191 lines
7.2 KiB
Python
191 lines
7.2 KiB
Python
import cv2
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import os
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import numpy as np
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import matplotlib.pyplot as plt
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def test_images():
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"""测试图像处理并显示结果"""
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# 获取图像路径
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image_dir = "/home/task/logs/image"
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if not os.path.exists(image_dir):
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print(f"找不到图像目录: {image_dir}")
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return
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# 获取所有原始图像
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orig_images = [f for f in os.listdir(image_dir) if f.startswith("dual_track_orig_")]
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if not orig_images:
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print(f"未找到原始图像文件在 {image_dir} 目录下")
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return
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print(f"找到 {len(orig_images)} 张原始图像")
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# 对每张图像进行处理
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for img_file in orig_images:
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img_path = os.path.join(image_dir, img_file)
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print(f"\n处理图像: {img_file}")
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# 读取图像
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img = cv2.imread(img_path)
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if img is None:
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print(f"无法读取图像: {img_path}")
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continue
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# 获取图像尺寸
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height, width = img.shape[:2]
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print(f"图像尺寸: {width}x{height}")
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# 转换为RGB以便使用matplotlib显示
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img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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# 转换到HSV颜色空间以便更容易提取黄色
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hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
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# 标准黄色的HSV范围
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lower_yellow = np.array([15, 80, 80])
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upper_yellow = np.array([35, 255, 255])
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# 创建黄色的掩码
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mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
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# 形态学操作以改善掩码
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kernel = np.ones((5, 5), np.uint8)
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mask = cv2.dilate(mask, kernel, iterations=1)
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mask = cv2.erode(mask, np.ones((3, 3), np.uint8), iterations=1)
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# 边缘检测
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edges = cv2.Canny(mask, 50, 150, apertureSize=3)
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# 霍夫变换检测直线
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lines = cv2.HoughLinesP(edges, 1, np.pi/180, threshold=25,
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minLineLength=width*0.05, maxLineGap=40)
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# 显示结果
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fig, axs = plt.subplots(2, 2, figsize=(12, 10))
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# 原始图像
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axs[0, 0].imshow(img_rgb)
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axs[0, 0].set_title('原始图像')
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axs[0, 0].axis('off')
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# 黄色掩码
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axs[0, 1].imshow(mask, cmap='gray')
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axs[0, 1].set_title('黄色掩码')
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axs[0, 1].axis('off')
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# 边缘检测
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axs[1, 0].imshow(edges, cmap='gray')
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axs[1, 0].set_title('边缘检测')
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axs[1, 0].axis('off')
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# 直线检测结果
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lines_img = img_rgb.copy()
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if lines is not None:
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center_x = width // 2
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# 筛选垂直线
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vertical_lines = []
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for line in lines:
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x1, y1, x2, y2 = line[0]
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# 计算斜率
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if abs(x2 - x1) < 5: # 几乎垂直的线
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slope = 100
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else:
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slope = (y2 - y1) / (x2 - x1)
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# 筛选接近垂直的线
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if abs(slope) > 0.75:
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line_length = np.sqrt((x2-x1)**2 + (y2-y1)**2)
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mid_x = (x1 + x2) / 2
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mid_y = (y1 + y2) / 2
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# 根据是左侧还是右侧线使用不同颜色
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color = (0, 0, 255) if mid_x < center_x else (255, 0, 0)
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# 绘制线段
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cv2.line(lines_img, (x1, y1), (x2, y2), color, 2)
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# 记录垂直线
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vertical_lines.append((line[0], mid_x, mid_y, slope, line_length))
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# 按x坐标将线分为左右两组
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left_lines = [line for line in vertical_lines if line[1] < center_x]
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right_lines = [line for line in vertical_lines if line[1] > center_x]
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print(f"检测到 {len(lines)} 条线，其中 {len(vertical_lines)} 条垂直线")
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print(f"左侧线: {len(left_lines)}, 右侧线: {len(right_lines)}")
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# 简化的对线进行评分 - 只考虑中心接近度
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def score_by_center_proximity(lines, is_left):
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center_x = width // 2
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scored_lines = []
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for line in lines:
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_, mid_x, _, _, _ = line
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distance_to_center = abs(mid_x - center_x)
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# 归一化到图像宽度
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normalized_distance = distance_to_center / (width * 0.5)
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center_score = max(0, 1.0 - normalized_distance)
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scored_lines.append((line, center_score))
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# 按照中心接近度评分排序
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return sorted(scored_lines, key=lambda x: x[1], reverse=True)
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# 找到最接近中心的左右线
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best_left_lines = score_by_center_proximity(left_lines, True)
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best_right_lines = score_by_center_proximity(right_lines, False)
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# 绘制最佳左右线
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if best_left_lines:
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best_left = best_left_lines[0][0]
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x1, y1, x2, y2 = best_left[0]
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cv2.line(lines_img, (x1, y1), (x2, y2), (0, 255, 0), 3)
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print(f"最佳左线: x={best_left[1]:.1f}, score={best_left_lines[0][1]:.2f}")
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if best_right_lines:
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best_right = best_right_lines[0][0]
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x1, y1, x2, y2 = best_right[0]
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cv2.line(lines_img, (x1, y1), (x2, y2), (0, 255, 0), 3)
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print(f"最佳右线: x={best_right[1]:.1f}, score={best_right_lines[0][1]:.2f}")
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# 绘制中心线
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if best_left_lines and best_right_lines:
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best_left = best_left_lines[0][0]
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best_right = best_right_lines[0][0]
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# 计算轨道宽度
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track_width = best_right[1] - best_left[1]
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print(f"轨道宽度: {track_width:.1f}像素 ({track_width/width*100:.1f}% 的图像宽度)")
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# 计算中心点
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center_line_x = (best_left[1] + best_right[1]) / 2
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center_line_y = height
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# 计算中心线与图像中心的偏差
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deviation = center_line_x - center_x
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print(f"中心偏差: {deviation:.1f}像素")
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# 绘制中心点和偏差信息
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cv2.circle(lines_img, (int(center_line_x), int(center_line_y)), 10, (255, 0, 255), -1)
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cv2.line(lines_img, (int(center_x), 0), (int(center_x), height), (0, 0, 255), 1)
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else:
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print("未检测到线段")
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# 将BGR转换为RGB以便matplotlib显示
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lines_img = cv2.cvtColor(lines_img, cv2.COLOR_BGR2RGB)
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axs[1, 1].imshow(lines_img)
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axs[1, 1].set_title('检测到的线段')
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axs[1, 1].axis('off')
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plt.tight_layout()
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plt.show()
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# 等待用户按键继续
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input("按Enter继续下一张图片...")
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if __name__ == "__main__":
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test_images() |