Update app.py

app.py

@@ -5,22 +5,19 @@ import numpy as np
 from ultralytics import YOLO
 from tqdm import tqdm
 
-# Pre-load models from the repo folder
+# Pre‐load models from your repo root
 extract_model = YOLO("best.pt")
 detect_model  = YOLO("yolov8n.pt")
 
 def process_video(video_path):
-    # Prepare output folder
     os.makedirs("frames", exist_ok=True)
 
-    # --- Step 1: Extract clean frames ---
+    # Step 1: Extract board‐only frames
     cap = cv2.VideoCapture(video_path)
-    frames = []
-    idx = 0
+    frames, idx = [], 0
     while cap.isOpened():
         ret, frame = cap.read()
-        if not ret:
-            break
+        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:
@@ -29,42 +26,37 @@ def process_video(video_path):
         idx += 1
     cap.release()
     if not frames:
-        raise RuntimeError("No frames found with only 'board' and no 'person'.")
+        raise RuntimeError("No frames with only 'board' and no 'person' found.")
 
-    # --- Step 2: Align frames ---
+    # 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
+        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
+        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)
-        if H is None:
-            return None
-        return cv2.warpPerspective(tgt, H, (ref.shape[1], ref.shape[0]))
+        return None if H is None else cv2.warpPerspective(tgt, H, (ref.shape[1], ref.shape[0]))
 
-    base = frames[0]
-    aligned = [base]
+    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 a is not None: aligned.append(a)
     if not aligned:
-        raise RuntimeError("No frames aligned successfully.")
+        raise RuntimeError("Alignment failed for all frames.")
 
-    # --- Step 3: Median fuse all aligned 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: Build person masks and selective fuse ---
-    masks = []
+    # 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)
@@ -72,43 +64,41 @@ def process_video(video_path):
             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)
-        masks.append(m)
-
-    sum_img = np.zeros_like(aligned[0], dtype=np.float32)
-    count   = np.zeros(aligned[0].shape[:2], dtype=np.float32)
-    for f, m in zip(aligned, masks):
-        inv    = cv2.bitwise_not(m)
+        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 += (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 final result ---
+    # 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.Video(
+        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="Selective Fusion (No Persons)"), 
         gr.Image(label="Sharpened Final Board")
     ],
     title="📹 Classroom Board Cleaner",
     description=(
-        "1️⃣ Upload your classroom video (no model upload needed)\n"
+        "1️⃣ Upload your classroom video (.mp4)\n"
         "2️⃣ Automatic extraction, alignment, masking, fusion & sharpening\n"
         "3️⃣ View three stages of the cleaned board output"
     )