Create app.py

app.py

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+import gradio as gr
+import os
+import cv2
+import numpy as np
+from ultralytics import YOLO
+from tqdm import tqdm
+
+# Pre‐load models from your repo root
+extract_model = YOLO("best.pt")
+detect_model  = YOLO("yolov8n.pt")
+
+def process_video(video_path):
+    os.makedirs("frames", exist_ok=True)
+
+    # Step 1: Extract board‐only frames
+    cap = cv2.VideoCapture(video_path)
+    frames, idx = [], 0
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret: break
+        results = extract_model(frame)
+        labels = [extract_model.names[int(c)] for c in results[0].boxes.cls.cpu().numpy()]
+        if "board" in labels and "person" not in labels:
+            frames.append(frame)
+            cv2.imwrite(f"frames/frame_{idx:04d}.jpg", frame)
+        idx += 1
+    cap.release()
+    if not frames:
+        raise RuntimeError("No frames with only 'board' and no 'person' found.")
+
+    # Step 2: Align
+    def align_frames(ref, tgt):
+        orb = cv2.ORB_create(500)
+        k1, d1 = orb.detectAndCompute(ref, None)
+        k2, d2 = orb.detectAndCompute(tgt, None)
+        if d1 is None or d2 is None: return None
+        matcher = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
+        matches = matcher.match(d1, d2)
+        if len(matches) < 10: return None
+        src = np.float32([k2[m.trainIdx].pt for m in matches]).reshape(-1,1,2)
+        dst = np.float32([k1[m.queryIdx].pt for m in matches]).reshape(-1,1,2)
+        H, _ = cv2.findHomography(src, dst, cv2.RANSAC)
+        return None if H is None else cv2.warpPerspective(tgt, H, (ref.shape[1], ref.shape[0]))
+
+    base = frames[0]; aligned = [base]
+    for f in tqdm(frames[1:], desc="Aligning"):
+        a = align_frames(base, f)
+        if a is not None: aligned.append(a)
+    if not aligned:
+        raise RuntimeError("Alignment failed for all frames.")
+
+    # Step 3: Median‐fuse
+    stack = np.stack(aligned, axis=0).astype(np.float32)
+    median_board = np.median(stack, axis=0).astype(np.uint8)
+    cv2.imwrite("clean_board.jpg", median_board)
+
+    # Step 4: Mask persons & selective fuse
+    masks, sum_img = [], np.zeros_like(aligned[0], dtype=np.float32)
+    count = np.zeros(aligned[0].shape[:2], dtype=np.float32)
+    for f in tqdm(aligned, desc="Masking persons"):
+        res = detect_model(f, verbose=False)
+        m = np.zeros(f.shape[:2], dtype=np.uint8)
+        for box in res[0].boxes:
+            if detect_model.names[int(box.cls)] == "person":
+                x1,y1,x2,y2 = map(int, box.xyxy[0])
+                cv2.rectangle(m, (x1,y1), (x2,y2), 255, -1)
+        inv = cv2.bitwise_not(m)
+        masked = cv2.bitwise_and(f, f, mask=inv)
+        sum_img += masked.astype(np.float32)
+        count += (inv>0).astype(np.float32)
+
+    count[count==0] = 1
+    selective = (sum_img / count[:,:,None]).astype(np.uint8)
+    cv2.imwrite("fused_board_selective.jpg", selective)
+
+    # Step 5: Sharpen
+    blur  = cv2.GaussianBlur(selective, (5,5), 0)
+    sharp = cv2.addWeighted(selective, 1.5, blur, -0.5, 0)
+    cv2.imwrite("sharpened_board_color.jpg", sharp)
+
+    return "clean_board.jpg", "fused_board_selective.jpg", "sharpened_board_color.jpg"
+
+
+demo = gr.Interface(
+    fn=process_video,
+    inputs=[
+        gr.File(
+            label="Upload Classroom Video (.mp4)",
+            file_types=['.mp4'],
+            file_count="single",
+            type="filepath"
+        )
+    ],
+    outputs=[
+        gr.Image(label="Median-Fused Clean Board"),
+        gr.Image(label="Selective Fusion (No Persons)"), 
+        gr.Image(label="Sharpened Final Board")
+    ],
+    title="📹 Classroom Board Cleaner",
+    description=(
+        "1️⃣ Upload your classroom video (.mp4)\n"
+        "2️⃣ Automatic extraction, alignment, masking, fusion & sharpening\n"
+        "3️⃣ View three stages of the cleaned board output"
+    )
+)
+
+if __name__ == "__main__":
+    demo.launch()