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a315a32 fd70b99 a315a32 2c0c4cc a315a32 fd70b99 699ac49 2c0c4cc a315a32 fd70b99 699ac49 fd70b99 | 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 | import matplotlib.pyplot as plt
import numpy as np
import cv2
import tensorflow as tf
from sklearn.metrics import f1_score
from tensorflow.keras.models import load_model
from tensorflow.keras import optimizers
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.layers import Dense, Flatten, MaxPooling2D, Dropout, Conv2D
import streamlit as st
# Loads the data required for detecting the license plates from cascade classifier.
plate_cascade = cv2.CascadeClassifier('indian_license_plate.xml')
# add the path to 'india_license_plate.xml' file.
model = load_model('licence_trained_model.h5', compile=False)
def detect_plate(img, text=''): # the function detects and perfors blurring on the number plate.
plate_img = img.copy()
roi = img.copy()
plate_rect = plate_cascade.detectMultiScale(plate_img, scaleFactor = 1.2, minNeighbors = 7) # detects numberplates and returns the coordinates and dimensions of detected license plate's contours.
for (x,y,w,h) in plate_rect:
roi_ = roi[y:y+h, x:x+w, :] # extracting the Region of Interest of license plate for blurring.
plate = roi[y:y+h, x:x+w, :]
cv2.rectangle(plate_img, (x+2,y), (x+w-3, y+h-5), (51,181,155), 3) # finally representing the detected contours by drawing rectangles around the edges.
if text!='':
plate_img = cv2.putText(plate_img, text, (x-w//2,y-h//2),
cv2.FONT_HERSHEY_COMPLEX_SMALL , 0.5, (51,181,155), 1, cv2.LINE_AA)
return plate_img, plate # returning the processed image.
# Testing the above function
def display(img, title=''):
img = cv2.cvtColor(img_, cv2.COLOR_BGR2RGB)
fig = plt.figure(figsize=(10,6))
ax = plt.subplot(111)
ax.imshow(img)
plt.axis('off')
plt.title(title)
plt.show()
display(img,'input image')
'''
def segment_characters(image) :
# Preprocess cropped license plate image
img_lp = cv2.resize(image, (333, 75))
img_gray_lp = cv2.cvtColor(img_lp, cv2.COLOR_BGR2GRAY)
_, img_binary_lp = cv2.threshold(img_gray_lp, 200, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
img_binary_lp = cv2.erode(img_binary_lp, (3,3))
img_binary_lp = cv2.dilate(img_binary_lp, (3,3))
LP_WIDTH = img_binary_lp.shape[0]
LP_HEIGHT = img_binary_lp.shape[1]
# Make borders white
img_binary_lp
'''
# Streamlit app
def run():
st.write('##### Traffic Sign Classifier')
# Making Form
# Create a Streamlit form
with st.form(key='vehicle number plate'):
# Add a file uploader to the form
#img= st.file_uploader("Upload a file of one of these format .JPEG/.JPG/.PNG file", accept_multiple_files=True)
img= st.file_uploader("Upload a file of one of these format .JPEG/.JPG/.PNG file", accept_multiple_files=True)
if __name__ == '__main__':
run()
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