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| import gradio as gr | |
| import mediapipe as mp | |
| import numpy as np | |
| import cv2 | |
| title = "Hugging Face Me" | |
| description = " Demo for overlaying the Hugging Face logo on your face using the Mediapipe Face Detection model." | |
| article = "<p style='text-align: center'><a href='https://google.github.io/mediapipe/solutions/face_detection.html' target='_blank'>Mediapipe Face Detection</a> | <a href='https://github.com/google/mediapipe' target='_blank'>Github Repo</a></p>" | |
| mp_face_detection = mp.solutions.face_detection | |
| mp_drawing = mp.solutions.drawing_utils | |
| def draw_huggingfaces(image, results): | |
| height, width, _ = image.shape | |
| output_img = image.copy() | |
| if results.detections: | |
| for detection in results.detections: | |
| face_coordinates = np.array([[detection.location_data.relative_keypoints[i].x*width, | |
| detection.location_data.relative_keypoints[i].y*height] | |
| for i in [0,1,3]], dtype=np.float32) | |
| M = cv2.getAffineTransform(huggingface_landmarks, face_coordinates) | |
| transformed_huggingface = cv2.warpAffine(huggingface_image, M, (width, height)) | |
| transformed_huggingface_mask = transformed_huggingface[:,:,3] != 0 | |
| output_img[transformed_huggingface_mask] = transformed_huggingface[transformed_huggingface_mask,:3] | |
| return output_img | |
| def huggingface_me(image): | |
| with mp_face_detection.FaceDetection( | |
| model_selection=0, | |
| min_detection_confidence=0.5) as face_detection: | |
| # Convert the BGR image to RGB and process it with MediaPipe Face Mesh. | |
| results = face_detection.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) | |
| return draw_huggingfaces(image, results) | |
| # Load hugging face logo and landmark coordinates | |
| huggingface_image = cv2.imread("images/hugging-face.png", cv2.IMREAD_UNCHANGED) | |
| huggingface_image = cv2.cvtColor(huggingface_image, cv2.COLOR_BGRA2RGBA) | |
| huggingface_landmarks = np.array([[747,697],[1289,697],[1022,1116]], dtype=np.float32) | |
| webcam_image = gr.inputs.Image(label="Input Image", source="webcam") | |
| output_image = gr.outputs.Image(label="Output Image") | |
| gr.Interface(huggingface_me, | |
| live=True, | |
| inputs=webcam_image, | |
| outputs=output_image, | |
| title=title, | |
| description=description, | |
| article=article, ).launch() | |