Update app.py

app.py

@@ -1,16 +1,17 @@
 import streamlit as st
 import torch
 from transformers import DetrImageProcessor, DetrForObjectDetection
-from PIL import Image
-import numpy as np
 import cv2
+import numpy as np
+import tempfile
+import os
 
 # Set page configuration
 st.set_page_config(page_title="Solar Panel Fault Detection", layout="wide")
 
 # Title and description
 st.title("Solar Panel Fault Detection PoC")
-st.write("Upload a thermal image of a solar panel to detect thermal, dust, and power generation faults.")
+st.write("Upload a thermal video (MP4) of a solar panel to detect thermal, dust, and power generation faults.")
 
 # Load model and processor
 @st.cache_resource
@@ -21,92 +22,153 @@ def load_model():
 
 processor, model = load_model()
 
-# Function to process image and detect faults
-def detect_faults(image):
-    # Convert PIL image to numpy array
-    img_np = np.array(image)
-    
-    # Convert to RGB if necessary
-    if img_np.shape[-1] == 4:
-        img_np = img_np[:, :, :3]
+# Function to process frame and detect faults
+def detect_faults(frame):
+    # Convert frame to RGB if necessary
+    if frame.shape[-1] == 4:
+        frame = frame[:, :, :3]
     
-    # Prepare image for model
-    inputs = processor(images=img_np, return_tensors="pt")
+    # Prepare frame for model
+    inputs = processor(images=frame, return_tensors="pt")
     
     # Run inference
     with torch.no_grad():
         outputs = model(**inputs)
     
     # Post-process outputs
-    target_sizes = torch.tensor([img_np.shape[:2]])
+    target_sizes = torch.tensor([frame.shape[:2]])
     results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
     
     # Initialize fault detection
     faults = {"Thermal Fault": False, "Dust Fault": False, "Power Generation Fault": False}
-    annotated_img = img_np.copy()
+    annotated_frame = frame.copy()
     
-    # Analyze thermal image for faults
+    # Analyze frame for faults
     for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
         box = [int(i) for i in box.tolist()]
         # Simulate fault detection based on bounding box and pixel intensity
-        roi = img_np[box[1]:box[3], box[0]:box[2]]
+        roi = frame[box[1]:box[3], box[0]:box[2]]
         mean_intensity = np.mean(roi)
         
         # Thermal Fault: High intensity (hotspot)
-        if mean_intensity > 200:  # Adjust threshold based on thermal image scale
+        if mean_intensity > 200:  # Adjust threshold based on thermal video scale
             faults["Thermal Fault"] = True
-            cv2.rectangle(annotated_img, (box[0], box[1]), (box[2], box[3]), (255, 0, 0), 2)
-            cv2.putText(annotated_img, "Thermal Fault", (box[0], box[1]-10), 
+            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)
         
         # Dust Fault: Low intensity or irregular patterns
         elif mean_intensity < 100:  # Adjust threshold
             faults["Dust Fault"] = True
-            cv2.rectangle(annotated_img, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 2)
-            cv2.putText(annotated_img, "Dust Fault", (box[0], box[1]-10), 
+            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)
         
         # Power Generation Fault: Any detected anomaly may indicate reduced efficiency
         if faults["Thermal Fault"] or faults["Dust Fault"]:
             faults["Power Generation Fault"] = True
     
-    return annotated_img, faults
+    return annotated_frame, faults
+
+# Function to process video and generate annotated output
+def process_video(video_path):
+    # Open video
+    cap = cv2.VideoCapture(video_path)
+    if not cap.isOpened():
+        st.error("Error: Could not open video file.")
+        return None, None
+    
+    # Get video properties
+    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))
+    
+    # Create temporary file for output video
+    output_path = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False).name
+    fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+    out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))
+    
+    # Initialize fault summary
+    video_faults = {"Thermal Fault": False, "Dust Fault": False, "Power Generation Fault": False}
+    
+    # Process each frame
+    frame_count = 0
+    with st.spinner("Analyzing video..."):
+        progress = st.progress(0)
+        while cap.isOpened():
+            ret, frame = cap.read()
+            if not ret:
+                break
+            
+            # Convert BGR to RGB
+            frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            
+            # Detect faults in frame
+            annotated_frame, faults = detect_faults(frame_rgb)
+            
+            # Update video faults
+            for fault in video_faults:
+                video_faults[fault] |= faults[fault]
+            
+            # Convert back to BGR for writing
+            annotated_frame_bgr = cv2.cvtColor(annotated_frame, cv2.COLOR_RGB2BGR)
+            out.write(annotated_frame_bgr)
+            
+            # Update progress
+            frame_count += 1
+            progress.progress(frame_count / total_frames)
+    
+    cap.release()
+    out.release()
+    
+    return output_path, video_faults
 
 # File uploader
-uploaded_file = st.file_uploader("Upload a thermal image", type=["png", "jpg", "jpeg"])
+uploaded_file = st.file_uploader("Upload a thermal video", type=["mp4"])
 
 if uploaded_file is not None:
-    # Load and display uploaded image
-    image = Image.open(uploaded_file).convert("RGB")
-    st.image(image, caption="Uploaded Thermal Image", use_column_width=True)
-    
-    # Process image
-    with st.spinner("Analyzing image..."):
-        annotated_img, faults = detect_faults(image)
-    
-    # Display results
-    st.subheader("Fault Detection Results")
-    st.image(annotated_img, caption="Annotated Image with Detected Faults", use_column_width=True)
-    
-    # Show fault summary
-    st.write("**Detected Faults:**")
-    for fault, detected in 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)
-    
-    # Provide recommendations
-    if any(faults.values()):
-        st.subheader("Recommendations")
-        if faults["Thermal Fault"]:
-            st.write("- **Thermal Fault**: Inspect for damaged components or overheating issues.")
-        if faults["Dust Fault"]:
-            st.write("- **Dust Fault**: Schedule cleaning to remove dust accumulation.")
-        if 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.")
+    # Save uploaded video to temporary file
+    tfile = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
+    tfile.write(uploaded_file.read())
+    tfile.close()
+    
+    # Display uploaded video
+    st.video(tfile.name, format="video/mp4")
+    
+    # Process video
+    output_path, video_faults = process_video(tfile.name)
+    
+    if output_path:
+        # Display results
+        st.subheader("Fault Detection Results")
+        st.video(output_path, format="video/mp4")
+        
+        # Show fault summary
+        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)
+        
+        # Provide recommendations
+        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.")
+        
+        # Clean up temporary files
+        os.unlink(output_path)
+    
+    # Clean up uploaded file
+    os.unlink(tfile.name)
 
 # Footer
 st.markdown("---")
-st.write("Built with Streamlit and Hugging Face Transformers for Solar Panel Fault Detection PoC")
+st.write("Built with Streamlit, Hugging Face Transformers, and OpenCV for Solar Panel Fault Detection PoC")