caffe_functions.py 文件源码

def load_minibatch(input_list, color, labels, start,num):
    # Enforce maximum on start
    start = max(0,start)

    # Enforce minimum on end
    end = start + num
    end = min(len(input_list), end)

    # Isolate files
    files = input_list[start:end]

    images = []
    for file in files:
        img = caffe.io.load_image(file, color)

        # Handle incorrect image dims for uncropped images
        # TODO: Get uncropped images to import correctly
        if img.shape[0] == 3 or img.shape[0] == 1:
            img = np.swapaxes(np.swapaxes(img, 0, 1), 1, 2)

        # BUG FIX: Is this ok?
        # color=True gets the correct desired dimension of WxHx3
        # But color=False gets images of WxHx1. Need WxHx3 or will get "Index out of bounds" exception
        # Fix by concatenating three copies of the image
        if img.shape[2] == 1:
            img = cv.merge([img,img,img])

        # Add image array to batch
        images.append(img)

    labelsReduced = labels[start:end]
    return images, labelsReduced

# Classify all images in a list of image file names
# No return value, but can display outputs if desired