Update detect.py

detect.py

@@ -1,124 +1,130 @@
-import streamlit as st
-from ultralytics import YOLO
-import cv2
-import easyocr
-import numpy as np
-import pandas as pd
-from PIL import Image
-import tempfile
-
-@st.cache_resource
-def load_model():
-	model = YOLO('yolo11n-custom.pt')
-	model.fuse()
-	return model
-
-model = load_model()
-
-reader = easyocr.Reader(['en'])
-
-def detect_license_plate(image):
-	results = model.predict(image, conf=0.15, iou=0.3, classes=[0])
-	plate_texts = []
-	img_array = np.array(image)
-	# img = cv2.imread(image_path)
-	img = cv2.cvtColor(img_array, cv2.COLOR_RGB2BGR)
-	img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-
-	img_height, img_width, _ = img.shape
-
-	for result in results:
-		for bbox in result.boxes.xyxy:
-			x1, y1, x2, y2 = map(int, bbox.tolist())
-			plate = img[int(y1):int(y2), int(x1):int(x2)]
-			scale=2
-			height, width = plate.shape[:2]
-			plate = cv2.resize(plate, (width * scale, height * scale), interpolation=cv2.INTER_CUBIC)
-			
-			text = reader.readtext(plate, detail=0, allowlist="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-")
-			text = " ".join(text).upper()
-			
-			text_scale = max(1, width / 250)
-			thickness = max(2, width // 200)
-			cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), thickness)
-			(text_width, text_height), _ = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, text_scale, thickness)
-			text_x = x1 + (width - text_width) // 2  # Centered horizontally
-			text_y = y1 - 10 if y1 > 50 else y2 + text_height + 20  # Above unless too high
-			text_box_y1 = text_y - text_height - 5
-			text_box_y2 = text_y + 5
-			cv2.rectangle(img, (text_x - 8, text_box_y1 - 3), (text_x + text_width + 8, text_box_y2 + 3), (0, 0, 0), -1)
-			cv2.rectangle(img, (text_x - 5, text_box_y1), (text_x + text_width + 5, text_box_y2), (255, 255, 255), -1)
-			cv2.putText(img, text, (text_x, text_y), cv2.FONT_HERSHEY_SIMPLEX, text_scale, (0, 0, 0), thickness)
-
-			plate_texts.append(text)
-	image = img
-	return image, plate_texts
-
-st.title("🚘 Real-Time License Plate Detection", anchor=False)
-
-st.write("For better license plate detection, ensure you use high-quality images. If detection is unclear, try enhancing the image first. Use the Refine Image for Detection tool.")
-
-st.write("Upload an image, upload a video, or use your webcam for real-time license plate detection.")
-
-option = st.radio("Choose Input Source:", ("Upload Image", "Upload Video", "Webcam"), horizontal=True )
-
-
-if option == "Upload Image":
-	uploaded_file = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
-	if uploaded_file:
-		img = Image.open(uploaded_file)
-		st.write("Processing...")
-
-		processed_img, plate_texts = detect_license_plate(img)
-
-		st.image(processed_img, caption="Detected Plates Image", use_container_width=True)
-		st.write("**Detected License Plates:**")
-		if plate_texts:
-			plates = pd.DataFrame({"License Plate": plate_texts})
-			plates.index = plates.index + 1
-			st.table(plates)
-		else:
-			st.write("No license plates detected.")
-
-elif option == "Upload Video":
-	uploaded_video = st.file_uploader("Choose a video...", type=["mp4", "avi", "mov"])
-	if uploaded_video is not None:
-		st.write("Processing video...")
-		tfile = tempfile.NamedTemporaryFile(delete=False)
-		tfile.write(uploaded_video.read())
-		cap = cv2.VideoCapture(tfile.name)
-		frame_placeholder = st.empty()
-
-		while cap.isOpened():
-			ret, frame = cap.read()
-			if not ret:
-				break
-			processed_frame, plate_texts = detect_license_plate(frame)
-
-			frame_placeholder.image(processed_frame, caption="Detected Plates Video", use_container_width=True)
-		cap.release()
-
-elif option == "Webcam":
-	if "running" not in st.session_state:
-		st.session_state.running = True
-	if st.button("Stop"):
-		st.session_state.running = False
-
-	st.write("Starting Webcam... Press **Stop** to end.")
-	cap = cv2.VideoCapture(0)
-	frame_placeholder = st.empty()
-
-	while cap.isOpened():
-		ret, frame = cap.read()
-		if not ret:
-			st.warning("Failed to capture webcam feed.")
