havoc420ubuntu
852a948a6f
- Updated task_1.py to improve navigation logic and streamline movement functions. - Enhanced task_2.py with refined movement parameters for better execution and added logging for debugging. - Adjusted function calls in main.py to reflect changes in task execution flow.
132 lines
4.4 KiB
Python
132 lines
4.4 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import cv2
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import numpy as np
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import os
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import argparse
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import matplotlib.pyplot as plt
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def analyze_yellow_area_ratio(image_path, debug=False, save_result=False):
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"""
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专门针对黄色区域的分析算法
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参数:
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image_path: 图片路径
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debug: 是否显示处理过程中的图像，用于调试
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save_result: 是否保存处理结果图像
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返回:
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yellow_ratio: 黄色区域占比（0-1之间的浮点数）
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"""
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# 读取图片
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img = cv2.imread(image_path)
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if img is None:
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raise ValueError(f"无法读取图片: {image_path}")
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# 获取图片文件名（不带路径和扩展名）
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filename = os.path.splitext(os.path.basename(image_path))[0]
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# 转换为HSV色彩空间（更适合颜色分割）
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hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
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# 提取图像的各个通道
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h, s, v = cv2.split(hsv)
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# 黄色在HSV中的范围：色调约为20-30度（OpenCV中为10-30）
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# 黄色通常有较高的饱和度和亮度
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yellow_hue_lower = np.array([20, 100, 100])
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yellow_hue_upper = np.array([40, 255, 255])
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# 创建黄色区域掩码
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yellow_mask = cv2.inRange(hsv, yellow_hue_lower, yellow_hue_upper)
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# 应用形态学操作
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kernel = np.ones((5, 5), np.uint8)
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yellow_mask = cv2.morphologyEx(yellow_mask, cv2.MORPH_OPEN, kernel)
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yellow_mask = cv2.morphologyEx(yellow_mask, cv2.MORPH_CLOSE, kernel)
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# 使用连通区域分析，去除小的噪点区域
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num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(yellow_mask, connectivity=8)
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# 过滤小的连通区域
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min_size = 500 # 最小连通区域大小
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filtered_yellow_mask = np.zeros_like(yellow_mask)
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# 从索引1开始，因为0是背景
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for i in range(1, num_labels):
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if stats[i, cv2.CC_STAT_AREA] >= min_size:
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filtered_yellow_mask[labels == i] = 255
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# 计算黄色区域占比
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height, width = yellow_mask.shape
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total_pixels = height * width
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yellow_pixels = np.sum(filtered_yellow_mask == 255)
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yellow_ratio = yellow_pixels / total_pixels
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# 在原图上标记黄色区域
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result = img.copy()
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overlay = img.copy()
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overlay[filtered_yellow_mask > 0] = [0, 165, 255] # 用橙色标记黄色区域
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cv2.addWeighted(overlay, 0.4, img, 0.6, 0, result) # 半透明效果
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# 显示检测结果信息
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cv2.putText(result, f"Yellow Ratio: {yellow_ratio:.2%}", (10, 30),
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cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 165, 255), 2)
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# 调试模式：显示处理过程图像
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if debug:
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plt.figure(figsize=(15, 10))
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plt.subplot(231)
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plt.title("Original Image")
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plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
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plt.subplot(232)
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plt.title("Hue Channel")
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plt.imshow(h, cmap='hsv')
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plt.subplot(233)
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plt.title("Saturation Channel")
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plt.imshow(s, cmap='gray')
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plt.subplot(234)
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plt.title("Initial Yellow Mask")
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plt.imshow(yellow_mask, cmap='gray')
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plt.subplot(235)
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plt.title("Filtered Yellow Mask")
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plt.imshow(filtered_yellow_mask, cmap='gray')
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plt.subplot(236)
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plt.title("Yellow Detection Result")
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plt.imshow(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
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plt.tight_layout()
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plt.show()
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# 保存结果
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if save_result:
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result_dir = "results"
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os.makedirs(result_dir, exist_ok=True)
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output_path = os.path.join(result_dir, f"{filename}_yellow_area_result.jpg")
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cv2.imwrite(output_path, result)
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print(f"结果已保存至: {output_path}")
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return yellow_ratio
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def main():
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parser = argparse.ArgumentParser(description='分析图片中黄色区域占比')
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parser.add_argument('--image_path', default='./image_20250525_090252.png', type=str, help='图片路径')
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parser.add_argument('--debug', default=False, action='store_true', help='显示处理过程图像')
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parser.add_argument('--save', action='store_true', help='保存处理结果图像')
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args = parser.parse_args()
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try:
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yellow_ratio = analyze_yellow_area_ratio(args.image_path, args.debug, args.save)
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print(f"黄色区域占比: {yellow_ratio:.2%}")
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except Exception as e:
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print(f"错误: {e}")
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if __name__ == "__main__":
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main() |