/
indian_license_plate_detection.py
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indian_license_plate_detection.py
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# -*- coding: utf-8 -*-
"""indian_license-plate-detection.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1mKJjK7fEvEXIa8DUGjbyeHwaE04bSqy3
"""
from google.colab import drive
drive.mount("/content/drive")
!ls /content/drive/MyDrive/Dataset
import pandas as pd
import urllib
import matplotlib.pyplot as plt
import numpy as np
import cv2
import glob
import os
import time
from PIL import Image
from keras.applications.vgg16 import VGG16
from keras.layers import Flatten, Dense, Conv2D, MaxPooling2D, Input, Dropout
from keras.models import Model, Sequential
from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import Adam
import numpy as np
import pandas as pd
#Utility
import urllib
import os
import shutil
import time
from PIL import Image
#Vizualization
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import cv2
#MODEL
import tensorflow as tf
from keras.layers import Flatten, Dense, Conv2D, MaxPooling2D, Input, Dropout, AveragePooling2D, Concatenate
from keras.models import Model, Sequential
from keras.applications.vgg16 import VGG16
from tensorflow.keras.applications import MobileNetV2
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
df = pd.read_json("/content/drive/MyDrive/Dataset/Indian_Number_plates.json", lines=True)
df
#Dictionray For Import
dataset = dict()
dataset["image_name"] = list()
dataset["image_width"] = list()
dataset["image_height"] = list()
dataset["top_x"] = list()
dataset["top_y"] = list()
dataset["bottom_x"] = list()
dataset["bottom_y"] = list()
#Extracting the Image and popualting the Dataset Dictionary
counter = 0
for index, row in df.iterrows():
img = urllib.request.urlopen(row["content"])
img = Image.open(img)
img = img.convert('RGB')
img.save(f"/content/drive/MyDrive/Dataset/indian_num-plate_dataset/licensed_car{counter}.jpeg","JPEG")
dataset["image_name"].append(f"licensed_car{counter}.jpeg")
data = row["annotation"]
dataset["image_width"].append(data[0]["imageWidth"])
dataset["image_height"].append(data[0]["imageHeight"])
dataset["top_x"].append(data[0]["points"][0]["x"])
dataset["top_y"].append(data[0]["points"][0]["y"])
dataset["bottom_x"].append(data[0]["points"][1]["x"])
dataset["bottom_y"].append(data[0]["points"][1]["y"])
counter += 1
print(f"Downloaded {counter} car images.")
#Saved For Future Use
pd.DataFrame(dataset).to_csv("/content/drive/MyDrive/Dataset/indian_plates.csv", index=False)
df = pd.read_csv("/content/drive/MyDrive/Dataset/indian_plates.csv")
df.head()
WIDTH = 224
HEIGHT = 224
def display_car_image(index, scale=True, WIDTH=224, HEIGHT=224):
images_dir = '/content/drive/MyDrive/Dataset/indian_num-plate_dataset/'
img = cv2.imread(images_dir + df['image_name'].iloc[index])
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, dsize=(WIDTH, HEIGHT))
top_x = int(df['top_x'].iloc[index] * WIDTH)
top_y = int(df['top_y'].iloc[index] * HEIGHT)
bottom_x = int(df['bottom_x'].iloc[index] * WIDTH)
bottom_y = int(df['bottom_y'].iloc[index] * HEIGHT)
center_x = 20
center_y = 20
# adding bounding box to the number plate in the car
bbox_width = 1
bbox_color = (255, 0, 0)
