Upload 7 files
Browse files- README.md +77 -10
- app.py +9 -0
- detect.py +124 -0
- home.py +17 -0
- refine.py +163 -0
- requirements.txt +9 -0
- yolo11n-custom.pt +3 -0
README.md
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---
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# π License Plate Detection & Refinement
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A **Streamlit-based application** for detecting and refining license plate images using **YOLO for detection** and **OpenCV for image enhancements**.
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[](your-streamlit-app-link)
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---
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## π§ Features
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### **π Real-Time License Plate Detection**
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- Upload an **image** or **video**, or use a **webcam** for real-time detection.
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- Uses **YOLOv11** for accurate license plate detection.
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- Extracts text using **EasyOCR**.
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- Displays detected plates in a structured table.
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### **π Refine Image for Detection**
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- Upload an image and apply **pre-processing techniques** to enhance detection.
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- Select from detected license plates to refine specific sections.
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- **Image enhancement options:**
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- **Noise Reduction** (Bilateral & Non-Local Means Denoising)
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- **Sharpening** (Enhances edges for better OCR accuracy)
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- **Grayscale Conversion** (Improves contrast)
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- **Adaptive Thresholding** (Dynamic brightness adjustment)
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- **Edge Detection** (Canny filter for better boundary detection)
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- **Invert Colors** (Helpful for light text on dark backgrounds)
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- **Auto Enhancement** (Applies recommended adjustments automatically)
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- **Gamma Correction, CLAHE, Histogram Equalization, Scaling, Blur, Contrast & Brightness adjustments**
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---
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## π How to Run Locally
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### **1οΈβ£ Install dependencies**
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```sh
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pip install -r requirements.txt
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```
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### **2οΈβ£ Run the application**
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```sh
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streamlit run app.py
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```
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---
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## π Project Structure
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```
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π License-Plate-Detection
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βββ app.py # Main navigation & home page
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βββ pages/
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β βββ detect.py # License plate detection module
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β βββ refine.py # Image enhancement module
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βββ requirements.txt # Required Python packages
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βββ README.md # Project documentation
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```
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---
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## π Deployment
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You can deploy this app on **Streamlit Cloud** or **Hugging Face Spaces**:
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1. **Fork & Push to GitHub**
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2. **Deploy via Streamlit Sharing / Hugging Face Spaces**
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[](your-huggingface-link)
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---
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## π Future Enhancements
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- **Real-time tracking of license plates**.
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- **OCR post-processing to correct errors**.
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- **Additional denoising & image restoration techniques**.
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---
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## π License
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This project is **open-source** under the **MIT License**. Feel free to modify and improve it!
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π If you like this project, donβt forget to **star the repository**!
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app.py
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import streamlit as st
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pages = [
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st.Page("home.py", title="Home", icon="π "),
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st.Page("detect.py", title="Real-Time License Plate Detection", icon="π"),
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st.Page("refine.py", title="Refine Image for Detection", icon="πΌοΈ")
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]
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nav = st.navigation(pages, position="sidebar", expanded=False)
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nav.run()
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detect.py
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import streamlit as st
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from ultralytics import YOLO
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import cv2
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import easyocr
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import numpy as np
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import pandas as pd
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from PIL import Image
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import tempfile
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@st.cache_resource
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def load_model():
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model = YOLO('yolo11n-custom.pt')
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model.fuse()
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return model
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model = load_model()
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reader = easyocr.Reader(['en'])
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def detect_license_plate(image):
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results = model.predict(image, conf=0.15, iou=0.3, classes=[0])
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plate_texts = []
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img_array = np.array(image)
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# img = cv2.imread(image_path)
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img = cv2.cvtColor(img_array, cv2.COLOR_RGB2BGR)
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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img_height, img_width, _ = img.shape
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for result in results:
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for bbox in result.boxes.xyxy:
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x1, y1, x2, y2 = map(int, bbox.tolist())
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plate = img[int(y1):int(y2), int(x1):int(x2)]
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scale=2
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height, width = plate.shape[:2]
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plate = cv2.resize(plate, (width * scale, height * scale), interpolation=cv2.INTER_CUBIC)
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text = reader.readtext(plate, detail=0, allowlist="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-")
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text = " ".join(text).upper()
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text_scale = max(1, width / 250)
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thickness = max(2, width // 200)
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cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), thickness)
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(text_width, text_height), _ = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, text_scale, thickness)
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text_x = x1 + (width - text_width) // 2 # Centered horizontally
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text_y = y1 - 10 if y1 > 50 else y2 + text_height + 20 # Above unless too high
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text_box_y1 = text_y - text_height - 5
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text_box_y2 = text_y + 5
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cv2.rectangle(img, (text_x - 8, text_box_y1 - 3), (text_x + text_width + 8, text_box_y2 + 3), (0, 0, 0), -1)
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cv2.rectangle(img, (text_x - 5, text_box_y1), (text_x + text_width + 5, text_box_y2), (255, 255, 255), -1)
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cv2.putText(img, text, (text_x, text_y), cv2.FONT_HERSHEY_SIMPLEX, text_scale, (0, 0, 0), thickness)
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plate_texts.append(text)
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image = img
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return image, plate_texts
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st.title("π Real-Time License Plate Detection", anchor=False)
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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.")
