\u4f7f\u7528opencv haar cascades\u7ea7\u8054\u68c0\u6d4b\u5668\u8bc6\u522b\u4eba\u8138\uff0c\u4f7f\u7528opencv facemark LBF\u8bc6\u522b\u4eba\u8138\u5173\u952e\u70b9\u3002<\/p>\n\n\n\n
from pathlib import Path\n\nimport cv2\n\n\ndef resolve_haarcascade(base_dir: Path) -> str:\n candidates = []\n\n if hasattr(cv2, \"data\") and hasattr(cv2.data, \"haarcascades\"):\n candidates.append(Path(cv2.data.haarcascades) \/ \"haarcascade_frontalface_alt2.xml\")\n\n cv2_root = Path(cv2.__file__).resolve().parent\n print(f\"cv2_root:{cv2_root}\")\n \n candidates.extend(\n [\n base_dir \/ \"haarcascade_frontalface_alt2.xml\",\n Path(\"\/opt\/homebrew\/Cellar\/opencv\/4.13.0_10\/share\/opencv4\/haarcascades\/haarcascade_frontalface_alt2.xml\"),\n cv2_root \/ \"data\" \/ \"haarcascade_frontalface_alt2.xml\",\n cv2_root.parent.parent.parent \/ \"share\" \/ \"opencv4\" \/ \"haarcascades\" \/ \"haarcascade_frontalface_alt2.xml\",\n ]\n )\n\n for candidate in candidates:\n if candidate.exists():\n return str(candidate)\n\n raise FileNotFoundError(\n \"Cannot find haarcascade_frontalface_alt2.xml. \"\n \"Download it from https:\/\/raw.githubusercontent.com\/opencv\/opencv\/4.x\/data\/haarcascades\/haarcascade_frontalface_alt2.xml \"\n \"and place it next to demo.py.\"\n )\n\n\ndef main() -> None:\n base_dir = Path(__file__).resolve().parent\n image_path = base_dir \/ \"face9.jpeg\"\n model_path = base_dir \/ \"model\" \/ \"lbfmodel.yaml\"\n output_path = base_dir \/ \"face9_landmarks.jpg\"\n\n image = cv2.imread(str(image_path))\n if image is None:\n raise FileNotFoundError(f\"Cannot read image: {image_path}\")\n\n gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n\n cascade_path = resolve_haarcascade(base_dir)\n detector = cv2.CascadeClassifier(cascade_path)\n if detector.empty():\n raise RuntimeError(f\"Failed to load Haar cascade: {cascade_path}\")\n\n faces = detector.detectMultiScale(\n gray,\n scaleFactor=1.1,\n minNeighbors=5,\n minSize=(80, 80),\n )\n if len(faces) == 0:\n raise RuntimeError(\"No face detected in face4.jpeg\")\n\n facemark = cv2.face.createFacemarkLBF()\n facemark.loadModel(str(model_path))\n\n ok, landmarks = facemark.fit(gray, faces)\n if not ok:\n raise RuntimeError(\"Facemark fitting failed\")\n\n for (x, y, w, h), face_landmarks in zip(faces, landmarks):\n cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)\n # for px, py in face_landmarks[0]:\n # cv2.circle(image, (int(px), int(py)), 2, (0, 0, 255), -1)\n for i, (px, py) in enumerate(face_landmarks[0]):\n x, y = int(px), int(py)\n cv2.circle(image, (x, y), 2, (0, 0, 255), -1)\n cv2.putText(image, str(i), (x + 2, y - 2),\n cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1)\n\n cv2.imwrite(str(output_path), image)\n\n print(f\"Detected {len(faces)} face(s)\")\n print(f\"Saved result to: {output_path}\")\n\n\nif __name__ == \"__main__\":\n main()\n\n<\/code><\/pre>\n\n\n\n\n- \u8fd0\u884c\u7a0b\u5e8f\u3002
\u5728\u865a\u62df\u73af\u5883\u5df2\u7ecf\u6fc0\u6d3b\u7684\u60c5\u51b5\u4e0b\uff08\u5982\u672a\u6fc0\u6d3b\uff0c\u5219\u9700\u8981\u6267\u884c\u4e0a\u9762\u63d0\u53ca\u7684source\u547d\u4ee4\uff09\uff0c\u5728\u7ec8\u7aef\u6216\u547d\u4ee4\u884c\u4e2d\uff0c\u5728\u9879\u76ee\u7684\u6839\u76ee\u5f55\u4e0b\uff0c\u6267\u884c\uff1apython demo.py
\u76ee\u5f55\u4e0b\u4f1a\u751f\u6210\u4e00\u5f20\u6807\u8bb0\u4eba\u8138\u53ca\u4eba\u8138\u5173\u952e\u70b9\u7684\u56fe\u7247\u3002\u4eba\u8138\u7528\u7eff\u8272\u65b9\u6846\u6807\u51fa\uff0c\u4eba\u8138\u5173\u952e\u70b9\u7528\u7ea2\u8272\u5706\u70b9\u6807\u51fa\uff0c\u6bcf\u4e2a\u5173\u952e\u70b9\u4f1a\u6807\u51fa\u5e8f\u53f7\uff0c\u4ece0\uff5e67\u3002\u56e0\u4e3a\u6211\u4eec\u4f7f\u7528\u7684\u662f\u4eba\u813868\u70b9\u6807\u8bb0\u3002<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"\u4f7f\u7528opencv haar cascades\u7ea7\u8054\u68c0\u6d4b\u5668\u8bc6\u522b\u4eba\u8138\uff0c\u4f7f\u7528opencv … \u7ee7\u7eed\u9605\u8bfb