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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") |