Upload folder using huggingface_hub

.gitattributes

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.gitignore

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+# Exclude virtual environment directories
+.venv/
+venv/
+env/
+
+# Exclude IDE settings, temporary files, and OS-specific files
+.idea/
+.vscode/
+*.swp
+.DS_Store
+__pycache__/
+
+# Optionally exclude large data files, logs, etc.
+*.log

.python-version

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+3.12

README.md

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 ---
-title: Javelin APP
-emoji: 🏃
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 5.25.0
+title: Javelin_APP
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 5.23.3
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Javelin_detector-
+# Javelin_APP

app.py

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+import cv2
+import mediapipe as mp
+import torch
+import numpy as np
+import math
+import tempfile
+import os
+import gradio as gr
+
+# -----------------------------
+# Configuration & Initialization
+# -----------------------------
+
+# Initialize MediaPipe Pose and drawing utilities.
+mp_pose = mp.solutions.pose
+mp_drawing = mp.solutions.drawing_utils
+
+# Load your local YOLOv5 model for javelin detection.
+# Adjust these paths as needed.
+model = torch.hub.load('/Users/user/yolov5', 'custom',
+                       path='/Users/user/yolov5/runs/train/exp/weights/best.pt',
+                       source='local')
+model.conf = 0.5  # detection confidence threshold
+
+
+# -----------------------------
+# Helper: Determine fixed orientation from first frame.
+# -----------------------------
+def determine_orientation(cap, pose):
+    # Read the first frame.
+    ret, frame = cap.read()
+    if not ret:
+        return "ltr"  # default if no frame
+    rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    results = pose.process(rgb_frame)
+    orientation = "ltr"  # default: use top-left -> bottom-right
+    if results.pose_landmarks:
+        landmarks = results.pose_landmarks.landmark
+        left_shoulder = landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value]
+        right_shoulder = landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value]
+        if left_shoulder.x < right_shoulder.x:
+            orientation = "ltr"  # left-to-right
+        else:
+            orientation = "rtl"  # right-to-left
+    # Reset video to the first frame.
+    cap.set(cv2.CAP_PROP_POS_FRAMES, 0)
+    return orientation
+
+
+# -----------------------------
+# Helper: Compute dominant line in ROI via Hough Transform.
+# -----------------------------
+def get_dominant_line_angle(roi, debug=False):
+    """
+    Process an ROI (BGR image) to extract edges and run HoughLinesP to get the longest line.
+    Returns the angle (in degrees, normalized to [0,180]) and the line coordinates.
+    """
+    gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
+    edges = cv2.Canny(gray, 50, 150)
+    if debug:
+        cv2.imshow('ROI Edges', edges)
+        cv2.waitKey(1)
+
+    diag = math.hypot(roi.shape[0], roi.shape[1])
+    min_line_length = int(0.4 * diag)
+    lines = cv2.HoughLinesP(edges, rho=1, theta=np.pi / 180, threshold=25,
+                            minLineLength=min_line_length, maxLineGap=15)
+    if lines is None:
+        return None, None
+
+    longest_length = 0
+    best_line = None
+    for line in lines:
+        x1, y1, x2, y2 = line[0]
+        length = math.hypot(x2 - x1, y2 - y1)
+        if length > longest_length:
+            longest_length = length
+            best_line = (x1, y1, x2, y2)
+    if best_line is None:
+        return None, None
+
+    # Calculate the angle relative to horizontal.
+    x1, y1, x2, y2 = best_line
+    angle_rad = math.atan2(y2 - y1, x2 - x1)
+    angle_deg = abs(math.degrees(angle_rad)) % 180
+    return angle_deg, best_line
+
+
+# -----------------------------
+# Main Processing Function with Fixed Orientation and Side Display
+# -----------------------------
+def process_video(video_file):
+    """
+    Processes each frame:
+      - Runs MediaPipe Pose detection and draws the pose.
+      - Runs YOLOv5 detection for the javelin.
+      - Draws a rectangle around the detected javelin.
+      - Based on a fixed decision from the first frame, draws a line across the detection box.
+      - Displays the line's angle above the box and on the left side of the frame.
+    Returns the path to the output video.
+    """
+    cap = cv2.VideoCapture(video_file)
+    if not cap.isOpened():
+        return "Error: Could not open video."
+
+    fps = cap.get(cv2.CAP_PROP_FPS)
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+    # Create a temporary output file.
+    temp_output = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
+    output_path = temp_output.name
+    temp_output.close()
+
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    writer = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
+
+    # Initialize MediaPipe Pose.
+    with mp_pose.Pose(min_detection_confidence=0.5,
+                      min_tracking_confidence=0.5) as pose:
+
+        # Decide fixed orientation based on the first frame.
+        orientation = determine_orientation(cap, pose)
+        print(f"Fixed orientation: {orientation}")
+
+        while cap.isOpened():
+            ret, frame = cap.read()
+            if not ret:
+                break
+
+            rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            pose_results = pose.process(rgb_frame)
+            annotated_frame = frame.copy()
+
+            # Draw pose landmarks.
+            if pose_results.pose_landmarks:
+                mp_drawing.draw_landmarks(annotated_frame,
+                                          pose_results.pose_landmarks,
+                                          mp_pose.POSE_CONNECTIONS)
+
+            # Run YOLOv5 detection for the javelin.
+            results = model([rgb_frame])
+            detections = results.xyxy[0].cpu().numpy()
+
+            if len(detections) > 0:
+                # Use the highest confidence detection.
+                detections = sorted(detections, key=lambda x: -x[4])
+                det = detections[0]
+                x1, y1, x2, y2, conf, cls = det
+                x1, y1, x2, y2 = map(int, (x1, y1, x2, y2))
+                cv2.rectangle(annotated_frame, (x1, y1), (x2, y2), (0, 255, 255), 2)
+
+                # Define the four corners of the bounding box.
+                top_left = (x1, y1)
+                bottom_right = (x2, y2)
+                bottom_left = (x1, y2)
+                top_right = (x2, y1)
+
+                # Decide which diagonal to use based on the fixed orientation.
+                if orientation == "ltr":
+                    line_start, line_end = top_left, bottom_right
+                else:
+                    line_start, line_end = bottom_left, top_right
+
+                # Draw the javelin line (with increased thickness if desired).
+                cv2.line(annotated_frame, line_start, line_end, (0, 0, 255), 4)
+
+                # Calculate the angle of the line.
+                angle_rad = math.atan2(line_end[1] - line_start[1], line_end[0] - line_start[0])
+                angle_deg = abs(math.degrees(angle_rad)) % 180
+
+                # Display the angle above the box.
+                cv2.putText(annotated_frame, f"Angle: {angle_deg:.1f} deg",
+                            (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)
+
+                # Also display the angle on the left side of the frame.
+                cv2.putText(annotated_frame, f"Angle: {angle_deg:.1f} deg",
+                            (10, height // 2), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 255, 0), 2)
+            else:
+                cv2.putText(annotated_frame, "No javelin detected", (10, 30),
+                            cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 255), 2)
+
+            writer.write(annotated_frame)
+
+    cap.release()
+    writer.release()
+    return output_path
+
+
+# -----------------------------
+# Create Gradio Interface -
+# -----------------------------
+iface = gr.Interface(
+    fn=process_video,
+    inputs=gr.Video(label="Upload Video"),
+    outputs=gr.Video(label="Processed Video"),
+    title="Javelin Detector with Pose & Angle",
+    description=("Upload a video to see pose landmarks, a javelin detection box, and a "
+                 "diagonal line drawn across the box with its angle (displayed above the box and on the side).")
+)
+
+if __name__ == '__main__':
+    iface.launch()

