212 lines
6.7 KiB
Python
212 lines
6.7 KiB
Python
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import os
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import cv2
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import numpy as np
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from pathlib import Path
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from ultralytics import YOLO
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import matplotlib.pyplot as plt
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TARGET_SIZE = 640 # 模型输入尺寸
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IMAGE_DIR = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/1/611/train/class0" # 👈 图片文件夹路径
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MODEL_PATH = "/home/hx/yolo/ultralytics_yolo11-main/runs/train/61seg/exp5/weights/best.pt"
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OUTPUT_DIR = "./output"
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# --------------------
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# 全局 ROI 定义
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# --------------------
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ROIS = [
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(670,623,465,178), # (x, y, w, h)
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]
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# --------------------
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# 根据角点分布，选取左右边缘角点
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# --------------------
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def select_edge_corners(corners, w, left_ratio=0.2, right_ratio=0.2, y_var_thresh=5):
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if corners is None:
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return [], []
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corners = np.int32(corners).reshape(-1, 2)
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x_min, x_max = 0, w
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left_thresh = x_min + int(w * left_ratio)
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right_thresh = x_max - int(w * right_ratio)
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# 左右候选角点
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left_candidates = corners[corners[:, 0] <= left_thresh]
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right_candidates = corners[corners[:, 0] >= right_thresh]
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# 按 y 变化筛选
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def filter_by_y_variation(pts):
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if len(pts) < 2:
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return pts
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pts_sorted = pts[np.argsort(pts[:, 1])]
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diffs = np.abs(np.diff(pts_sorted[:, 1]))
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keep_idx = np.where(diffs > y_var_thresh)[0]
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selected = [pts_sorted[i] for i in keep_idx] + [pts_sorted[i + 1] for i in keep_idx]
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return np.array(selected) if len(selected) > 0 else pts_sorted
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left_final = filter_by_y_variation(left_candidates)
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right_final = filter_by_y_variation(right_candidates)
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return left_final, right_final
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# --------------------
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# 拟合直线并剔除离散点
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# --------------------
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def fit_line_with_outlier_removal(pts, dist_thresh=10):
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if pts is None or len(pts) < 2:
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return None, pts
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pts = np.array(pts)
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x, y = pts[:, 0], pts[:, 1]
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# 第一次拟合
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m, b = np.polyfit(y, x, 1) # x = m*y + b
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x_fit = m * y + b
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dists = np.abs(x - x_fit)
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# 剔除离群点
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mask = dists < dist_thresh
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x2, y2 = x[mask], y[mask]
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if len(x2) < 2:
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return (m, b), pts
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# 二次拟合
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m, b = np.polyfit(y2, x2, 1)
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return (m, b), np.stack([x2, y2], axis=1)
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# --------------------
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# 推理 ROI 并计算左右直线 x 差
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# --------------------
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def infer_mask_and_get_xdiff(image_path, model, output_dir="./output"):
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image_path = Path(image_path)
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orig_img = cv2.imread(str(image_path))
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overlay_img = orig_img.copy()
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x_diff_value = None
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intersections = [] # 👈 保存交点 (全图坐标)
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for idx, (x, y, w, h) in enumerate(ROIS):
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roi_img = orig_img[y:y+h, x:x+w]
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resized_img = cv2.resize(roi_img, (TARGET_SIZE, TARGET_SIZE))
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results = model(source=resized_img, imgsz=TARGET_SIZE, verbose=False)
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result = results[0]
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if result.masks is None or len(result.masks.data) == 0:
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print(f"❌ {image_path.name} 未检测到 mask")
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continue
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mask = result.masks.data[0].cpu().numpy()
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mask_bin = (mask > 0.5).astype(np.uint8)
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mask_bin = cv2.resize(mask_bin, (w, h), interpolation=cv2.INTER_NEAREST)
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mask_gray = (mask_bin * 255).astype(np.uint8)
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corners = cv2.goodFeaturesToTrack(mask_gray, maxCorners=200, qualityLevel=0.01, minDistance=5)
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left_pts, right_pts = select_edge_corners(corners, w)
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left_line, _ = fit_line_with_outlier_removal(left_pts)
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right_line, _ = fit_line_with_outlier_removal(right_pts)
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if left_line and right_line:
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# ---------- 黄色参考线 ----------
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y_ref = int(h * 0.6)
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# ---------- 左右交点 ----------
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m1, b1 = left_line
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m2, b2 = right_line
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x_left = int(m1 * y_ref + b1)
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x_right = int(m2 * y_ref + b2)
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x_diff_value = abs(x_right - x_left)
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# ---------- ROI → 原图坐标 ----------
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left_pt_global = (x + x_left, y + y_ref)
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right_pt_global = (x + x_right, y + y_ref)
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intersections.append({
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"left": left_pt_global,
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"right": right_pt_global,
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"x_diff": x_diff_value
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})
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# ---------- 画黄色参考线 ----------
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cv2.line(
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overlay_img[y:y+h, x:x+w],
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(0, y_ref), (w, y_ref),
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(0, 255, 255), 2
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)
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# ---------- 画红 / 蓝拟合线 ----------
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for (m, b), color in [(left_line, (0, 0, 255)), (right_line, (255, 0, 0))]:
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y1, y2 = 0, h
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x1_line, x2_line = int(m * y1 + b), int(m * y2 + b)
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cv2.line(
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overlay_img[y:y+h, x:x+w],
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(x1_line, y1), (x2_line, y2),
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color, 3
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)
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# ---------- 画交点 ----------
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cv2.circle(overlay_img, left_pt_global, 6, (0, 0, 255), -1)
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cv2.circle(overlay_img, right_pt_global, 6, (255, 0, 0), -1)
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cv2.putText(
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overlay_img,
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f"L{left_pt_global} R{right_pt_global}",
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(x + 10, y + 30),
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cv2.FONT_HERSHEY_SIMPLEX,
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0.8,
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(0, 255, 255),
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2
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)
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save_path = Path(output_dir) / f"mask_edge_{image_path.name}"
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cv2.imwrite(str(save_path), overlay_img)
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print(f"✅ 保存结果: {save_path}")
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return x_diff_value, intersections
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# =====================
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# 批量处理文件夹中的图片
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# =====================
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def process_image_folder(image_dir, model_path):
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model = YOLO(model_path)
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output_dir = OUTPUT_DIR
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Path(output_dir).mkdir(parents=True, exist_ok=True)
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xdiff_results = {}
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for img_name in sorted(os.listdir(image_dir)):
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if not img_name.lower().endswith((".jpg", ".png", ".jpeg")):
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continue
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img_path = os.path.join(image_dir, img_name)
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x_diff = infer_mask_and_get_xdiff(img_path, model, output_dir)
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if x_diff is not None:
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xdiff_results[img_name] = x_diff
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# 结果展示
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if xdiff_results:
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plt.figure(figsize=(8, 4))
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plt.plot(list(xdiff_results.keys()), list(xdiff_results.values()), '-o')
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plt.xticks(rotation=45)
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plt.title("同一y坐标下左右液面直线x差变化趋势")
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plt.ylabel("x差 (像素)")
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plt.xlabel("图片序号")
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plt.tight_layout()
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plt.show()
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print("\n=== 液面变化结果 ===")
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for k, v in xdiff_results.items():
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print(f"{k}: x差 = {v:.2f}")
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else:
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print("⚠️ 未检测到有效结果")
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# ===================== 使用示例 =====================
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if __name__ == "__main__":
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process_image_folder(IMAGE_DIR, MODEL_PATH)
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