percoandClaude Sonnet 4.6
65b74ce46d
- Remplace la source Frigate par l'API GetAiState de la caméra Reolink - Capture RTSP en temps réel (~20s) quand véhicule détecté, garde la meilleure frame - LPR avec YOLOv9 (détection plaque) + PaddleOCR v4 (lecture texte) via ONNX - Modèles partagés avec Frigate (volume local ./models/) - Cooldown 60s entre événements pour éviter les doublons Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
72 lines
2.1 KiB
Python
72 lines
2.1 KiB
Python
import cv2
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import numpy as np
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import colorsys
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def extract_dominant_color_from_frame(frame: np.ndarray, bbox: dict | None = None) -> tuple[str, str]:
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"""Returns (hex_color, color_name) from a BGR numpy frame."""
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if frame is None:
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return "#808080", "Inconnu"
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return _dominant_color(frame, bbox)
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def extract_dominant_color(image_path: str, bbox: dict | None = None) -> tuple[str, str]:
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"""Returns (hex_color, color_name) from image path, optionally cropped to bbox."""
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img = cv2.imread(image_path)
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if img is None:
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return "#808080", "Inconnu"
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return _dominant_color(img, bbox)
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def _dominant_color(img: np.ndarray, bbox: dict | None = None) -> tuple[str, str]:
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if bbox:
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h, w = img.shape[:2]
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x1 = max(0, int(bbox.get("x", 0) * w))
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y1 = max(0, int(bbox.get("y", 0) * h))
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x2 = min(w, int((bbox.get("x", 0) + bbox.get("width", 1)) * w))
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y2 = min(h, int((bbox.get("y", 0) + bbox.get("height", 1)) * h))
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if x2 > x1 and y2 > y1:
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img = img[y1:y2, x1:x2]
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small = cv2.resize(img, (60, 60), interpolation=cv2.INTER_AREA)
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pixels = small.reshape(-1, 3).astype(np.float32)
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k = min(4, len(pixels))
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_, labels, centers = cv2.kmeans(
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pixels, k, None,
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(cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 20, 1.0),
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10, cv2.KMEANS_RANDOM_CENTERS
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)
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counts = np.bincount(labels.flatten())
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dominant = centers[np.argmax(counts)]
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b, g, r = int(dominant[0]), int(dominant[1]), int(dominant[2])
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hex_color = f"#{r:02x}{g:02x}{b:02x}"
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return hex_color, _name_color(r, g, b)
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def _name_color(r: int, g: int, b: int) -> str:
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h, s, v = colorsys.rgb_to_hsv(r / 255, g / 255, b / 255)
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if v < 0.18:
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return "Noir"
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if v > 0.82 and s < 0.18:
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return "Blanc"
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if s < 0.18:
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return "Gris"
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hue = h * 360
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if hue < 15 or hue >= 345:
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return "Rouge"
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if hue < 45:
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return "Orange"
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if hue < 75:
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return "Jaune"
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if hue < 150:
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return "Vert"
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if hue < 195:
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return "Cyan"
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if hue < 255:
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return "Bleu"
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if hue < 290:
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return "Violet"
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return "Rose"
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