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st.write("Upload an image, upload a video, or use your webcam for real-time license plate detection.")
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option = st.radio("Choose Input Source:", ("Upload Image", "Upload Video", "Webcam"), horizontal=True )
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if option == "Upload Image":
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uploaded_file = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
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if uploaded_file:
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img = Image.open(uploaded_file)
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st.write("Processing...")
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processed_img, plate_texts = detect_license_plate(img)
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st.image(processed_img, caption="Detected Plates Image", use_container_width=True)
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st.write("**Detected License Plates:**")
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if plate_texts:
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plates = pd.DataFrame({"License Plate": plate_texts})
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plates.index = plates.index + 1
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st.table(plates)
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else:
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st.write("No license plates detected.")
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elif option == "Upload Video":
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uploaded_video = st.file_uploader("Choose a video...", type=["mp4", "avi", "mov"])
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if uploaded_video is not None:
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st.write("Processing video...")
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tfile = tempfile.NamedTemporaryFile(delete=False)
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tfile.write(uploaded_video.read())
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cap = cv2.VideoCapture(tfile.name)
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frame_placeholder = st.empty()
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while cap.isOpened():
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ret, frame = cap.read()
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if not ret:
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break
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processed_frame, plate_texts = detect_license_plate(frame)
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frame_placeholder.image(processed_frame, caption="Detected Plates Video", use_container_width=True)
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cap.release()
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elif option == "Webcam":
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if "running" not in st.session_state:
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st.session_state.running = True
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if st.button("Stop"):
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st.session_state.running = False
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st.write("Starting Webcam... Press **Stop** to end.")
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cap = cv2.VideoCapture(0)
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frame_placeholder = st.empty()
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while cap.isOpened():
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ret, frame = cap.read()
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if not ret:
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st.warning("Failed to capture webcam feed.")
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break
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processed_frame, plate_texts = detect_license_plate(frame)
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frame_placeholder.image(processed_frame, channels="BGR", caption="Webcam Feed", use_container_width=True)
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if not st.session_state.running:
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break
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cap.release()
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home.py
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import streamlit as st
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st.title("Welcome to License Plate Detection App", anchor=False)
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st.write("Select a feature below to detect or enhance license plate images.")
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# Buttons for navigation
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col1, col2 = st.columns(2)
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with col1:
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if st.button("π Detect License Plates"):
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st.switch_page("detect.py") # Navigates to the detection page
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st.caption("Upload an image, video, or use a webcam for real-time detection.")
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with col2:
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if st.button("πΌοΈ Enhance Image for Detection"):
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st.switch_page("refine.py") # Navigates to the image enhancement page
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st.caption("Improve image quality for better license plate recognition.")
