Spaces:
Sleeping
Sleeping
File size: 4,572 Bytes
e81860f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 |
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
@st.cache_resource
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") |