Duplicate from datasciencedojo/Hand-Keypoint-Detection-Realtime

Co-authored-by: Data Science Dojo <[email protected]>

.gitattributes

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README.md

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+---
+title: Hand Keypoint Detection Realtime
+emoji: 🔥
+colorFrom: pink
+colorTo: blue
+sdk: gradio
+sdk_version: 3.4.1
+app_file: app.py
+pinned: false
+duplicated_from: datasciencedojo/Hand-Keypoint-Detection-Realtime
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import cv2
+import gradio as gr
+import mediapipe as mp
+mp_drawing = mp.solutions.drawing_utils
+mp_drawing_styles = mp.solutions.drawing_styles
+mp_hands = mp.solutions.hands
+
+def fun(img):
+  print(type(img))
+  with mp_hands.Hands( model_complexity=0,min_detection_confidence=0.5,min_tracking_confidence=0.5) as hands:
+    img.flags.writeable = False
+    image = cv2.flip(img[:,:,::-1], 1)
+    # Convert the BGR image to RGB before processing.
+    results = hands.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+    image.flags.writeable = True
+    if results.multi_hand_landmarks:
+      for hand_landmarks in results.multi_hand_landmarks:
+        mp_drawing.draw_landmarks(
+            image,
+            hand_landmarks,
+            mp_hands.HAND_CONNECTIONS,
+            mp_drawing_styles.get_default_hand_landmarks_style(),
+            mp_drawing_styles.get_default_hand_connections_style())
+  
+    return cv2.flip(image[:,:,::-1],1)
+    
+with gr.Blocks(title="Realtime Keypoint Detection | Data Science Dojo", css="footer {display:none !important} .output-markdown{display:none !important}") as demo:
+
+    with gr.Row():
+      with gr.Column():
+        input = gr.Webcam(streaming=True)
+
+      with gr.Column():
+        output = gr.outputs.Image()
+
+
+    input.stream(fn=fun,
+        inputs = input,
+        outputs = output)
+
+ 
+
+demo.launch(debug=True)

requirements.txt

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+mediapipe