| import gradio as gr |
| import pandas as pd |
| import os |
| import cv2 |
| import tempfile |
|
|
| |
| GT_FILE = "data/train_logs.csv" |
| LEADERBOARD_FILE = "leaderboard.csv" |
|
|
| |
| if os.path.exists(GT_FILE): |
| gt_df = pd.read_csv(GT_FILE) |
| else: |
| gt_df = pd.DataFrame(columns=["frame", "lens"]) |
|
|
| |
| if not os.path.exists(LEADERBOARD_FILE): |
| pd.DataFrame(columns=["Name", "Score"]).to_csv(LEADERBOARD_FILE, index=False) |
|
|
| |
| |
| def detect_lens_changes(video_path): |
| cap = cv2.VideoCapture(video_path) |
| total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) |
| preds = pd.DataFrame({"frame": range(total_frames), "lens": ["A"]*total_frames}) |
| cap.release() |
| return preds |
|
|
| |
| def evaluate_video(video_file, name): |
| try: |
| tmp_video = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") |
| tmp_video.write(video_file.read()) |
| tmp_video.close() |
|
|
| pred_df = detect_lens_changes(tmp_video.name) |
|
|
| merged = pd.merge(gt_df, pred_df, on="frame", how="left") |
| if merged.empty or "lens_x" not in merged.columns or "lens_y" not in merged.columns: |
| return "Error: Ground truth or prediction missing columns.", pd.read_csv(LEADERBOARD_FILE) |
|
|
| merged.fillna("", inplace=True) |
| score = (merged["lens_x"] == merged["lens_y"]).mean() |
| score = round(float(score), 4) |
|
|
| |
| lb_df = pd.read_csv(LEADERBOARD_FILE) |
| lb_df.loc[len(lb_df)] = [name, score] |
| lb_df = lb_df.sort_values("Score", ascending=False).reset_index(drop=True) |
| lb_df.to_csv(LEADERBOARD_FILE, index=False) |
|
|
| return f"Your Score: {score}", lb_df |
|
|
| except Exception as e: |
| return f"Error: {str(e)}", pd.read_csv(LEADERBOARD_FILE) |
|
|
| |
| instructions = """ |
| # 🏆 Video Lens Detection Hackathon |
| |
| **Instructions:** |
| 1. Download dataset from `data/` folder. |
| 2. Videos contain eye test recordings. |
| 3. Train your model locally to detect lens changes per frame. |
| 4. Upload your video predictions here. |
| 5. Enter your name to appear on the leaderboard. |
| """ |
|
|
| |
| with gr.Blocks() as demo: |
| gr.Markdown(instructions) |
|
|
| with gr.Row(): |
| name_input = gr.Textbox(label="Your Name") |
| video_input = gr.File(label="Upload your video", file_types=[".mp4"]) |
|
|
| output_text = gr.Textbox(label="Score / Error", interactive=False) |
| leaderboard = gr.Dataframe(pd.read_csv(LEADERBOARD_FILE), label="Leaderboard") |
|
|
| submit_btn = gr.Button("Submit") |
| submit_btn.click( |
| fn=evaluate_video, |
| inputs=[video_input, name_input], |
| outputs=[output_text, leaderboard] |
| ) |
|
|
| demo.launch() |
|
|
