Delete app.py

app.py

@@ -1,106 +0,0 @@
-import cv2
-import mediapipe as mp
-import numpy as np
-import gradio as gr
-import tempfile
-
-# Load model
-MODEL_PATH = "hand_landmarker.task"
-
-BaseOptions = mp.tasks.BaseOptions
-HandLandmarker = mp.tasks.vision.HandLandmarker
-HandLandmarkerOptions = mp.tasks.vision.HandLandmarkerOptions
-VisionRunningMode = mp.tasks.vision.RunningMode
-mp_image = mp.Image
-mp_format = mp.ImageFormat
-
-# Finger connections and colors
-HAND_CONNECTIONS = [
-    (0, 1), (1, 2), (2, 3), (3, 4),
-    (0, 5), (5, 6), (6, 7), (7, 8),
-    (0, 9), (9,10), (10,11), (11,12),
-    (0,13), (13,14), (14,15), (15,16),
-    (0,17), (17,18), (18,19), (19,20)
-]
-
-FINGER_COLORS = {
-    'thumb': (245, 245, 245),
-    'index': (128, 0, 128),
-    'middle': (0, 255, 0),
-    'ring': (0, 165, 255),
-    'pinky': (255, 0, 0),
-    'palm': (100, 100, 100)
-}
-
-def get_finger_color(start_idx):
-    if start_idx in range(0, 5):
-        return FINGER_COLORS['thumb']
-    elif start_idx in range(5, 9):
-        return FINGER_COLORS['index']
-    elif start_idx in range(9, 13):
-        return FINGER_COLORS['middle']
-    elif start_idx in range(13, 17):
-        return FINGER_COLORS['ring']
-    elif start_idx in range(17, 21):
-        return FINGER_COLORS['pinky']
-    else:
-        return FINGER_COLORS['palm']
-
-def process_video(video_path):
-    cap = cv2.VideoCapture(video_path)
-
-    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-    tmp_out = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
-    out_path = tmp_out.name
-
-    fps = cap.get(cv2.CAP_PROP_FPS)
-    w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-    h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-    out = cv2.VideoWriter(out_path, fourcc, fps, (w, h))
-
-    options = HandLandmarkerOptions(
-        base_options=BaseOptions(model_asset_path=MODEL_PATH),
-        running_mode=VisionRunningMode.IMAGE,
-        num_hands=2,
-        min_hand_detection_confidence=0.5,
-        min_hand_presence_confidence=0.5,
-        min_tracking_confidence=0.5
-    )
-
-    with HandLandmarker.create_from_options(options) as landmarker:
-        while cap.isOpened():
-            ret, frame = cap.read()
-            if not ret:
-                break
-
-            rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-            mp_img = mp_image(image_format=mp_format.SRGB, data=rgb_frame)
-            results = landmarker.detect(mp_img)
-
-            if results.hand_landmarks:
-                for hand_landmarks in results.hand_landmarks:
-                    points = [(int(lm.x * w), int(lm.y * h)) for lm in hand_landmarks]
-
-                    for start, end in HAND_CONNECTIONS:
-                        color = get_finger_color(start)
-                        cv2.line(frame, points[start], points[end], color, 2)
-
-                    for i, (x, y) in enumerate(points):
-                        cv2.circle(frame, (x, y), 4, (0, 255, 255), -1)
-
-            out.write(frame)
-
-    cap.release()
-    out.release()
-    return out_path
-
-# Gradio interface
-demo = gr.Interface(
-    fn=process_video,
-    inputs=gr.Video(label="Upload Video or Use Webcam"),
-    outputs=gr.Video(label="Hand Landmark Annotated Video"),
-    title="Hand Detection ",
-    description="Upload a video or use webcam to detect hands."
-)
-
-demo.launch()