car = cv2.rectangle(img, (top_x, top_y), (bottom_x, bottom_y), bbox_color, bbox_width)
car = cv2.circle(car, (center_x, center_x), radius=0, color=(0, 0, 255), thickness=-1)
plt.figure(figsize=(20, 10))
plt.imshow(car)
plt.show()
# cropping the number plate to display
number_plate = Image.fromarray(img).crop((top_x + bbox_width, top_y + bbox_width, bottom_x, bottom_y))
plt.figure(figsize=(5, 5))
plt.imshow(number_plate)
plt.show()
display_car_image(1)
#FUNCTION FOR PLOTTING
def plot_loss_acc(acc,val_acc,loss,val_loss):
epochs=range(len(acc)) # Get number of epochs
#------------------------------------------------
# Plot training and validation accuracy per epoch
#------------------------------------------------
plt.plot(epochs, acc, 'r')
plt.plot(epochs, val_acc, 'b')
plt.title('Training and validation accuracy')
plt.legend(['Train', 'Validation'], loc='upper left')
plt.grid()
plt.figure()
#------------------------------------------------
# Plot training and validation loss per epoch
#------------------------------------------------
plt.plot(epochs, loss, 'r')
plt.plot(epochs, val_loss, 'b')
plt.title('Training and validation loss')
plt.legend(['Train', 'Validation'], loc='lower left')
plt.grid()
#Creating Training and Validation Data Generators
datagen = ImageDataGenerator(rescale=1./255,
validation_split=0.1,
)
train_generator = datagen.flow_from_dataframe(
df,
directory="/content/drive/MyDrive/Dataset/indian_num-plate_dataset/",
x_col="image_name",
y_col=["top_x", "top_y", "bottom_x", "bottom_y"],
target_size=(WIDTH, HEIGHT),
batch_size=32,
class_mode="other",
subset="training")
validation_generator = datagen.flow_from_dataframe(
df,
directory="/content/drive/MyDrive/Dataset/indian_num-plate_dataset/",
x_col="image_name",
y_col=["top_x", "top_y", "bottom_x", "bottom_y"],
target_size=(WIDTH, HEIGHT),
batch_size=32,
class_mode="other",
subset="validation")
#Estimating Step size for train and validation set
STEP_SIZE_TRAIN = int(np.ceil(train_generator.n / train_generator.batch_size))
STEP_SIZE_VAL = int(np.ceil(validation_generator.n / validation_generator.batch_size))
print("Train step size:", STEP_SIZE_TRAIN)
print("Validation step size:", STEP_SIZE_VAL)
train_generator.reset()
validation_generator.reset()
#Creating CNN
cnnmodel = Sequential()
cnnmodel.add(Conv2D(64, (3,3), activation='relu', input_shape=(WIDTH,HEIGHT,3)))
cnnmodel.add(MaxPooling2D(2,2))
cnnmodel.add(Conv2D(32, (3,3), activation='relu'))
cnnmodel.add(MaxPooling2D(2,2))
cnnmodel.add(Conv2D(16, (3,3), activation='relu'))
cnnmodel.add(MaxPooling2D(2,2))
cnnmodel.add(Flatten())
cnnmodel.add(Dense(128, activation="relu"))
cnnmodel.add(Dense(64, activation="relu"))
cnnmodel.add(Dense(64, activation="relu"))
cnnmodel.add(Dense(4, activation="sigmoid"))
cnnmodel.summary()
cnnmodel.compile(optimizer=Adam(lr=0.0005), loss="mse", metrics=['acc'])
history = cnnmodel.fit_generator(train_generator,
steps_per_epoch=STEP_SIZE_TRAIN,
validation_data=validation_generator,
validation_steps=STEP_SIZE_VAL,
epochs=30)
acc=history.history['acc']
val_acc=history.history['val_acc']
loss=history.history['loss']
val_loss=history.history['val_loss']
plot_loss_acc(acc,val_acc,loss,val_loss)
lucky_test_samples = np.random.randint(0, len(df), 5)
lucky_test_samples
