圖像排序張量流

Question

我正在使用張量流運行滑動窗口的代碼。

這是針對Theano的，這就是為什么我收到圖像尺寸排序錯誤的原因。

誰能告訴我如何針對張量流進行修改？

錯誤：

ValueError：檢查時出錯：預期input_2具有形狀（None，224、224、3）但具有形狀（1、3、224、224）的數組

image=load_img('\e.jpg')
image= np.array(image).reshape((3264,5896,3))
image = image.astype('float32')
image /= 255
plt.imshow(image)
print(image.shape)


#%%SLIDING WINDOW
 ''
def find_a_object(image, step=224, window_sizes=[224]):
    boxCrack = 0  
    locations = []
    for win_size in window_sizes:
        #top = Y, left = X
        for Y in range(0, image.shape[0] - win_size + 1, step):
            for X in range(0, image.shape[1] - win_size + 1, step):
                # compute the (top, left, bottom, right) of the bounding box
                box = (Y, X, Y + win_size, X + win_size)

                #crop original image 
                cropped_img = image[box[0]:box[2], box[1]:box[3]]
                #reshape cropped image by window
                cropped_img = np.array(cropped_img).reshape((1,3,224,224))


                boxCrack = predict_function(cropped_img)

                if boxCrack ==0:
                    #print('box classified as crack') 
                    #save location of it                 
                    locations.append(box)
                    print("found")
        return locations
#%%FUNCTIONS
def predict_function(x):
    result =  model.predict_classes(x)
    if result==0:
        return 0
    else:
        return 1   
##SHOW CROPPED IMAGE
#def show_image(im):
#    plt.imshow(im.reshape((100,100,3)))
)

#DRAW BOXES FROM LOCATIONS when classified
def draw_boxes(image, locations):
    fix,ax = plt.subplots(1)
    ax.imshow(image)       
    for l in locations:
        print (l)
        rectR = patches.Rectangle((l[1],l[0]),224,224,linewidth=1,edgecolor='R',facecolor='none'
        ax.add_patch(rectR)


#%%get locations from image
locations = find_a_object(image)

draw_boxes(image,locations)

Answer 1

好吧，錯誤消息非常清楚，TF中的順序應該是(None, dim_x, dim,_y, n_channels) 。 我認為按如下所示放置圖像數組應該可以解決問題。

cropped_img = image[box[0]:box[2], box[1]:box[3]]
#reshape cropped image by window
cropped_img = np.array(cropped_img).reshape((1,3,224,224))
cropped_img = cropped_img.transpose(0,2,3,1)