data to send to the predicter:',np.shape(flat_windows))

results, max_index = Predict(flat_windows) # get predictions on all the windows