143 lines
4.9 KiB
Python
143 lines
4.9 KiB
Python
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#!/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_gray_sky_ratio(image_path, debug=False, save_result=False):
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"""
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专门针对灰色天空图片的分析算法
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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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sky_ratio: 天空占比（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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# 灰色天空特征：饱和度低（灰色是低饱和度的颜色）
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# 亮度适中到较高（相对于黑色路面）
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low_saturation_mask = s < 30 # 低饱和度阈值
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medium_high_value_mask = v > 120 # 中高亮度阈值
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# 组合掩码：低饱和度且中高亮度的区域可能是灰色天空
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sky_mask = np.logical_and(low_saturation_mask, medium_high_value_mask).astype(np.uint8) * 255
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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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sky_mask = cv2.morphologyEx(sky_mask, cv2.MORPH_OPEN, kernel)
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sky_mask = cv2.morphologyEx(sky_mask, cv2.MORPH_CLOSE, kernel)
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# 假设天空通常位于图像上部
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height, width = sky_mask.shape
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sky_region_height = int(height * 0.6) # 假设天空在上部60%的区域
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# 保留上部区域的掩码
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upper_mask = np.zeros_like(sky_mask)
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upper_mask[:sky_region_height, :] = 255
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# 结合上部区域限制
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sky_mask = cv2.bitwise_and(sky_mask, upper_mask)
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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(sky_mask, connectivity=8)
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# 过滤小的连通区域
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min_size = 1000 # 最小连通区域大小
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filtered_sky_mask = np.zeros_like(sky_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_sky_mask[labels == i] = 255
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# 计算天空区域占比
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total_pixels = height * width
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sky_pixels = np.sum(filtered_sky_mask == 255)
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sky_ratio = sky_pixels / total_pixels
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# 在原图上标记天空区域
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result = img.copy()
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overlay = img.copy()
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overlay[filtered_sky_mask > 0] = [0, 255, 255] # 用黄色标记天空区域
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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"Sky Ratio: {sky_ratio:.2%}", (10, 30),
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cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 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("Saturation Channel")
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plt.imshow(s, cmap='gray')
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plt.subplot(233)
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plt.title("Value Channel")
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plt.imshow(v, cmap='gray')
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plt.subplot(234)
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plt.title("Initial Sky Mask")
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plt.imshow(sky_mask, cmap='gray')
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plt.subplot(235)
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plt.title("Filtered Sky Mask")
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plt.imshow(filtered_sky_mask, cmap='gray')
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plt.subplot(236)
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plt.title("Sky 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}_gray_sky_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 sky_ratio
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def main():
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parser = argparse.ArgumentParser(description='分析图片中灰色天空区域占比')
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# parser.add_argument('--image_path', default='./res/bar/image_20250525_085235.png', type=str, help='图片路径')
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parser.add_argument('--image_path', default='./res/bar/image_20250525_085240.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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sky_ratio = analyze_gray_sky_ratio(args.image_path, args.debug, args.save)
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print(f"灰色天空区域占比: {sky_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()
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