requirements.txt

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+absl-py==2.2.2
+aiofiles==23.2.1
+annotated-types==0.7.0
+anyio==4.9.0
+attrs==25.3.0
+blinker==1.9.0
+certifi==2025.1.31
+cffi==1.17.1
+charset-normalizer==3.4.1
+click==8.1.8
+contourpy==1.3.1
+cycler==0.12.1
+fastapi==0.115.12
+ffmpy==0.5.0
+filelock==3.18.0
+Flask==3.1.0
+flatbuffers==25.2.10
+fonttools==4.57.0
+fsspec==2025.3.2
+gitdb==4.0.12
+GitPython==3.1.44
+gradio==5.23.3
+gradio_client==1.8.0
+groovy==0.1.2
+h11==0.14.0
+httpcore==1.0.7
+httpx==0.28.1
+huggingface-hub==0.30.1
+idna==3.10
+itsdangerous==2.2.0
+jax==0.5.3
+jaxlib==0.5.3
+Jinja2==3.1.6
+kiwisolver==1.4.8
+markdown-it-py==3.0.0
+MarkupSafe==3.0.2
+matplotlib==3.10.1
+mdurl==0.1.2
+mediapipe==0.10.21
+ml_dtypes==0.5.1
+mpmath==1.3.0
+networkx==3.4.2
+numpy==1.26.4
+opencv-contrib-python==4.11.0.86
+opencv-python==4.11.0.86
+opt_einsum==3.4.0
+orjson==3.10.16
+packaging==24.2
+pandas==2.2.3
+pillow==11.1.0
+protobuf==4.25.6
+psutil==7.0.0
+py-cpuinfo==9.0.0
+pycparser==2.22
+pydantic==2.11.2
+pydantic_core==2.33.1
+pydub==0.25.1
+Pygments==2.19.1
+pyparsing==3.2.3
+python-dateutil==2.9.0.post0
+python-multipart==0.0.20
+pytz==2025.2
+PyYAML==6.0.2
+requests==2.32.3
+rich==14.0.0
+ruff==0.11.4
+safehttpx==0.1.6
+scipy==1.15.2
+seaborn==0.13.2
+semantic-version==2.10.0
+sentencepiece==0.2.0
+setuptools==78.1.0
+shellingham==1.5.4
+six==1.17.0
+smmap==5.0.2
+sniffio==1.3.1
+sounddevice==0.5.1
+starlette==0.46.1
+sympy==1.13.1
+tomlkit==0.13.2
+torch==2.6.0
+torchvision==0.21.0
+tqdm==4.67.1
+typer==0.15.2
+typing-inspection==0.4.0
+typing_extensions==4.13.1
+tzdata==2025.2
+ultralytics==8.3.102
+ultralytics-thop==2.0.14
+urllib3==2.3.0
+uvicorn==0.34.0
+websockets==15.0.1
+Werkzeug==3.1.3

weights/best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e5cd9fb216c13fe4fb45cd7e252203e80de5c3048ce15ff08a762b131bd86460
+size 14359336