195 lines
7.0 KiB
Python
Executable File
195 lines
7.0 KiB
Python
Executable File
#!/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_sky_ratio(image_path, method="threshold", debug=False, save_result=False):
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"""
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分析图片中天空占整个图片的比例，支持多种检测方法
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参数:
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image_path: 图片路径
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method: 检测方法，可选 "threshold"(阈值法), "kmeans"(K均值聚类), "gradient"(梯度法)
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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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# 根据不同方法检测天空
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if method == "threshold":
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sky_mask, sky_ratio = threshold_method(img)
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elif method == "kmeans":
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sky_mask, sky_ratio = kmeans_method(img)
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elif method == "gradient":
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sky_mask, sky_ratio = gradient_method(img)
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else:
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raise ValueError(f"不支持的检测方法: {method}")
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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[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=(12, 8))
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plt.subplot(221)
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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(222)
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plt.title("Sky Mask")
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plt.imshow(sky_mask, cmap='gray')
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plt.subplot(223)
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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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# 直方图分析
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plt.subplot(224)
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plt.title("Grayscale Histogram")
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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plt.hist(gray.ravel(), 256, [0, 256])
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plt.xlim([0, 256])
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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}_{method}_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 threshold_method(img, threshold=180):
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"""使用简单阈值法检测天空"""
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# 转换为灰度图
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# 使用阈值分割天空区域（天空通常是图像中较亮的部分）
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_, sky_mask = cv2.threshold(gray, threshold, 255, cv2.THRESH_BINARY)
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# 应用形态学操作，去除噪点
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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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# 计算天空区域占比
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total_pixels = gray.shape[0] * gray.shape[1]
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sky_pixels = np.sum(sky_mask == 255)
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sky_ratio = sky_pixels / total_pixels
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return sky_mask, sky_ratio
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def kmeans_method(img, k=3):
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"""使用K均值聚类检测天空"""
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# 转换为RGB格式用于聚类
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img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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# 重塑图像为二维数组
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pixels = img_rgb.reshape((-1, 3)).astype(np.float32)
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# 定义K均值聚类终止条件
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criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.2)
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# 应用K均值聚类
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_, labels, centers = cv2.kmeans(pixels, k, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
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# 找出最亮的聚类中心（可能是天空）
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centers = centers.astype(np.uint8)
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brightness = np.mean(centers, axis=1)
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sky_cluster = np.argmax(brightness)
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# 创建掩码：仅保留最亮聚类的像素
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sky_mask = np.zeros(img_rgb.shape[:2], dtype=np.uint8)
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labels = labels.reshape(img_rgb.shape[:2])
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sky_mask[labels == sky_cluster] = 255
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# 应用形态学操作，去除噪点
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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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# 计算天空区域占比
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total_pixels = sky_mask.shape[0] * sky_mask.shape[1]
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sky_pixels = np.sum(sky_mask == 255)
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sky_ratio = sky_pixels / total_pixels
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return sky_mask, sky_ratio
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def gradient_method(img):
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"""使用图像梯度检测天空（假设天空在图像上部且边缘较少）"""
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# 转换为灰度图
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# 使用Sobel算子计算梯度
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sobel_x = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=3)
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sobel_y = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=3)
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# 计算梯度幅值
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gradient_magnitude = np.sqrt(sobel_x**2 + sobel_y**2)
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gradient_magnitude = cv2.normalize(gradient_magnitude, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8)
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# 假设天空区域梯度较小（纹理较少）
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_, gradient_mask = cv2.threshold(gradient_magnitude, 50, 255, cv2.THRESH_BINARY_INV)
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# 假设天空通常位于图像上部
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height, width = gradient_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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# 创建天空掩码
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sky_mask = np.zeros_like(gradient_mask)
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sky_mask[:sky_region_height, :] = gradient_mask[:sky_region_height, :]
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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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# 计算天空区域占比
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total_pixels = gray.shape[0] * gray.shape[1]
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sky_pixels = np.sum(sky_mask == 255)
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sky_ratio = sky_pixels / total_pixels
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return sky_mask, sky_ratio
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def main():
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parser = argparse.ArgumentParser(description='分析图片中天空区域占比')
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parser.add_argument('image_path', help='图片路径')
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parser.add_argument('--method', default='threshold', choices=['threshold', 'kmeans', 'gradient'],
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help='天空检测方法: threshold(阈值法), kmeans(K均值聚类), gradient(梯度法)')
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parser.add_argument('--debug', 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_sky_ratio(args.image_path, args.method, args.debug, args.save)
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print(f"使用{args.method}方法检测的天空区域占比: {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() |