charlie880624
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- Add golf1/2/4/7/8 dataset classes for semantic segmentation - Add kneron-specific configs (meconfig series, kn_stdc1_golf4class) - Organize scripts into tools/check/ and tools/kneron/ - Add kneron_preprocessing module - Update README with quick-start guide - Update .gitignore to exclude data dirs, onnx, nef outputs Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
71 lines
2.2 KiB
Python
71 lines
2.2 KiB
Python
import cv2
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import numpy as np
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# === 1. 檔案與參數設定 ===
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img_path = r'C:\Users\rd_de\kneronstdc\work_dirs\vis_results\good\pic_0441_jpg.rf.6e56eb8c0bed7f773fb447b9e217f779_leftImg8bit.png'
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# 色彩轉 label ID（RGB）
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CLASS_RGB_TO_ID = {
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(128, 64, 128): 3, # road（灰）
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(0, 255, 0): 1, # grass（綠）
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(255, 0, 255): 9, # background or sky（紫）可忽略
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}
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ROAD_ID = 3
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GRASS_ID = 1
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# === 2. 讀圖並轉為 label mask ===
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bgr_img = cv2.imread(img_path)
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rgb_img = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2RGB)
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height, width, _ = rgb_img.shape
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label_mask = np.zeros((height, width), dtype=np.uint8)
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for rgb, label in CLASS_RGB_TO_ID.items():
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match = np.all(rgb_img == rgb, axis=-1)
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label_mask[match] = label
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# === 3. 分析畫面中下區域 ===
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y_start = int(height * 0.6)
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x_start = int(width * 0.4)
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x_end = int(width * 0.6)
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roi = label_mask[y_start:, x_start:x_end]
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total_pixels = roi.size
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road_pixels = np.sum(roi == ROAD_ID)
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grass_pixels = np.sum(roi == GRASS_ID)
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road_ratio = road_pixels / total_pixels
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grass_ratio = grass_pixels / total_pixels
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# === 4. 重心偏移分析 ===
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road_mask = (label_mask == ROAD_ID).astype(np.uint8)
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M = cv2.moments(road_mask)
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center_x = width // 2
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offset = 0
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cx = center_x
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if M["m00"] > 0:
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cx = int(M["m10"] / M["m00"])
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offset = cx - center_x
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# === 5. 結果輸出 ===
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print(f"🔍 中央 ROI - road比例: {road_ratio:.2f}, grass比例: {grass_ratio:.2f}")
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if road_ratio < 0.5:
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print("⚠️ 偏離道路（ROI 中道路比例過少）")
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if grass_ratio > 0.3:
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print("❗ 車輛壓到草地！")
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if abs(offset) > 40:
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print(f"⚠️ 道路重心偏移：{offset} px")
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else:
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print("✅ 道路重心正常")
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# === 6. 可視化 ===
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vis_img = bgr_img.copy()
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cv2.rectangle(vis_img, (x_start, y_start), (x_end, height), (0, 255, 255), 2) # 黃色框 ROI
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cv2.line(vis_img, (center_x, 0), (center_x, height), (255, 0, 0), 2) # 藍色中心線
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cv2.circle(vis_img, (cx, height // 2), 6, (0, 0, 255), -1) # 紅色重心點
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# 輸出圖片
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save_path = r'C:\Users\rd_de\kneronstdc\work_dirs\vis_results\good\visual_check.png'
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cv2.imwrite(save_path, vis_img)
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print(f"✅ 分析圖儲存成功：{save_path}")
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