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refine.py
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import streamlit as st
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from ultralytics import YOLO
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import cv2
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import easyocr
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import numpy as np
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import pandas as pd
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from PIL import Image
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import tempfile
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# Upload image
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@st.cache_resource
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def load_model():
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model = YOLO('yolo11n-custom.pt')
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model.fuse()
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return model
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model = load_model()
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reader = easyocr.Reader(['en'])
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def apply_filters(image, noise, sharpen, grayscale, threshold, edges, invert, auto, blur, contrast, brightness, scale, denoise, hist_eq, gamma, clahe):
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img = np.array(image)
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# Auto Enhancement
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if auto:
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lab = cv2.cvtColor(img, cv2.COLOR_RGB2LAB)
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l, a, b = cv2.split(lab)
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clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
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l = clahe.apply(l)
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img = cv2.merge((l, a, b))
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img = cv2.cvtColor(img, cv2.COLOR_LAB2RGB)
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# Scaling
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if scale != 1.0:
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height, width = img.shape[:2]
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img = cv2.resize(img, (int(width * scale), int(height * scale)))
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# Noise Reduction (Bilateral Filtering)
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if noise:
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40 |
+
img = cv2.bilateralFilter(img, 9, 75, 75)
|
41 |
+
|
42 |
+
# Noise Reduction (Non-Local Means)
|
43 |
+
if denoise:
|
44 |
+
img = cv2.fastNlMeansDenoisingColored(img, None, 10, 10, 7, 21)
|
45 |
+
|
46 |
+
# Sharpening
|
47 |
+
if sharpen:
|
48 |
+
kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]])
|
49 |
+
img = cv2.filter2D(img, -1, kernel)
|
50 |
+
|
51 |
+
# Convert to Grayscale
|
52 |
+
if grayscale or threshold or hist_eq or clahe:
|
53 |
+
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
|
54 |
+
|
55 |
+
# Histogram Equalization
|
56 |
+
if hist_eq:
|
57 |
+
img = cv2.equalizeHist(img)
|
58 |
+
|
59 |
+
# CLAHE (Contrast Limited Adaptive Histogram Equalization)
|
60 |
+
if clahe:
|
61 |
+
clahe_filter = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
|
62 |
+
img = clahe_filter.apply(img)
|
63 |
+
|
64 |
+
# Adaptive Thresholding
|
65 |
+
if threshold:
|
66 |
+
img = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)
|
67 |
+
|
68 |
+
# Edge Detection
|
69 |
+
if edges:
|
70 |
+
img = cv2.Canny(img, 100, 200)
|
71 |
+
|
72 |
+
# Invert Colors
|
73 |
+
if invert:
|
74 |
+
img = cv2.bitwise_not(img)
|
75 |
+
|
76 |
+
# Blur
|
77 |
+
if gamma != 1.0:
|
78 |
+
inv_gamma = 1.0 / gamma
|
79 |
+
table = np.array([(i / 255.0) ** inv_gamma * 255 for i in np.arange(0, 256)]).astype("uint8")
|
80 |
+
img = cv2.LUT(img, table)
|
81 |
+
|
82 |
+
# Blur
|
83 |
+
if blur:
|
84 |
+
img = cv2.GaussianBlur(img, (2*blur + 1, 2*blur + 1), 0)
|
85 |
+
|
86 |
+
# Contrast & Brightness
|
87 |
+
if contrast != 1.0 or brightness != 0:
|
88 |
+
img = cv2.convertScaleAbs(img, alpha=contrast, beta=brightness)
|
89 |
+
|
90 |
+
return img
|
91 |
+
|
92 |
+
st.title("πΌοΈ Refine Image for Detection")
|
93 |
+
st.write("Enhance the license plate image for better recognition.")
|
94 |
+
|
95 |
+
uploaded_file = st.file_uploader("π€ Upload an image", type=["jpg", "png", "jpeg"])
|
96 |
+
|
97 |
+
if uploaded_file:
|
98 |
+
# Read image
|
99 |
+
img = Image.open(uploaded_file)
|
100 |
+
img = np.array(img)
|
101 |
+
|
102 |
+
# Detect license plates
|
103 |
+
st.write("π Detecting license plates...")
|
104 |
+
results = model.predict(img, conf=0.15, iou=0.3, classes=[0])
|
105 |
+
plates = results[0].boxes.xyxy if len(results) > 0 else []
|
106 |
+
|
107 |
+
if len(plates) == 0:
|
108 |
+
st.error("β No license plates detected. Try another image.")
