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ValueError：预期 2D 数组，得到 1D 数组：array=[0.05760078 0.02291208 0.01819267 ... 0.02739229 0.01199168 0.00890131]。如果您的数据具有单个特征，则使用 array.reshape(-1, 1) 重塑您的数据，如果它包含单个样本，则使用 array.reshape(1, -1) 。

任何快速的解决方案都可以帮助我很多。这是代码：

 # Define a single function that can extract features using hog sub-sampling and make predictions
    def find_cars(img, ystart, ystop, scale, cspace, hog_channel, svc, X_scaler, orient, 
                  pix_per_cell, cell_per_block, spatial_size, hist_bins, show_all_rectangles=False):

        # array of rectangles where cars were detected
        rectangles = []

        img = img.astype(np.float32)/255

        img_tosearch = img[ystart:ystop,:,:]

        # apply color conversion if other than 'RGB'
        if cspace != 'RGB':
            if cspace == 'HSV':
                ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2HSV)
            elif cspace == 'LUV':
                ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2LUV)
            elif cspace == 'HLS':
                ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2HLS)
            elif cspace == 'YUV':
                ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2YUV)
            elif cspace == 'YCrCb':
                ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2YCrCb)
        else: ctrans_tosearch = np.copy(image)   

        # rescale image if other than 1.0 scale
        if scale != 1:
            imshape = ctrans_tosearch.shape
            ctrans_tosearch = cv2.resize(ctrans_tosearch, (np.int(imshape[1]/scale), np.int(imshape[0]/scale)))

        # select colorspace channel for HOG 
        if hog_channel == 'ALL':
            ch1 = ctrans_tosearch[:,:,0]
            ch2 = ctrans_tosearch[:,:,1]
            ch3 = ctrans_tosearch[:,:,2]
        else: 
            ch1 = ctrans_tosearch[:,:,hog_channel]

        # Define blocks and steps as above
        nxblocks = (ch1.shape[1] // pix_per_cell)+1  #-1
        nyblocks = (ch1.shape[0] // pix_per_cell)+1  #-1 
        nfeat_per_block = orient*cell_per_block**2
        # 64 was the orginal sampling rate, with 8 cells and 8 pix per cell
        window = 64
        nblocks_per_window = (window // pix_per_cell)-1 
        cells_per_step = 2  # Instead of overlap, define how many cells to step
        nxsteps = (nxblocks - nblocks_per_window) // cells_per_step
        nysteps = (nyblocks - nblocks_per_window) // cells_per_step

        # Compute individual channel HOG features for the entire image
        hog1 = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, feature_vec=False)   
        if hog_channel == 'ALL':
            hog2 = get_hog_features(ch2, orient, pix_per_cell, cell_per_block, feature_vec=False)
            hog3 = get_hog_features(ch3, orient, pix_per_cell, cell_per_block, feature_vec=False)

        for xb in range(nxsteps):
            for yb in range(nysteps):
                ypos = yb*cells_per_step
                xpos = xb*cells_per_step
                # Extract HOG for this patch
                hog_feat1 = hog1[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
                if hog_channel == 'ALL':
                    hog_feat2 = hog2[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel() 
                    hog_feat3 = hog3[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel() 
                    hog_features = np.hstack((hog_feat1, hog_feat2, hog_feat3))
                else:
                    hog_features = hog_feat1

                xleft = xpos*pix_per_cell
                ytop = ypos*pix_per_cell

                ################ ONLY FOR BIN_SPATIAL AND COLOR_HIST ################

                # Extract the image patch
                #subimg = cv2.resize(ctrans_tosearch[ytop:ytop+window, xleft:xleft+window], (64,64))

                # Get color features
                #spatial_features = bin_spatial(subimg, size=spatial_size)
                #hist_features = color_hist(subimg, nbins=hist_bins)

                # Scale features and make a prediction
                #test_features = X_scaler.transform(np.hstack((spatial_features, hist_features, hog_features)).reshape(1, -1))    
                #test_features = X_scaler.transform(np.hstack((shape_feat, hist_feat)).reshape(1, -1))    
                #test_prediction = svc.predict(test_features)

                ######################################################################

                test_prediction = svc.predict(hog_features)

                if test_prediction == 1 or show_all_rectangles:
                    xbox_left = np.int(xleft*scale)
                    ytop_draw = np.int(ytop*scale)
                    win_draw = np.int(window*scale)
                    rectangles.append(((xbox_left, ytop_draw+ystart),(xbox_left+win_draw,ytop_draw+win_draw+ystart)))

        return rectangles

    print('...')

#------------Now calling find_cars()

        test_img = mpimg.imread('./test_images/test1.jpg')

        ystart = 400
        ystop = 656
        scale = 1.5
        colorspace = 'YUV' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
        orient = 11
        pix_per_cell = 16
        cell_per_block = 2
        hog_channel = 'ALL' # Can be 0, 1, 2, or "ALL"

        rectangles = find_cars(test_img, ystart, ystop, scale, colorspace, hog_channel, svc, None, orient, pix_per_cell, cell_per_block, None, None)

        print(len(rectangles), 'rectangles found in image')
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1 回答 1

0

您确定不应该向函数传递值列表吗？

find_cars([test_img, ystart, ystop, scale, colorspace, hog_channel, svc, None, orient, pix_per_cell, cell_per_block, None, None])
于 2019-03-06T16:20:18.560 回答