191 lines
6.3 KiB
Python
191 lines
6.3 KiB
Python
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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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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# --------------------
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# 参数设置（固定在脚本中）
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# --------------------
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INPUT_DIR = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/test_l" # 图片文件夹
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MODEL_PATH = "best.pt" # YOLO 模型
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OUTPUT_DIR = "./output" # 保存结果
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TARGET_SIZE = 640 # YOLO 输入尺寸
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DIST_THRESH = 15 # 剔除离群点阈值
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MAX_CORNERS = 200 # goodFeaturesToTrack 最大角点数
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QUALITY_LEVEL = 0.01 # goodFeaturesToTrack qualityLevel
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MIN_DISTANCE = 5 # goodFeaturesToTrack minDistance
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# 全局 ROI 定义
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ROIS = [
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(859, 810, 696, 328), # (x, y, w, h)
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]
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# --------------------
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# 左右边缘角点筛选
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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 np.zeros((0,2), dtype=np.int32), np.zeros((0,2), dtype=np.int32)
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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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left_candidates = corners[corners[:,0] <= left_thresh]
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right_candidates = corners[corners[:,0] >= right_thresh]
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def filter_by_y_variation(pts):
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if len(pts) < 2:
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return pts.astype(np.int32)
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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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if len(keep_idx) == 0:
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return pts_sorted.astype(np.int32)
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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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selected = np.array(selected)
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selected = selected[np.argsort(selected[:,1])]
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_, idx = np.unique(selected.reshape(-1,2), axis=0, return_index=True)
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selected = selected[np.sort(idx)]
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return selected.astype(np.int32)
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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=DIST_THRESH):
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if pts is None or len(pts) < 2:
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return None, np.zeros((0,2), dtype=np.int32)
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pts = np.array(pts, dtype=np.float64)
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x = pts[:,0]
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y = pts[:,1]
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try:
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m, b = np.polyfit(y, x, 1)
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except:
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return None, np.zeros((0,2), dtype=np.int32)
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x_fit = m*y + b
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dists = np.abs(x - x_fit)
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mask = dists < dist_thresh
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if np.sum(mask) < 2:
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return (m,b), pts.astype(np.int32)
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x2, y2 = x[mask], y[mask]
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try:
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m2, b2 = np.polyfit(y2, x2, 1)
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except:
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return (m,b), np.stack([x2,y2],axis=1).astype(np.int32)
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inliers = np.stack([x2,y2],axis=1).astype(np.int32)
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return (m2,b2), inliers
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# --------------------
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# 单张图 ROI 处理
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# --------------------
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def process_roi_on_image(orig_img, roi):
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rx, ry, rw, rh = roi
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h_img, w_img = orig_img.shape[:2]
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rx = max(0, rx); ry = max(0, ry)
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rw = min(rw, w_img - rx); rh = min(rh, h_img - ry)
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roi_img = orig_img[ry:ry+rh, rx:rx+rw].copy()
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if roi_img.size == 0:
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return None
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resized = cv2.resize(roi_img, (TARGET_SIZE, TARGET_SIZE))
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results = MODEL(resized, imgsz=TARGET_SIZE, verbose=False)
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result = results[0]
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overlay_roi = roi_img.copy()
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if result.masks is None or len(result.masks.data)==0:
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return overlay_roi
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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,(rw,rh), interpolation=cv2.INTER_NEAREST)
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# mask 半透明覆盖
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color_mask = np.zeros_like(overlay_roi, dtype=np.uint8)
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color_mask[mask_bin==1] = (0,255,0)
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overlay_roi = cv2.addWeighted(overlay_roi,0.7,color_mask,0.3,0)
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# 角点检测
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mask_gray = (mask_bin*255).astype(np.uint8)
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corners = cv2.goodFeaturesToTrack(mask_gray, maxCorners=MAX_CORNERS,
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qualityLevel=QUALITY_LEVEL, minDistance=MIN_DISTANCE)
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left_pts, right_pts = select_edge_corners(corners, rw)
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left_line, left_inliers = fit_line_with_outlier_removal(left_pts)
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right_line, right_inliers = fit_line_with_outlier_removal(right_pts)
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# 可视化 inliers
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for (cx,cy) in left_inliers:
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cv2.circle(overlay_roi,(int(cx),int(cy)),4,(0,0,255),-1)
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for (cx,cy) in right_inliers:
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cv2.circle(overlay_roi,(int(cx),int(cy)),4,(255,0,0),-1)
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# 拟合直线
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if left_line is not None:
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m,b = left_line
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y1,y2 = 0, rh-1
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x1 = int(m*y1+b); x2 = int(m*y2+b)
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cv2.line(overlay_roi,(x1,y1),(x2,y2),(0,0,200),3)
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if right_line is not None:
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m,b = right_line
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y1,y2 = 0, rh-1
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x1 = int(m*y1+b); x2 = int(m*y2+b)
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cv2.line(overlay_roi,(x1,y1),(x2,y2),(200,0,0),3)
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return overlay_roi
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# --------------------
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# 批量推理文件夹
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# --------------------
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def infer_folder_images():
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input_dir = Path(INPUT_DIR)
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output_dir = Path(OUTPUT_DIR)
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output_dir.mkdir(parents=True, exist_ok=True)
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exts = ('*.jpg','*.jpeg','*.png','*.bmp','*.tif','*.tiff')
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files = []
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for e in exts:
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files.extend(sorted(input_dir.glob(e)))
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if len(files)==0:
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print("未找到图片文件")
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return
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print(f"找到 {len(files)} 张图片，开始推理...")
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for img_path in files:
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print("-> 处理:", img_path.name)
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orig_img = cv2.imread(str(img_path))
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if orig_img is None:
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print(" 无法读取，跳过")
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continue
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out_img = orig_img.copy()
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for roi in ROIS:
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overlay_roi = process_roi_on_image(orig_img, roi)
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if overlay_roi is not None:
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rx,ry,rw,rh = roi
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h,w = overlay_roi.shape[:2]
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out_img[ry:ry+h, rx:rx+w] = overlay_roi
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save_path = output_dir / f"mask_edge_corners_{img_path.name}"
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cv2.imwrite(str(save_path), out_img)
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print(" 已保存 ->", save_path.name)
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print("批量推理完成，结果保存在:", output_dir)
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# --------------------
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# 主程序
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# --------------------
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if __name__ == "__main__":
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MODEL = YOLO(MODEL_PATH)
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infer_folder_images()
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