initial commit

README.md

@@ -1,8 +1,8 @@
 ---
-title: Hugging Face Face
-emoji: 🔥
-colorFrom: pink
-colorTo: pink
+title: Hugging Face Me
+emoji: 🤗
+colorFrom: gray
+colorTo: indigo
 sdk: gradio
 app_file: app.py
 pinned: false

app.py

@@ -0,0 +1,58 @@
+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()
+

requirements.txt

@@ -0,0 +1,3 @@
+mediapipe
+numpy
+opencv-python-headless