Python MemoryError:无法分配内存。形状为（3000、4000、3）且数据类型为float32的数组的MiB

我在运行一个包含10个正面和10个负面训练数据的模型时遇到了这个问题

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MemoryError:无法分配内存。形状为（3000、4000、3）且数据类型为float32的数组的MiB

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我的代码是：

# Define a single function that can extract features using hog sub-sampling and make predictions
def find_cars(img, ystart, ystop, scale, svc,orient, 
              pix_per_cell, cell_per_block,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'
    ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2YUV)
    
    # rescale image if other than 1.0 scale
    imshape = ctrans_tosearch.shape
    ctrans_tosearch = cv2.resize(ctrans_tosearch, (np.int(imshape[1]/scale), np.int(imshape[0]/scale)))
    
    ch1 = ctrans_tosearch[:,:,0]
    ch2 = ctrans_tosearch[:,:,1]
    ch3 = ctrans_tosearch[:,:,2]
    
    # 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)   
    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()
            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))
            xleft = xpos*pix_per_cell
            ytop = ypos*pix_per_cell
                                  
            test_prediction = svc.predict(hog_features.reshape(1,-1))
            
            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

test_img = mpimg.imread('C:/Users/shrey/Desktop/Mod1-IITR/DJI_0314.JPG')

ystart = 20
ystop = 50
scale = 1
orient = 11
pix_per_cell = 16
cell_per_block = 2

rectangles = find_cars(test_img, ystart, ystop, scale, svc,orient, pix_per_cell, cell_per_block)

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

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我正在使用Windows-Jupyter笔记本电脑。如何更正此代码问题？

错误是，它无法分配内存，因为内存不足

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然而，您的系统上的可用内存可能比137MB多得多，jupyter只是限制了系统中可以使用的内存量，因为在您开始运行代码之前，它必须保留一些内存。有关如何在jupyter中增加内存限制的信息，请参见。您在哪里感到困惑？错误信息似乎很清楚：您正在尝试分配137Mb的数据，但您没有可用的数据。虽然此链接可以回答问题，但最好在此处包含答案的基本部分，并提供链接供参考。如果链接页面发生更改，仅链接的答案可能无效。-@SimasJoneliunas我真的看不出问题所在，链接不是解决方案，它只是猜测问题的原因。这不是一个“仅链接的答案”。据我们所知，jupyter内存不足，我们没有其他信息，也没有试图解决问题。我们无法修复未描述的问题。很可能这个相关的问题恰好是根本原因。如果是，则整个问题都是重复的，您不应投票删除答案，而应投票赞成关闭该问题。