cnnmodel.evaluate_generator(validation_generator, steps=STEP_SIZE_VAL)
for idx, row in df.iloc[lucky_test_samples].iterrows():
img = cv2.resize(cv2.imread("/content/drive/MyDrive/Dataset/indian_num-plate_dataset/" + row[0]) / 255.0, dsize=(WIDTH, HEIGHT))
y_hat = cnnmodel.predict(img.reshape(1, WIDTH, HEIGHT, 3)).reshape(-1) * WIDTH
xt, yt = y_hat[0], y_hat[1]
xb, yb = y_hat[2], y_hat[3]
img = cv2.cvtColor(img.astype(np.float32), cv2.COLOR_BGR2RGB)
image = cv2.rectangle(img, (xt, yt), (xb, yb), (0, 0, 255), 1)
plt.imshow(image)
plt.show()
#VGG16 MODEL
VGG16model = Sequential()
VGG16model.add(VGG16(weights="imagenet", include_top=False, input_shape=(HEIGHT, WIDTH, 3)))
VGG16model.add(Flatten())
VGG16model.add(Dense(128, activation="relu"))
VGG16model.add(Dense(64, activation="relu"))
VGG16model.add(Dense(64, activation="relu"))
VGG16model.add(Dense(4, activation="sigmoid"))
VGG16model.layers[-6].trainable = False
VGG16model.summary()
VGG16model.compile(optimizer=Adam(lr=0.0005), loss="mse", metrics=['acc'])
history = VGG16model.fit_generator(train_generator,
steps_per_epoch=STEP_SIZE_TRAIN,
validation_data=validation_generator,
validation_steps=STEP_SIZE_VAL,
epochs=30)
acc=history.history['acc']
val_acc=history.history['val_acc']
loss=history.history['loss']
val_loss=history.history['val_loss']
plot_loss_acc(acc,val_acc,loss,val_loss)
VGG16model.evaluate_generator(validation_generator, steps=STEP_SIZE_VAL)
for idx, row in df.iloc[lucky_test_samples].iterrows():
img = cv2.resize(cv2.imread("/content/drive/MyDrive/Dataset/indian_num-plate_dataset/" + row[0]) / 255.0, dsize=(WIDTH, HEIGHT))
y_hat = VGG16model.predict(img.reshape(1, WIDTH, HEIGHT, 3)).reshape(-1) * WIDTH
xt, yt = y_hat[0], y_hat[1]
xb, yb = y_hat[2], y_hat[3]
img = cv2.cvtColor(img.astype(np.float32), cv2.COLOR_BGR2RGB)
image = cv2.rectangle(img, (xt, yt), (xb, yb), (0, 0, 255), 1)
plt.imshow(image)
plt.show()
#MOBILENETV2
MNV2model = Sequential()
MNV2model.add(MobileNetV2(weights="imagenet", include_top=False, input_shape=(HEIGHT, WIDTH, 3)))
MNV2model.add(Flatten())
MNV2model.add(Dense(128, activation="relu"))
MNV2model.add(Dense(64, activation="relu"))
MNV2model.add(Dense(64, activation="relu"))
MNV2model.add(Dense(4, activation="sigmoid"))
MNV2model.layers[-6].trainable = False
MNV2model.summary()
MNV2model.compile(optimizer=Adam(lr=0.0005), loss="mse", metrics=['acc'])
history3 = MNV2model.fit_generator(train_generator,
steps_per_epoch=STEP_SIZE_TRAIN,
validation_data=validation_generator,
validation_steps=STEP_SIZE_VAL,
epochs=30)
acc=history3.history['acc']
val_acc=history3.history['val_acc']
loss=history3.history['loss']
val_loss=history3.history['val_loss']
plot_loss_acc(acc,val_acc,loss,val_loss)
MNV2model.evaluate_generator(validation_generator, steps=STEP_SIZE_VAL)
for idx, row in df.iloc[lucky_test_samples].iterrows():
img = cv2.resize(cv2.imread("/content/drive/MyDrive/Dataset/indian_num-plate_dataset/" + row[0]) / 255.0, dsize=(WIDTH, HEIGHT))
y_hat = MNV2model.predict(img.reshape(1, WIDTH, HEIGHT, 3)).reshape(-1) * WIDTH
xt, yt = y_hat[0], y_hat[1]
xb, yb = y_hat[2], y_hat[3]
img = cv2.cvtColor(img.astype(np.float32), cv2.COLOR_BGR2RGB)
image = cv2.rectangle(img, (xt, yt), (xb, yb), (0, 0, 255), 1)
plt.imshow(image)
plt.show()