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8c42a4b c88f605 8c42a4b c88f605 8c42a4b c88f605 8c42a4b c88f605 8c42a4b ac6a917 c88f605 8c42a4b c88f605 8c42a4b ac6a917 c88f605 ac6a917 ee29146 c88f605 ee29146 c88f605 8c42a4b c88f605 ac6a917 ee29146 ac6a917 c88f605 ee29146 c88f605 ee29146 ac6a917 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 | # import streamlit as st
# import cv2
# import numpy as np
# import easyocr
# from PIL import Image
# from tensorflow.keras.models import load_model
# from tensorflow.keras.preprocessing import image as keras_image
# # Load model and OCR tools
# model = load_model("Vehicle_number_plate_Detection.keras")
# plate_detector = cv2.CascadeClassifier("haarcascade_russian_plate_number.xml")
# reader = easyocr.Reader(['en'])
# # Plate Detection Function
# def detect_and_predict(img_input):
# img = np.array(img_input.convert("RGB"))
# frame = img.copy()
# gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
# plates = plate_detector.detectMultiScale(gray, 1.1, 4)
# plate_text = "Not Detected"
# confidence = None
# for x, y, w, h in plates:
# roi = frame[y:y+h, x:x+w]
# try:
# test_img = cv2.resize(roi, (200, 200))
# test_img = keras_image.img_to_array(test_img) / 255.0
# test_img = np.expand_dims(test_img, axis=0)
# pred = model.predict(test_img)[0][0]
# except Exception as e:
# print(f"Prediction error: {e}")
# continue
# if pred < 0.5:
# result = reader.readtext(roi)
# if result:
# plate_text = result[0][1]
# confidence = result[0][2]
# label = f"Plate: {plate_text}"
# else:
# label = "Plate Detected (No text)"
# else:
# label = "Plate Not Detected"
# # Draw detection
# cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
# cv2.putText(frame, label, (x, y - 10),
# cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
# # Ensure output image is same size as input
# result_img = Image.fromarray(frame)
# return result_img, confidence, plate_text
# # Streamlit App UI
# st.set_page_config(page_title="License Plate Detection", layout="wide")
# st.title("🚘 License Plate Detection App")
# tab1, tab2 = st.tabs(["📁 Upload Image", "📷 Webcam Capture"])
# # Tab 1 - Upload Image
# with tab1:
# col1, col2 = st.columns([1, 2])
# uploaded_file = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"])
# if uploaded_file:
# image_input = Image.open(uploaded_file)
# with col1:
# st.image(image_input, caption="Uploaded Image", use_container_width=True)
# if st.button("🔍 Detect from Upload"):
# with st.spinner("Processing..."):
# result_img, confidence, label = detect_and_predict(image_input)
# with col2:
# st.image(result_img, caption="Detection Result", use_container_width=True)
# if confidence:
# st.metric("Confidence", f"{confidence * 100:.2f}%")
# st.success(f"Detected Text: {label}")
# else:
# st.warning("No plate text detected.")
# # Tab 2 - Webcam Input (camera snapshot)
# with tab2:
# col1, col2 = st.columns([1, 2])
# with col1:
# camera_image = st.camera_input("📷 Take a picture using your webcam")
# if camera_image:
# try:
# image_input = Image.open(camera_image)
# with st.spinner("Analyzing..."):
# result_img, confidence, label = detect_and_predict(image_input)
# with col2:
# st.image(result_img, caption="Detection Result", use_container_width=True)
# if confidence is not None:
# st.metric("Confidence", f"{confidence*100:.2f}%")
# st.success(f"Detected Text: {label}")
# else:
# st.warning("Plate detected but no readable text found.")
# except Exception as e:
# st.error(f"❌ Error: {str(e)}")
import streamlit as st
import cv2
import numpy as np
import easyocr
from PIL import Image
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image as keras_image
# Load model and OCR tools
model = load_model("Vehicle_number_plate_Detection.keras")
plate_detector = cv2.CascadeClassifier("haarcascade_russian_plate_number.xml")
reader = easyocr.Reader(['en'])
# Plate Detection Function
def detect_and_predict(img_input):
img = np.array(img_input.convert("RGB"))
frame = img.copy()
gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
plates = plate_detector.detectMultiScale(gray, 1.1, 4)
plate_text = "Not Detected"
confidence = None
for x, y, w, h in plates:
roi = frame[y:y+h, x:x+w]
try:
test_img = cv2.resize(roi, (200, 200))
test_img = keras_image.img_to_array(test_img) / 255.0
test_img = np.expand_dims(test_img, axis=0)
pred = model.predict(test_img)[0][0]
except Exception as e:
print(f"Prediction error: {e}")
continue
if pred < 0.5:
result = reader.readtext(roi)
if result:
plate_text = result[0][1]
confidence = result[0][2]
label = f"Plate: {plate_text}"
else:
label = "Plate Detected (No text)"
else:
label = "Plate Not Detected"
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.putText(frame, label, (x, y - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
# Convert back to PIL and resize to 450x450 for display
result_img = Image.fromarray(frame)
result_img = result_img.resize((450, 450))
return result_img, confidence, plate_text
# Streamlit UI
st.set_page_config(page_title="License Plate Detection", layout="wide")
st.title("🚘 License Plate Detection App")
tab1, tab2 = st.tabs(["📁 Upload Image", "📷 Webcam Capture"])
# Tab 1 - Upload Image
with tab1:
col1, col2 = st.columns([1, 2])
with col1:
uploaded_file = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"], key="uploader")
if uploaded_file:
image_input = Image.open(uploaded_file)
st.image(image_input, caption="Uploaded Image", use_container_width=True)
if st.button("🔍 Detect from Upload"):
with st.spinner("Processing..."):
result_img, confidence, label = detect_and_predict(image_input)
with col2:
st.image(result_img, caption="Detection Result (450x450)", use_container_width=False)
if confidence:
st.metric("Confidence", f"{confidence * 100:.2f}%")
st.success(f"Detected Text: {label}")
else:
st.warning("No plate text detected.")
# Tab 2 - Webcam Input (camera snapshot)
with tab2:
col1, col2 = st.columns([1, 2])
with col1:
camera_image = st.camera_input("📷 Take a picture using your webcam")
if camera_image:
try:
image_input = Image.open(camera_image)
with st.spinner("Analyzing..."):
result_img, confidence, label = detect_and_predict(image_input)
with col2:
st.image(result_img, caption="Detection Result (450x450)", use_container_width=False)
if confidence is not None:
st.metric("Confidence", f"{confidence*100:.2f}%")
st.success(f"Detected Text: {label}")
else:
st.warning("Plate detected but no readable text found.")
except Exception as e:
st.error(f"❌ Error: {str(e)}")
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