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https://github.com/nizhangdaye/TLD_Code.git
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62 lines
3.7 KiB
Python
62 lines
3.7 KiB
Python
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import cv2
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import os
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def get_num_list(folder_path):
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"""
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读取文件夹中的所有.png格式图片，并进行二值化处理，并返回包含二值化图片的列表。
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"""
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binary_images = []
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# 遍历文件夹中的所有文件
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for filename in sorted(os.listdir(folder_path)):
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# 检查文件是否是.png格式
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if filename.endswith('.png'):
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# 构建图片的完整路径
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img_path = os.path.join(folder_path, filename)
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# 使用OpenCV读取图片为灰度图
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img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
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# 检查图片是否成功读取
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if img is not None:
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# 使用阈值进行二值化处理
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# 假设我们使用127作为阈值，但这可以根据你的需求进行调整
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_, binary_img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
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# 将二值化后的图片添加到列表中
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binary_images.append(binary_img.tolist())
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return binary_images
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num_list = [
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[[0, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255],
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[255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255],
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[255, 255, 0, 0, 255, 255], [0, 255, 255, 255, 255, 0]],
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[[0, 0, 255, 255], [255, 255, 255, 255], [0, 0, 255, 255], [0, 0, 255, 255], [0, 0, 255, 255], [0, 0, 255, 255],
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[0, 0, 255, 255], [0, 0, 255, 255], [0, 0, 255, 255], [0, 0, 255, 255]],
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[[0, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255],
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[0, 0, 0, 255, 255, 0], [0, 0, 255, 255, 0, 0], [0, 255, 255, 0, 0, 0], [255, 255, 0, 0, 0, 0],
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[255, 255, 0, 0, 0, 0], [255, 255, 255, 255, 255, 255]],
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[[0, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255],
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[0, 0, 255, 255, 255, 0], [0, 0, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255],
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[255, 255, 0, 0, 255, 255], [0, 255, 255, 255, 255, 0]],
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[[0, 0, 0, 0, 255, 255], [0, 0, 0, 255, 255, 255], [0, 0, 255, 255, 255, 255], [0, 0, 255, 255, 255, 255],
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[0, 255, 255, 0, 255, 255], [0, 255, 255, 0, 255, 255], [255, 255, 0, 0, 255, 255], [255, 255, 255, 255, 255, 255],
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[0, 0, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255]],
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[[255, 255, 255, 255, 255, 255], [255, 255, 0, 0, 0, 0], [255, 255, 0, 0, 0, 0], [255, 255, 0, 0, 0, 0],
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[255, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255],
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[255, 255, 0, 0, 255, 255], [0, 255, 255, 255, 255, 0]],
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[[0, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 0, 0], [255, 255, 0, 0, 0, 0],
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[255, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255],
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[255, 255, 0, 0, 255, 255], [0, 255, 255, 255, 255, 0]],
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[[255, 255, 255, 255, 255, 255], [0, 0, 0, 0, 255, 255], [0, 0, 0, 255, 255, 0], [0, 0, 0, 255, 255, 0],
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[0, 0, 255, 255, 0, 0], [0, 0, 255, 255, 0, 0], [0, 0, 255, 255, 0, 0], [0, 255, 255, 0, 0, 0],
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[0, 255, 255, 0, 0, 0], [0, 255, 255, 0, 0, 0]],
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[[0, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255],
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[0, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255],
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[255, 255, 0, 0, 255, 255], [0, 255, 255, 255, 255, 0]],
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[[0, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255],
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[255, 255, 0, 0, 255, 255], [0, 255, 255, 255, 255, 255], [0, 0, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255],
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[255, 255, 0, 0, 255, 255], [0, 255, 255, 255, 255, 0]]]
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