181 lines
6.1 KiB
Python
181 lines
6.1 KiB
Python
import cv2
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import numpy as np
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import os
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from pathlib import Path
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def preprocess_image(image):
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"""
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预处理图像：转换为HSV色彩空间，检测白色区域
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"""
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# 转换为HSV色彩空间
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hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
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# 定义白色的HSV范围 - 放宽范围
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# 白色在HSV中：饱和度低，明度高
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# 原来的范围：lower_white = np.array([0, 0, 200]), upper_white = np.array([180, 30, 255])
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# 放宽后的范围：
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lower_white = np.array([0, 0, 150]) # 降低明度下限，从200降到150
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upper_white = np.array([180, 60, 255]) # 提高饱和度上限，从30提高到60
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# 创建白色掩码
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white_mask = cv2.inRange(hsv, lower_white, upper_white)
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# 形态学操作：开运算去除小噪点，闭运算填充小孔
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kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
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cleaned = cv2.morphologyEx(white_mask, cv2.MORPH_OPEN, kernel)
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cleaned = cv2.morphologyEx(cleaned, cv2.MORPH_CLOSE, kernel)
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return cleaned
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def find_white_rectangles(binary_image):
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"""
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查找白色矩形区域
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"""
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contours, _ = cv2.findContours(
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binary_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
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)
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# 筛选合适的矩形轮廓
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valid_rectangles = []
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for contour in contours:
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area = cv2.contourArea(contour)
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if area > 200: # 过滤太小的区域，从500降低到200
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# 计算轮廓的边界矩形
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x, y, w, h = cv2.boundingRect(contour)
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# 计算轮廓的近似多边形
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epsilon = 0.02 * cv2.arcLength(contour, True)
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approx = cv2.approxPolyDP(contour, epsilon, True)
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# 检查是否为四边形（矩形）
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if len(approx) == 4:
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# 计算宽高比
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aspect_ratio = w / h
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# 矩形应该有合理的宽高比（不是太细长）
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if 0.3 < aspect_ratio < 3.0:
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valid_rectangles.append((contour, (x, y, w, h), approx))
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return valid_rectangles
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def analyze_white_region(roi, original_roi):
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"""
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分析白色区域的特征
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"""
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# 计算白色像素比例
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total_pixels = roi.shape[0] * roi.shape[1]
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white_pixels = np.sum(roi == 255)
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white_ratio = white_pixels / total_pixels
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# 计算区域的形状特征
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height, width = roi.shape
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aspect_ratio = width / height
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# 计算边缘强度（白色区域应该有清晰的边缘）
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edges = cv2.Canny(original_roi, 50, 150)
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edge_density = np.sum(edges > 0) / total_pixels
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return {
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'white_ratio': white_ratio,
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'aspect_ratio': aspect_ratio,
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'edge_density': edge_density,
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'area': total_pixels
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}
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def detect_white_rectangles_in_image(image_path):
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"""
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检测图片中的白色矩形区域
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"""
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# 读取图像
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image = cv2.imread(image_path)
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if image is None:
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print(f"无法读取图像: {image_path}")
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return []
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# 预处理
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white_mask = preprocess_image(image)
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# 查找白色矩形
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valid_rectangles = find_white_rectangles(white_mask)
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results = []
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for contour, (x, y, w, h), approx in valid_rectangles:
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# 提取ROI
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roi_mask = white_mask[y:y+h, x:x+w]
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roi_original = image[y:y+h, x:x+w]
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# 分析特征
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features = analyze_white_region(roi_mask, roi_original)
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# 判断是否为有效的白色矩形
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is_valid_white_rectangle = (
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features['white_ratio'] > 0.4 and # 白色像素比例要求降低，从0.6降到0.4
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features['edge_density'] > 0.005 # 边缘密度要求降低，从0.01降到0.005
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)
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if is_valid_white_rectangle:
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results.append({
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'type': 'white_rectangle',
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'position': (x, y, w, h),
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'features': features,
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'confidence': 'high' if features['white_ratio'] > 0.8 else 'medium'
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})
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# 在图像上绘制结果
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cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
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cv2.putText(image, f"白色矩形", (x, y - 10),
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cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
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# 绘制轮廓点
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cv2.drawContours(image, [approx], -1, (255, 0, 0), 2)
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return image, results
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def main():
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"""
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主函数：测试白色矩形检测
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"""
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# 图片路径
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img_dir = Path("imgs")
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img1_path = img_dir / "1.jpg"
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img2_path = img_dir / "2.jpg"
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print("开始检测图片中的白色矩形区域...")
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# 检测第一张图片
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if img1_path.exists():
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print(f"\n检测图片: {img1_path}")
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result_img1, results1 = detect_white_rectangles_in_image(str(img1_path))
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print("检测结果:")
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for result in results1:
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print(f" 类型: {result['type']}, 位置: {result['position']}, 置信度: {result['confidence']}")
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print(f" 特征: 白色比例={result['features']['white_ratio']:.2f}, "
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f"宽高比={result['features']['aspect_ratio']:.2f}")
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# 保存结果图片
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output_path1 = img_dir / "result_1.jpg"
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cv2.imwrite(str(output_path1), result_img1)
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print(f"结果图片已保存到: {output_path1}")
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# 检测第二张图片
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if img2_path.exists():
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print(f"\n检测图片: {img2_path}")
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result_img2, results2 = detect_white_rectangles_in_image(str(img2_path))
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print("检测结果:")
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for result in results2:
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print(f" 类型: {result['type']}, 位置: {result['position']}, 置信度: {result['confidence']}")
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print(f" 特征: 白色比例={result['features']['white_ratio']:.2f}, "
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f"宽高比={result['features']['aspect_ratio']:.2f}")
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# 保存结果图片
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output_path2 = img_dir / "result_2.jpg"
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cv2.imwrite(str(output_path2), result_img2)
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print(f"结果图片已保存到: {output_path2}")
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print("\n检测完成！")
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if __name__ == "__main__":
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main()
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