Update app.py

app.py

@@ -62,4 +62,185 @@ def resize_frame(frame, width=None):
 # Function to process a batch of frames
 async def detect_faults_batch(frames, processor, model, device):
     try:
-        frames = [resize_frame(frame, resize
+        frames = [resize_frame(frame, resize_width) for frame in frames]
+        inputs = processor(images=frames, return_tensors="pt").to(device)
+        with torch.no_grad():
+            outputs = model(**inputs)
+        target_sizes = torch.tensor([frame.shape[:2] for frame in frames]).to(device)
+        results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)
+        
+        annotated_frames = []
+        all_faults = []
+        
+        for frame, result in zip(frames, results):
+            faults = {"Thermal Fault": False, "Dust Fault": False, "Power Generation Fault": False}
+            annotated_frame = frame.copy()
+            
+            for score, label, box in zip(result["scores"], result["labels"], result["boxes"]):
+                box = [int(i) for i in box.tolist()]
+                roi = frame[box[1]:box[3], box[0]:box[2]]
+                mean_intensity = np.mean(roi)
+                
+                if mean_intensity > 200:
+                    faults["Thermal Fault"] = True
+                    cv2.rectangle(annotated_frame, (box[0], box[1]), (box[2], box[3]), (255, 0, 0), 2)
+                    cv2.putText(annotated_frame, "Thermal Fault", (box[0], box[1]-10), 
+                               cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
+                elif mean_intensity < 100:
+                    faults["Dust Fault"] = True
+                    cv2.rectangle(annotated_frame, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 2)
+                    cv2.putText(annotated_frame, "Dust Fault", (box[0], box[1]-10), 
+                               cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+                
+                if faults["Thermal Fault"] or faults["Dust Fault"]:
+                    faults["Power Generation Fault"] = True
+            
+            annotated_frames.append(annotated_frame)
+            all_faults.append(faults)
+        
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        
+        return annotated_frames, all_faults
+    except Exception as e:
+        st.error(f"Error during fault detection: {str(e)}")
+        return [], []
+
+# Function to process video
+async def process_video(video_path, frame_skip, batch_size):
+    try:
+        cap = cv2.VideoCapture(video_path)
+        if not cap.isOpened():
+            st.error("Error: Could not open video file.")
+            return None, None
+        
+        frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        frame_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))
+        
+        out_width = resize_width if resize_width else frame_width
+        out_height = int(out_width * frame_height / frame_width) if resize_width else frame_height
+        
+        output_path = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False).name
+        fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+        out = cv2.VideoWriter(output_path, fourcc, fps, (out_width, out_height))
+        
+        video_faults = {"Thermal Fault": False, "Dust Fault": False, "Power Generation Fault": False}
+        frame_count = 0
+        frames_batch = []
+        processed_frames = 0
+        
+        with st.spinner("Analyzing video..."):
+            progress = st.progress(0)
+            executor = ThreadPoolExecutor(max_workers=2)
+            
+            while cap.isOpened():
+                ret, frame = cap.read()
+                if not ret:
+                    break
+                
+                if frame_count % frame_skip != 0:
+                    frame = resize_frame(frame, resize_width)
+                    out.write(frame)
+                    frame_count += 1
+                    processed_frames += 1
+                    progress.progress(min(processed_frames / total_frames, 1.0))
+                    continue
+                
+                frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                frames_batch.append(frame_rgb)
+                
+                if len(frames_batch) >= batch_size:
+                    annotated_frames, batch_faults = await detect_faults_batch(frames_batch, processor, model, device)
+                    for annotated_frame, faults in zip(annotated_frames, batch_faults):
+                        for fault in video_faults:
+                            video_faults[fault] |= faults[fault]
+                        annotated_frame_bgr = cv2.cvtColor(annotated_frame, cv2.COLOR_RGB2BGR)
+                        out.write(annotated_frame_bgr)
+                    
+                    frames_batch = []
+                    processed_frames += batch_size
+                    progress.progress(min(processed_frames / total_frames, 1.0))
+                
+                frame_count += 1
+            
+            if frames_batch:
+                annotated_frames, batch_faults = await detect_faults_batch(frames_batch, processor, model, device)
+                for annotated_frame, faults in zip(annotated_frames, batch_faults):
+                    for fault in video_faults:
+                        video_faults[fault] |= faults[fault]
+                    annotated_frame_bgr = cv2.cvtColor(annotated_frame, cv2.COLOR_RGB2BGR)
+                    out.write(annotated_frame_bgr)
+                
+                processed_frames += len(frames_batch)
+                progress.progress(min(processed_frames / total_frames, 1.0))
+        
+        cap.release()
+        out.release()
+        return output_path, video_faults
+    
+    except Exception as e:
+        st.error(f"Error processing video: {str(e)}")
+        return None, None
+    finally:
+        if 'cap' in locals() and cap.isOpened():
+            cap.release()
+        if 'out' in locals():
+            out.release()
+
+# File uploader
+uploaded_file = st.file_uploader("Upload a thermal video", type=["mp4"])
+
+if uploaded_file is not None:
+    try:
+        tfile = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
+        tfile.write(uploaded_file.read())
+        tfile.close()
+        
+        st.video(tfile.name, format="video/mp4")
+        
+        # Create a new event loop for Streamlit's ScriptRunner thread
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
+        try:
+            output_path, video_faults = loop.run_until_complete(process_video(tfile.name, frame_skip, batch_size))
+        finally:
+            loop.close()
+        
+        if output_path and video_faults:
+            st.subheader("Fault Detection Results")
+            st.video(output_path, format="video/mp4")
+            
+            st.write("**Detected Faults in Video:**")
+            for fault, detected in video_faults.items():
+                status = "Detected" if detected else "Not Detected"
+                color = "red" if detected else "green"
+                st.markdown(f"- **{fault}**: <span style='color:{color}'>{status}</span>", unsafe_allow_html=True)
+            
+            if any(video_faults.values()):
+                st.subheader("Recommendations")
+                if video_faults["Thermal Fault"]:
+                    st.write("- **Thermal Fault**: Inspect for damaged components or overheating issues.")
+                if video_faults["Dust Fault"]:
+                    st.write("- **Dust Fault**: Schedule cleaning to remove dust accumulation.")
+                if video_faults["Power Generation Fault"]:
+                    st.write("- **Power Generation Fault**: Investigate efficiency issues due to detected faults.")
+            else:
+                st.write("No faults detected. The solar panel appears to be functioning normally.")
+            
+            if os.path.exists(output_path):
+                os.unlink(output_path)
+        
+        if os.path.exists(tfile.name):
+            os.unlink(tfile.name)
+    
+    except Exception as e:
+        st.error(f"Error handling uploaded file: {str(e)}")
+    finally:
+        if 'tfile' in locals() and os.path.exists(tfile.name):
+            os.unlink(tfile.name)
+
+# Footer
+st.markdown("---")
+st.write("Built with Streamlit, Hugging Face Transformers, and OpenCV for Solar Panel Fault Detection PoC")