Update app.py

app.py

@@ -1,68 +1,68 @@
-import streamlit as st
-import cv2
-import numpy as np
-import torch
-from yolov5 import YOLOv5
-
-# Initialize YOLOv5 model
-model = YOLOv5('yolov5s.pt')  # Replace with your model path
-
-def detect_number_plate(frame):
-    results = model(frame)
-    detections = results.pandas().xyxy[0]
-    plates = []
-
-    for _, row in detections.iterrows():
-        if row['name'] == 'number_plate':  # Adjust class name
-            plates.append({
-                'class': row['name'],
-                'confidence': row['confidence'],
-                'x_min': row['xmin'],
-                'y_min': row['ymin'],
-                'x_max': row['xmax'],
-                'y_max': row['ymax']
-            })
-    
-    return plates
-
-def detect_smoke(frame):
-    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-    blur = cv2.GaussianBlur(gray, (21, 21), 0)
-    _, thresh = cv2.threshold(blur, 200, 255, cv2.THRESH_BINARY)
-    
-    smoke_intensity = np.sum(thresh) / (thresh.shape[0] * thresh.shape[1])
-    smoke_detected = smoke_intensity > 0.1  # Adjust this threshold
-    
-    return smoke_detected, smoke_intensity
-
-def process_frame(frame):
-    plates = detect_number_plate(frame)
-    smoke_detected, smoke_intensity = detect_smoke(frame)
-    return {
-        'smoke_detected': smoke_detected,
-        'smoke_intensity': smoke_intensity,
-        'number_plates': plates
-    }
-
-# Streamlit app
-st.title("Vehicle Number Plate and Smoke Detection")
-
-uploaded_file = st.file_uploader("Choose an image...", type="jpg")
-
-if uploaded_file is not None:
-    # Convert file to image
-    in_memory_file = uploaded_file.read()
-    np_arr = np.frombuffer(in_memory_file, np.uint8)
-    frame = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
-
-    # Process the frame
-    results = process_frame(frame)
-
-    st.subheader("Results")
-    st.write(f"Smoke Detected: {results['smoke_detected']}")
-    st.write(f"Smoke Intensity: {results['smoke_intensity']:.2f}")
-
-    st.subheader("Number Plates Detected")
-    for plate in results['number_plates']:
-        st.write(f"Class: {plate['class']}, Confidence: {plate['confidence']:.2f}")
-        st.write(f"Bounding Box: ({plate['x_min']}, {plate['y_min']}) to ({plate['x_max']}, {plate['y_max']})")
+import streamlit as st
+import cv2
+import numpy as np
+import torch
+from yolov5 import YOLOv5
+
+# Initialize YOLOv5 model
+model = YOLOv5('best.pt')  # Replace with your model path
+
+def detect_number_plate(frame):
+    results = model(frame)
+    detections = results.pandas().xyxy[0]
+    plates = []
+
+    for _, row in detections.iterrows():
+        if row['name'] == 'number_plate':  # Adjust class name
+            plates.append({
+                'class': row['name'],
+                'confidence': row['confidence'],
+                'x_min': row['xmin'],
+                'y_min': row['ymin'],
+                'x_max': row['xmax'],
+                'y_max': row['ymax']
+            })
+    
+    return plates
+
+def detect_smoke(frame):
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    blur = cv2.GaussianBlur(gray, (21, 21), 0)
+    _, thresh = cv2.threshold(blur, 200, 255, cv2.THRESH_BINARY)
+    
+    smoke_intensity = np.sum(thresh) / (thresh.shape[0] * thresh.shape[1])
+    smoke_detected = smoke_intensity > 0.1  # Adjust this threshold
+    
+    return smoke_detected, smoke_intensity
+
+def process_frame(frame):
+    plates = detect_number_plate(frame)
+    smoke_detected, smoke_intensity = detect_smoke(frame)
+    return {
+        'smoke_detected': smoke_detected,
+        'smoke_intensity': smoke_intensity,
+        'number_plates': plates
+    }
+
+# Streamlit app
+st.title("Vehicle Number Plate and Smoke Detection")
+
+uploaded_file = st.file_uploader("Choose an image...", type="jpg")
+
+if uploaded_file is not None:
+    # Convert file to image
+    in_memory_file = uploaded_file.read()
+    np_arr = np.frombuffer(in_memory_file, np.uint8)
+    frame = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
+
+    # Process the frame
+    results = process_frame(frame)
+
+    st.subheader("Results")
+    st.write(f"Smoke Detected: {results['smoke_detected']}")
+    st.write(f"Smoke Intensity: {results['smoke_intensity']:.2f}")
+
+    st.subheader("Number Plates Detected")
+    for plate in results['number_plates']:
+        st.write(f"Class: {plate['class']}, Confidence: {plate['confidence']:.2f}")
+        st.write(f"Bounding Box: ({plate['x_min']}, {plate['y_min']}) to ({plate['x_max']}, {plate['y_max']})")