insightface

import cv2 import numpy as np import insightface from insightface.app import FaceAnalysis from insightface.data import get_image as ins_get_image # Initialize FaceAnalysis app with desired providers (e.g., CUDA or CPU) # Ensure onnxruntime-gpu is installed for CUDAExecutionProvider app = FaceAnalysis(providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) app.prepare(ctx_id=0, det_size=(640, 640)) # Load a sample image img = ins_get_image('t1') # 't1' is a sample image bundled with the library # Perform face detection and analysis faces = app.get(img) # Draw results on the image rimg = app.draw_on(img, faces) # Display or save the output image # cv2.imshow("InsightFace Demo", rimg) # cv2.waitKey(0) # cv2.destroyAllWindows() cv2.imwrite("./t1_output.jpg", rimg) print(f"Detected {len(faces)} face(s). Output saved to t1_output.jpg") if faces: face = faces[0] print(f"Face 0: Bounding Box: {face.bbox}, Landmark: {face.landmark}") if face.embedding is not None: print(f"Face 0: Embedding shape: {face.embedding.shape}") if face.gender is not None: print(f"Face 0: Gender: {face.gender}, Age: {face.age}")