lesson_functions.py 文件源码

def average_boxes(hot_windows, old_boxes, 
                  image_shape):
    hot_boxes = initialize_center_box(hot_windows)
    new_boxes = add_center_box(hot_boxes, old_boxes)
    # print('new_boxes', new_boxes)
    filtered_boxes = []
    for new_box in new_boxes:
        # Draw boxes only if the confidence level is above 1
        if new_box[-1] > 2:
            filtered_boxes.append(new_box)
    new_windows = []
    # convert center-width-height to lefttop-rightbottom format
    for filtered_box in filtered_boxes:
        new_center, new_width, new_height,new_move, new_prob = filtered_box
        new_windows.append(((int(new_center[0]-new_width), int(new_center[1]-new_height)), 
            (int(new_center[0]+new_width), int(new_center[1]+new_height))))
    # Create a heatmap
    heatmap = create_heatmap(new_windows, image_shape)
    # Check if there is any overlap of windows
    if np.unique(heatmap)[-1] >= 2:
        labels = ndi.label(heatmap)[0]
        heatmap_2 = np.zeros_like(heatmap)
        heatmap_2[heatmap>=2] = 1
        labels_2 = ndi.label(heatmap_2)
        array_2 = np.argwhere(labels_2[0])
        for car_number in range(1, labels_2[1]+1):
            # Find pixels with each car_number label value
            nonzero = (labels_2[0] == car_number).nonzero()
            # Identify x and y values of those pixels
            num = labels[nonzero[0][0], nonzero[1][0]]
            labels[labels == num] = 0
        heatmap = labels + heatmap_2
        new_windows = find_windows_from_heatmap(heatmap)

    # return the boxes with high confidence and new set of probability array
    return new_windows, new_boxes