|
109 |
+
else:
|
110 |
+
st.write("π **Select a License Plate by Clicking Below**")
|
111 |
+
|
112 |
+
# Show detected plates in a grid
|
113 |
+
if "selected_plate_index" not in st.session_state:
|
114 |
+
st.session_state.selected_plate_index = 0
|
115 |
+
|
116 |
+
selected_plate_index = st.session_state.get("selected_plate_index", 0)
|
117 |
+
cols = st.columns(len(plates)) # Create dynamic columns
|
118 |
+
|
119 |
+
for i, (x1, y1, x2, y2) in enumerate(plates):
|
120 |
+
plate_img = img[int(y1):int(y2), int(x1):int(x2)]
|
121 |
+
plate_img = Image.fromarray(plate_img)
|
122 |
+
|
123 |
+
with cols[i]: # Place each image in a column
|
124 |
+
st.image(plate_img, caption=f"Plate {i+1}", use_container_width =True)
|
125 |
+
if st.button(f"Select Plate {i+1}", key=f"plate_{i}"):
|
126 |
+
st.session_state["selected_plate_index"] = i
|
127 |
+
|
128 |
+
# Get the selected plate
|
129 |
+
selected_index = st.session_state["selected_plate_index"]
|
130 |
+
x1, y1, x2, y2 = map(int, plates[selected_index])
|
131 |
+
cropped_plate = img[y1:y2, x1:x2]
|
132 |
+
refined_img = cropped_plate.copy()
|
133 |
+
|
134 |
+
# Sidebar for enhancements
|
135 |
+
st.sidebar.header("π§ Enhancement Options")
|
136 |
+
blur = st.sidebar.slider("πΉ Blur", 0, 10, 0)
|
137 |
+
contrast = st.sidebar.slider("πΉ Contrast", 0.5, 2.0, 1.0)
|
138 |
+
brightness = st.sidebar.slider("πΉ Brightness", 0.5, 2.0, 1.0)
|
139 |
+
gamma = st.sidebar.slider("Gamma Correction", 0.1, 3.0, 1.0, 0.1)
|
140 |
+
scale = st.sidebar.slider("πΉ Scale", 1.0, 10.0, 5.0)
|
141 |
+
noise = st.sidebar.checkbox("Noise Reduction (Bilateral)")
|
142 |
+
denoise = st.sidebar.checkbox("Denoise (Non-Local Means)")
|
143 |
+
sharpen = st.sidebar.checkbox("Sharpening")
|
144 |
+
hist_eq = st.sidebar.checkbox("Histogram Equalization")
|
145 |
+
clahe = st.sidebar.checkbox("CLAHE (Advanced Contrast)")
|
146 |
+
grayscale = st.sidebar.checkbox("Grayscale Conversion")
|
147 |
+
threshold = st.sidebar.checkbox("Adaptive Thresholding")
|
148 |
+
edges = st.sidebar.checkbox("Edge Detection")
|
149 |
+
invert = st.sidebar.checkbox("Invert Colors")
|
150 |
+
auto = st.sidebar.checkbox("Auto Enhancement")
|
151 |
+
|
152 |
+
refined_img = apply_filters(refined_img, noise, sharpen, grayscale, threshold, edges, invert, auto, blur, contrast, brightness, scale, denoise, hist_eq, gamma, clahe)
|
153 |
+
|
154 |
+
st.image(refined_img, caption="Refined License Plate", use_container_width=True)
|
155 |
+
|
156 |
+
if st.button("π Detect License Plate Text"):
|
157 |
+
with st.spinner("π Reading text..."):
|
158 |
+
ocr_result = reader.readtext(np.array(refined_img), detail=0, allowlist="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-")
|
159 |
+
plate_text = " ".join(ocr_result).upper() if ocr_result else "β No text detected."
|
160 |
+
|
161 |
+
# Show detected text
|
162 |
+
st.subheader("π Detected License Plate:")
|
163 |
+
st.code(plate_text, language="plaintext")
|
requirements.txt
ADDED
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
streamlit
|
2 |
+
opencv-python
|
3 |
+
torch
|
4 |
+
torchvision
|
5 |
+
easyocr
|
6 |
+
numpy
|
7 |
+
pandas
|
8 |
+
pillow
|
9 |
+
ultralytics
|
yolo11n-custom.pt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:c5ebaab2bc7c2036d97706cd1992389998de338961d0485879d0d128755c52e2
|
3 |
+
size 5477537
|