Update app.py

app.py

@@ -1,159 +1,83 @@
 import gradio as gr
 from ultralytics import YOLO
-from PIL import Image
 import cv2
 import numpy as np
 import tempfile
-import os
-from pathlib import Path
+from PIL import Image
+from collections import Counter
 
-# Load the YOLOv8 model
+# تحميل نموذج YOLOv8
 model = YOLO('yolov8n.pt')
 
 def process_image(image):
-    """
-    Process a single image for object detection
-    """
+    """ معالجة الصور للكشف عن الكائنات """
     results = model(image)
-    # Get detection information
     boxes = results[0].boxes
-    detection_info = []
-    for box in boxes:
-        class_id = int(box.cls[0])
-        class_name = results[0].names[class_id]
-        confidence = float(box.conf[0])
-        detection_info.append(f"{class_name}: {confidence:.2%}")
-    
+    detection_info = [f"{results[0].names[int(box.cls[0])]}: {float(box.conf[0]):.2%}" for box in boxes]
     return Image.fromarray(results[0].plot()), "\n".join(detection_info)
 
 def process_video(video_path):
-    """
-    Process video for object detection
-    """
-    with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as temp_file:
-        output_path = temp_file.name
+    """ معالجة الفيديو للكشف عن الكائنات """
+    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.mp4')
+    output_path = temp_file.name
 
     cap = cv2.VideoCapture(video_path)
-    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-    fps = int(cap.get(cv2.CAP_PROP_FPS))
-    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-    
-    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-    out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
+    width, height = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    fps, total_frames = int(cap.get(cv2.CAP_PROP_FPS)), int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
     
+    out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (width, height))
     detection_summary = []
-    frame_count = 0
     
-    try:
-        while cap.isOpened():
-            ret, frame = cap.read()
-            if not ret:
-                break
-                
-            frame_count += 1
-            results = model(frame)
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
             
-            # Collect detection information for this frame
-            if frame_count % int(fps) == 0:  # Sample every second
-                for box in results[0].boxes:
-                    class_id = int(box.cls[0])
-                    class_name = results[0].names[class_id]
-                    detection_summary.append(class_name)
-            
-            annotated_frame = results[0].plot()
-            out.write(annotated_frame)
-            
-    finally:
-        cap.release()
-        out.release()
-    
-    # Create summary of detected objects
-    if detection_summary:
-        from collections import Counter
-        counts = Counter(detection_summary)
-        summary = "\n".join([f"{obj}: {count} occurrences" for obj, count in counts.most_common()])
-    else:
-        summary = "No objects detected"
-    
+        results = model(frame)
+        if int(cap.get(cv2.CAP_PROP_POS_FRAMES)) % int(fps) == 0:
+            detection_summary.extend([results[0].names[int(box.cls[0])] for box in results[0].boxes])
+
+        out.write(results[0].plot())
+
+    cap.release()
+    out.release()
+
+    summary = "\n".join([f"{obj}: {count} occurrences" for obj, count in Counter(detection_summary).most_common()]) if detection_summary else "No objects detected"
     return output_path, summary
 
 def detect_objects(media):
-    """
-    Unified function to handle both image and video inputs
-    """
+    """ دالة موحدة للتعامل مع الصور والفيديو """
     if media is None:
-        return None, None, None, "Please upload an image or video to begin detection.", gr.update(visible=True), gr.update(visible=False)
-    
+        return None, None, "Please upload an image or video.", gr.update(visible=True), gr.update(visible=False)
+
     try:
         if isinstance(media, str) and media.lower().endswith(('.mp4', '.avi', '.mov')):
             output_video, detection_summary = process_video(media)
-            return (None, output_video, detection_summary,
-                    "✅ Video processing complete! Check the detection summary below.", 
-                    gr.update(visible=False), gr.update(visible=True))
+            return None, output_video, detection_summary, "✅ Video processing complete!", gr.update(visible=False), gr.update(visible=True)
         else:
-            if isinstance(media, str):
-                image = cv2.imread(media)
-                image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-            else:
-                image = media
+            image = cv2.imread(media) if isinstance(media, str) else media
+            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
             processed_image, detection_info = process_image(image)
-            return (processed_image, None, detection_info,
-                    "✅ Image processing complete! Check the detections below.", 
-                    gr.update(visible=True), gr.update(visible=False))
+            return processed_image, None, detection_info, "✅ Image processing complete!", gr.update(visible=True), gr.update(visible=False)
     except Exception as e:
-        return None, None, None, f"❌ Error: {str(e)}", gr.update(visible=False), gr.update(visible=False)
+        return None, None, f"❌ Error: {str(e)}", gr.update(visible=False), gr.update(visible=False)
 
+# تصميم الواجهة باستخدام Gradio
+with gr.Blocks() as demo:
+    gr.Markdown("# 🔍 Object Detection")
 
-# Create Gradio interface
-with gr.Blocks(css=custom_css) as demo:
-    with gr.Column(elem_id="app-container"):
-       
-        gr.Markdown("# 🔍 Object Detection")
-        
-        # Upload Section
-        with gr.Column(elem_classes="upload-box"):
-            gr.Markdown("### 📤 Upload your file")
-            input_media = gr.File(
-                label="Drag and drop or click to upload (Images: jpg, jpeg, png | Videos: mp4, avi, mov)",
-                file_types=["image", "video"]
-            )
-        
-        # Status Message
-        status_text = gr.Textbox(
-            label="Status",
-            value="Waiting for upload...",
-            interactive=False
-        )
-        
-        # Detection Information
-        detection_info = gr.Textbox(
-            label="Detection Results",
-            elem_classes="detection-info",
-            interactive=False
-        )
-        
-        # Results Section
-        with gr.Column(elem_classes="results-container"):
-            with gr.Row():
-                with gr.Column(visible=False) as image_column:
-                    output_image = gr.Image(label="Detected Objects")
-                with gr.Column(visible=False) as video_column:
-                    output_video = gr.Video(label="Processed Video")
-    
-        # Handle file upload
-        input_media.upload(
-            fn=detect_objects,
-            inputs=[input_media],
-            outputs=[
-                output_image,
-                output_video,
-                detection_info,
-                status_text,
-                image_column,
-                video_column
-            ]
-        )
+    input_media = gr.File(label="Upload Image/Video (jpg, png, mp4, avi)", file_types=["image", "video"])
+    status_text = gr.Textbox(label="Status", value="Waiting for upload...", interactive=False)
+    detection_info = gr.Textbox(label="Detection Results", interactive=False)
+
+    with gr.Row():
+        with gr.Column(visible=False) as image_column:
+            output_image = gr.Image(label="Detected Objects")
+        with gr.Column(visible=False) as video_column:
+            output_video = gr.Video(label="Processed Video")
+
+    input_media.upload(detect_objects, inputs=[input_media], outputs=[output_image, output_video, detection_info, status_text, image_column, video_column])
 
+# تشغيل التطبيق
 if __name__ == "__main__":
-    demo.launch(share=True)
+    demo.launch(share=True)