-			break
-
-		processed_frame, plate_texts = detect_license_plate(frame)
-
-		frame_placeholder.image(processed_frame, channels="BGR", caption="Webcam Feed", use_container_width=True)
-
-		if not st.session_state.running:
-			break
-
+import streamlit as st
+from ultralytics import YOLO
+import cv2
+import easyocr
+import numpy as np
+import pandas as pd
+from PIL import Image
+import tempfile
+
+@st.cache_resource
+def load_model():
+	model = YOLO('yolo11n-custom.pt')
+	model.fuse()
+	return model
+
+model = load_model()
+
+reader = easyocr.Reader(['en'])
+
+def detect_license_plate(image):
+	results = model.predict(image, conf=0.15, iou=0.3, classes=[0])
+	plate_texts = []
+	img_array = np.array(image)
+	# img = cv2.imread(image_path)
+	img = cv2.cvtColor(img_array, cv2.COLOR_RGB2BGR)
+	img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+	img_height, img_width, _ = img.shape
+
+	for result in results:
+		for bbox in result.boxes.xyxy:
+			x1, y1, x2, y2 = map(int, bbox.tolist())
+			plate = img[int(y1):int(y2), int(x1):int(x2)]
+			scale=2
+			height, width = plate.shape[:2]
+			plate = cv2.resize(plate, (width * scale, height * scale), interpolation=cv2.INTER_CUBIC)
+            lab = cv2.cvtColor(plate, cv2.COLOR_RGB2LAB)
+    		l, a, b = cv2.split(lab)
+    		clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
+    		l = clahe.apply(l)
+    		plate = cv2.merge((l, a, b))
+    		plate = cv2.cvtColor(plate, cv2.COLOR_LAB2RGB)
+			
+			text = reader.readtext(plate, detail=0, allowlist="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-")
+			text = " ".join(text).upper()
+			
+			text_scale = max(1, width / 250)
+			thickness = max(2, width // 200)
+			cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), thickness)
+			(text_width, text_height), _ = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, text_scale, thickness)
+			text_x = x1 + (width - text_width) // 2  # Centered horizontally
+			text_y = y1 - 10 if y1 > 50 else y2 + text_height + 20  # Above unless too high
+			text_box_y1 = text_y - text_height - 5
+			text_box_y2 = text_y + 5
+			cv2.rectangle(img, (text_x - 8, text_box_y1 - 3), (text_x + text_width + 8, text_box_y2 + 3), (0, 0, 0), -1)
+			cv2.rectangle(img, (text_x - 5, text_box_y1), (text_x + text_width + 5, text_box_y2), (255, 255, 255), -1)
+			cv2.putText(img, text, (text_x, text_y), cv2.FONT_HERSHEY_SIMPLEX, text_scale, (0, 0, 0), thickness)
+
+			plate_texts.append(text)
+	image = img
+	return image, plate_texts
+
+st.title("🚘 Real-Time License Plate Detection", anchor=False)
+
+st.write("For better license plate detection, ensure you use high-quality images. If detection is unclear, try enhancing the image first. Use the Refine Image for Detection tool.")
+
+st.write("Upload an image, upload a video, or use your webcam for real-time license plate detection.")
+
+option = st.radio("Choose Input Source:", ("Upload Image", "Upload Video", "Webcam"), horizontal=True )
+
+
+if option == "Upload Image":
+	uploaded_file = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
+	if uploaded_file:
+		img = Image.open(uploaded_file)
+		st.write("Processing...")
+
+		processed_img, plate_texts = detect_license_plate(img)
+
+		st.image(processed_img, caption="Detected Plates Image", use_container_width=True)
+		st.write("**Detected License Plates:**")
+		if plate_texts:
+			plates = pd.DataFrame({"License Plate": plate_texts})
+			plates.index = plates.index + 1
+			st.table(plates)
+		else:
+			st.write("No license plates detected.")
+
+elif option == "Upload Video":
+	uploaded_video = st.file_uploader("Choose a video...", type=["mp4", "avi", "mov"])
+	if uploaded_video is not None:
+		st.write("Processing video...")
+		tfile = tempfile.NamedTemporaryFile(delete=False)
+		tfile.write(uploaded_video.read())
+		cap = cv2.VideoCapture(tfile.name)
+		frame_placeholder = st.empty()
+
+		while cap.isOpened():
+			ret, frame = cap.read()
+			if not ret:
+				break
+			processed_frame, plate_texts = detect_license_plate(frame)
+
+			frame_placeholder.image(processed_frame, caption="Detected Plates Video", use_container_width=True)
+		cap.release()
+
+elif option == "Webcam":
+	if "running" not in st.session_state:
+		st.session_state.running = True
+	if st.button("Stop"):
+		st.session_state.running = False
+
+	st.write("Starting Webcam... Press **Stop** to end.")
+	cap = cv2.VideoCapture(0)
+	frame_placeholder = st.empty()
+
+	while cap.isOpened():
+		ret, frame = cap.read()
+		if not ret:
+			st.warning("Failed to capture webcam feed.")
+			break
+
+		processed_frame, plate_texts = detect_license_plate(frame)
+
+		frame_placeholder.image(processed_frame, channels="BGR", caption="Webcam Feed", use_container_width=True)
+
+		if not st.session_state.running:
+			break
+
 	cap.release()