Initial commit

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README.md

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+---
+title: Data Sains Sawit Counting
+emoji: 🏃
+colorFrom: indigo
+colorTo: purple
+sdk: gradio
+sdk_version: 6.0.1
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import cv2
+from ultralytics import YOLO
+import os
+
+MODEL_PATH = "best.pt" 
+TRACKER_FILE = "my_tracker.yaml"
+FOOTAGE_EXAMPLE_PATH = "drone_footage.mp4"
+
+tracker_config = """
+tracker_type: bytetrack
+track_high_thresh: 0
+track_low_thresh: 0
+track_buffer: 300
+fuse_score: True
+match_thresh: 0.9
+new_track_thresh: 0.85
+"""
+with open(TRACKER_FILE, "w") as f:
+    f.write(tracker_config)
+
+model = YOLO(MODEL_PATH)
+
+def process_video(video_path, conf_threshold, iou_threshold):
+    if video_path is None:
+        return None
+
+    MIN_FRAMES_TO_COUNT = 60
+    class_names = model.names
+    
+    track_history = {}
+    class_counts = {name: 0 for name in class_names.values()}
+    stable_counted_ids = set()
+
+    cap = cv2.VideoCapture(video_path)
+    w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    fps = cap.get(cv2.CAP_PROP_FPS)
+    
+    output_path = "output_counted.mp4"
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    out = cv2.VideoWriter(output_path, fourcc, fps, (w, h))
+    
+    print("Processing video...")
+
+    while cap.isOpened():
+        success, frame = cap.read()
+        if not success:
+            break
+            
+        results = model.track(
+            frame, 
+            persist=True, 
+            verbose=False, 
+            tracker=TRACKER_FILE,
+            conf=conf_threshold,
+            iou=iou_threshold
+        )
+        
+        annotated_frame = results[0].plot(line_width=2, font_size=1)
+        
+        if results[0].boxes.id is not None:
+            track_ids = results[0].boxes.id.int().tolist()
+            class_indices = results[0].boxes.cls.int().tolist()
+            
+            for track_id, cls_index in zip(track_ids, class_indices):
+                class_name = class_names[cls_index]
+
+                if track_id not in track_history:
+                    track_history[track_id] = {
+                        'frame_count': 1,
+                        'class_votes': {class_name: 1}
+                    }
+                else:
+                    track_history[track_id]['frame_count'] += 1
+                    votes = track_history[track_id]['class_votes']
+                    votes[class_name] = votes.get(class_name, 0) + 1
+
+                if track_history[track_id]['frame_count'] >= MIN_FRAMES_TO_COUNT and track_id not in stable_counted_ids:
+                    stable_counted_ids.add(track_id) 
+
+                    votes = track_history[track_id]['class_votes']
+                    stable_class = max(votes, key=votes.get) 
+                    
+                    class_counts[stable_class] += 1
+
+        total_stable_count = len(stable_counted_ids)
+        
+        text_lines = [f'Total FFBs Counted: {total_stable_count}']
+        for class_name, count in class_counts.items():
+            if count > 0:
+                text_lines.append(f'{class_name}: {count}')
+
+        font_scale = 1.0
+        thickness = 2
+        font = cv2.FONT_HERSHEY_SIMPLEX
+        
+        (text_w, text_h), _ = cv2.getTextSize('Test', font, font_scale, thickness)
+        line_height = text_h + 10
+        
+        x_pos, y_pos = 10, 10
+        
+        max_line_w = 0
+        for line in text_lines:
+            (line_w, _), _ = cv2.getTextSize(line, font, font_scale, thickness)
+            if line_w > max_line_w:
+                max_line_w = line_w
+
+        total_block_h = 10 + (line_height * len(text_lines)) - 5 
+        total_block_w = 10 + max_line_w + 10
+        
+        cv2.rectangle(annotated_frame, (x_pos, y_pos), (total_block_w, total_block_h), (0, 0, 0), -1)
+
+        current_y = y_pos + text_h + 5
+        for line in text_lines:
+            cv2.putText(annotated_frame, line, (x_pos + 5, current_y), font, font_scale, (255, 255, 255), thickness)
+            current_y += line_height
+                    
+        out.write(annotated_frame)
+
+    cap.release()
+    out.release()
+    
+    print(f"Final Count: {len(stable_counted_ids)}")
+    print(f"Class Counts: {class_counts}")
+
+    final_output_path = "final_web_ready.mp4"
+    os.system(f"ffmpeg -y -i {output_path} -vcodec libx264 {final_output_path}")
+    
+    return final_output_path
+
+
+description_html = """
+<p>Upload a video **(preferably drone footage)** showing Oil Palm Fresh Fruit Bunches (FFB). The model will count the detected FFBs.</p>
+<h3>Demo Result:</h3>
+<div style="display: flex; justify-content: center;">
+    <video width="640" height="360" controls autoplay loop muted>
+        <source src="drone_footage_result.mp4" type="video/mp4">
+        Your browser does not support the video tag.
+    </video>
+</div>
+"""
+
+iface = gr.Interface(
+    fn=process_video,
+    inputs=[
+        gr.Video(label="Upload Video"),
+        gr.Slider(minimum=0.0, maximum=1.0, value=0.25, step=0.01, label="Confidence Threshold"),
+        gr.Slider(minimum=0.0, maximum=1.0, value=0.45, step=0.01, label="IoU Threshold"),
+    ],
+    outputs=gr.Video(label="Processed Result"),
+    title="Oil Palm Fresh Fruit Bunch Classification and Counter",
+    description=description_html,
+
+    # Drone Footage Example
+    examples=[
+        # Format: [Video_Path, Conf_Value, IoU_Value]
+        [FOOTAGE_EXAMPLE_PATH, 0.25, 0.45] 
+    ],
+    cache_examples=True
+)
+
+if __name__ == "__main__":
+    iface.launch(ssr_mode=False)

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3a6ab4f4b5411ed2e020f7e473d93c99c0a02ded67c59be9802cb05e67a56325
+size 5501267

drone_footage.mp4

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f9a2b945b5101929e3edf0d8568c617d2805ab24882fa61040f17e2c718f7be0
+size 131613067

drone_footage_result.mp4

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dec0f1a75f79184bbb706627aafab72363ff227b6edef30258d6672f7962ccad
+size 380493944

requirements.txt

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+ultralytics
+gradio
+opencv-python-headless
+pillow
+numpy<2