Create refine.py

refine.py

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+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
+
+# Upload image
+@st.cache_resource
+def load_model():
+	model = YOLO('yolo11n-custom.pt')
+	model.fuse()
+	return model
+
+model = load_model()
+
+reader = easyocr.Reader(['en'])
+def apply_filters(image, noise, sharpen, grayscale, threshold, edges, invert, auto, blur, contrast, brightness, scale, denoise, hist_eq, gamma, clahe):
+	img = np.array(image)
+	
+	# Auto Enhancement
+	if auto:
+		lab = cv2.cvtColor(img, cv2.COLOR_RGB2LAB)
+		l, a, b = cv2.split(lab)
+		clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
+		l = clahe.apply(l)
+		img = cv2.merge((l, a, b))
+		img = cv2.cvtColor(img, cv2.COLOR_LAB2RGB)
+
+
+	# Scaling
+	if scale != 1.0:
+		height, width = img.shape[:2]
+		img = cv2.resize(img, (int(width * scale), int(height * scale)))
+	
+	# Noise Reduction (Bilateral Filtering)
+	if noise:
+		img = cv2.bilateralFilter(img, 9, 75, 75)
+
+	# Noise Reduction (Non-Local Means)
+	if denoise:
+		img = cv2.fastNlMeansDenoisingColored(img, None, 10, 10, 7, 21)
+	
+	# Sharpening
+	if sharpen:
+		kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]])
+		img = cv2.filter2D(img, -1, kernel)
+	
+	# Convert to Grayscale
+	if grayscale or threshold or hist_eq or clahe:
+		img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+
+	# Histogram Equalization
+	if hist_eq:
+		img = cv2.equalizeHist(img)
+	
+	# CLAHE (Contrast Limited Adaptive Histogram Equalization)
+	if clahe:
+		clahe_filter = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
+		img = clahe_filter.apply(img)
+	
+	# Adaptive Thresholding
+	if threshold:
+		img = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)
+	
+	# Edge Detection
+	if edges:
+		img = cv2.Canny(img, 100, 200)
+	
+	# Invert Colors
+	if invert:
+		img = cv2.bitwise_not(img)
+	
+	# Blur
+	if gamma != 1.0:
+		inv_gamma = 1.0 / gamma
+		table = np.array([(i / 255.0) ** inv_gamma * 255 for i in np.arange(0, 256)]).astype("uint8")
+		img = cv2.LUT(img, table)
+
+	# Blur
+	if blur:
+		img = cv2.GaussianBlur(img, (2*blur + 1, 2*blur + 1), 0)
+	
+	# Contrast & Brightness
+	if contrast != 1.0 or brightness != 0:
+		img = cv2.convertScaleAbs(img, alpha=contrast, beta=brightness)
+	
+	return img
+
+st.title("🖼️ Refine Image for Detection")
+st.write("Enhance the license plate image for better recognition.")
+
+uploaded_file = st.file_uploader("📤 Upload an image", type=["jpg", "png", "jpeg"])
+
+if uploaded_file:
+	# Read image
+	img = Image.open(uploaded_file)
+	img = np.array(img)
+
+	# Detect license plates
+	st.write("🔍 Detecting license plates...")
+	results = model.predict(img, conf=0.15, iou=0.3, classes=[0])  
+	plates = results[0].boxes.xyxy if len(results) > 0 else []
+
+	if len(plates) == 0:
+		st.error("❌ No license plates detected. Try another image.")
+	else:
+		st.write("📌 **Select a License Plate by Clicking Below**")
+		
+		# Show detected plates in a grid
+		if "selected_plate_index" not in st.session_state:
+			st.session_state.selected_plate_index = 0
+
+		selected_plate_index = st.session_state.get("selected_plate_index", 0)
+		cols = st.columns(len(plates))  # Create dynamic columns
+
+		for i, (x1, y1, x2, y2) in enumerate(plates):
+			plate_img = img[int(y1):int(y2), int(x1):int(x2)]
+			plate_img = Image.fromarray(plate_img)
+
+			with cols[i]:  # Place each image in a column
+				st.image(plate_img, caption=f"Plate {i+1}", use_container_width =True)
+				if st.button(f"Select Plate {i+1}", key=f"plate_{i}"):
+					st.session_state["selected_plate_index"] = i
+
+		# Get the selected plate
+		selected_index = st.session_state["selected_plate_index"]
+		x1, y1, x2, y2 = map(int, plates[selected_index])
+		cropped_plate = img[y1:y2, x1:x2]
+		refined_img = cropped_plate.copy()
+
+		# Sidebar for enhancements
+		st.sidebar.header("🔧 Enhancement Options")
+		blur = st.sidebar.slider("🔹 Blur", 0, 10, 0)
+		contrast = st.sidebar.slider("🔹 Contrast", 0.5, 2.0, 1.0)
+		brightness = st.sidebar.slider("🔹 Brightness", 0.5, 2.0, 1.0)
+		gamma = st.sidebar.slider("Gamma Correction", 0.1, 3.0, 1.0, 0.1)
+		scale = st.sidebar.slider("🔹 Scale", 1.0, 10.0, 5.0)
+		noise = st.sidebar.checkbox("Noise Reduction (Bilateral)")
+		denoise = st.sidebar.checkbox("Denoise (Non-Local Means)")
+		sharpen = st.sidebar.checkbox("Sharpening")
+		hist_eq = st.sidebar.checkbox("Histogram Equalization")
+		clahe = st.sidebar.checkbox("CLAHE (Advanced Contrast)")
+		grayscale = st.sidebar.checkbox("Grayscale Conversion")
+		threshold = st.sidebar.checkbox("Adaptive Thresholding")
+		edges = st.sidebar.checkbox("Edge Detection")
+		invert = st.sidebar.checkbox("Invert Colors")
+		auto = st.sidebar.checkbox("Auto Enhancement")
+
+		refined_img = apply_filters(refined_img, noise, sharpen, grayscale, threshold, edges, invert, auto, blur, contrast, brightness, scale, denoise, hist_eq, gamma, clahe)
+
+		st.image(refined_img, caption="Refined License Plate", use_container_width=True)
+
+		if st.button("📖 Detect License Plate Text"):
+			with st.spinner("🔎 Reading text..."):
+				ocr_result = reader.readtext(np.array(refined_img), detail=0, allowlist="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-")
+				plate_text = " ".join(ocr_result).upper() if ocr_result else "❌ No text detected."
+			
+			# Show detected text
+			st.subheader("📜 Detected License Plate:")
+			st.code(plate_text, language="plaintext")