66 lines
2.3 KiB
Python
66 lines
2.3 KiB
Python
import cv2
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import numpy as np
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from Image_Process.Image_Mask_Ground_Truth_Processing import XMLAnnotationProcessor
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def test_bounding_box_processing_with_mock_data():
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# 初始化XML處理器
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processor = XMLAnnotationProcessor()
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# 創建一個測試圖像 (300x300 彩色圖像)
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test_image = np.ones((300, 300, 3), dtype=np.uint8) * 128 # 灰色背景
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# 在圖像中央創建一個彩色區域
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test_image[100:200, 100:200, 0] = 255 # 紅色通道
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test_image[120:180, 120:180, 1] = 255 # 綠色通道
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test_image[140:160, 140:160, 2] = 255 # 藍色通道
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# 創建模擬的bounding box數據
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mock_bounding_boxes = [
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{
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'name': 'test_object',
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'xmin': 50,
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'ymin': 50,
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'xmax': 150,
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'ymax': 150
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},
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{
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'name': 'test_object2',
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'xmin': 200,
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'ymin': 200,
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'xmax': 250,
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'ymax': 250
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}
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]
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# 調用draw_bounding_boxes方法
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result_image = processor.draw_bounding_boxes(test_image, mock_bounding_boxes)
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# 顯示處理結果
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print(f"原始圖像形狀: {test_image.shape}")
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print(f"結果圖像形狀: {result_image.shape}")
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# 檢查圖像是否有非黑色區域（bounding box內的原圖）
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non_black_pixels = np.sum(result_image > 0)
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print(f"非黑色像素數量: {non_black_pixels}")
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# 檢查第一個bounding box區域是否保留了原圖
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box1 = result_image[50:150, 50:150]
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box1_matches = np.array_equal(box1, test_image[50:150, 50:150])
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print(f"第一個bounding box區域是否與原圖相同: {box1_matches}")
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# 檢查框外區域是否為黑色
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outside_box = result_image[0:40, 0:40] # 選擇一個框外區域
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is_black = np.sum(outside_box) == 0
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print(f"框外區域是否為黑色: {is_black}")
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if non_black_pixels > 0 and box1_matches and is_black:
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print("測試成功: bounding box內保持原圖，框外變為黑色")
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else:
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print("測試失敗")
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# 保存結果圖像以便查看
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cv2.imwrite("test_bounding_box_result.png", result_image)
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print("已保存結果圖像: test_bounding_box_result.png")
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if __name__ == "__main__":
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test_bounding_box_processing_with_mock_data() |