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| import streamlit as st | |
| import torch | |
| from transformers import DetrImageProcessor, DetrForObjectDetection | |
| from PIL import Image | |
| import numpy as np | |
| import cv2 | |
| # 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.") | |
| # Load model and processor | |
| def load_model(): | |
| processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50") | |
| model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50") | |
| return processor, 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] | |
| # Prepare image for model | |
| inputs = processor(images=img_np, return_tensors="pt") | |
| # Run inference | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| # Post-process outputs | |
| target_sizes = torch.tensor([img_np.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() | |
| # Analyze thermal image 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]] | |
| mean_intensity = np.mean(roi) | |
| # Thermal Fault: High intensity (hotspot) | |
| if mean_intensity > 200: # Adjust threshold based on thermal image 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.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.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 | |
| # File uploader | |
| uploaded_file = st.file_uploader("Upload a thermal image", type=["png", "jpg", "jpeg"]) | |
| 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.") | |
| # Footer | |
| st.markdown("---") | |
| st.write("Built with Streamlit and Hugging Face Transformers for Solar Panel Fault Detection PoC") |