132 lines
2.9 KiB
Python
132 lines
2.9 KiB
Python
# %% [markdown]
|
|
# # Import the required libraries
|
|
|
|
# %%
|
|
import pandas as pd
|
|
import numpy as np
|
|
import tensorflow as tf
|
|
|
|
# %% [markdown]
|
|
# # Preprocessing the image data
|
|
|
|
# %%
|
|
#taking the train validation ratio as 4:1
|
|
|
|
# %%
|
|
batch_size=32
|
|
img_height=256
|
|
img_width=256
|
|
|
|
|
|
train_ds = tf.keras.utils.image_dataset_from_directory(
|
|
"asl_alphabet_train/asl_alphabet_train/",
|
|
validation_split=0.2,
|
|
subset="training",
|
|
seed=123,
|
|
image_size=(img_height, img_width),
|
|
batch_size=batch_size)
|
|
|
|
# %%
|
|
test_ds = tf.keras.utils.image_dataset_from_directory(
|
|
"asl_alphabet_train/asl_alphabet_train/",
|
|
validation_split=0.2,
|
|
subset="validation",
|
|
seed=123,
|
|
image_size=(img_height, img_width),
|
|
batch_size=batch_size)
|
|
|
|
# %%
|
|
class_names = train_ds.class_names
|
|
print("Class names:",class_names)
|
|
print("Total classes:",len(class_names))
|
|
|
|
# %%
|
|
#random samples of images from the train data
|
|
|
|
# %%
|
|
import matplotlib.pyplot as plt
|
|
|
|
plt.figure(figsize=(10, 10))
|
|
for images, labels in train_ds.take(1):
|
|
for i in range(29):
|
|
ax = plt.subplot(6,5 , i + 1)
|
|
plt.imshow(images[i].numpy().astype("uint8"))
|
|
plt.title(class_names[labels[i]])
|
|
plt.axis("off")
|
|
|
|
# %% [markdown]
|
|
# # Modelling and training
|
|
|
|
# %%
|
|
#modelling
|
|
|
|
from tensorflow.keras import Sequential
|
|
from tensorflow.keras import layers
|
|
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
|
|
|
|
model = Sequential([
|
|
layers.Rescaling(1./255, input_shape=(img_height, img_width, 3)),
|
|
layers.Conv2D(16, 3, padding='same', activation='relu'),
|
|
layers.MaxPooling2D(),
|
|
layers.Conv2D(32, 3, padding='same', activation='relu'),
|
|
layers.MaxPooling2D(),
|
|
layers.Conv2D(64, 3, padding='same', activation='relu'),
|
|
layers.MaxPooling2D(),
|
|
layers.Flatten(),
|
|
layers.Dense(128, activation='relu'),
|
|
layers.Dense(29,activation='softmax')
|
|
])
|
|
|
|
# %%
|
|
model.summary()
|
|
|
|
# %%
|
|
model.compile(loss='sparse_categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
|
|
|
|
# %%
|
|
model.fit(train_ds, batch_size=128,validation_batch_size=128, validation_data=test_ds,epochs=20)
|
|
|
|
|
|
model.save('sign_car_detection_model')
|
|
|
|
|
|
|
|
# %% [markdown]
|
|
# # Prediction on the test data
|
|
|
|
# %%
|
|
import os
|
|
|
|
# Get the list of all files and directories
|
|
path = "asl_alphabet_test/asl_alphabet_test/"
|
|
dir_list = os.listdir(path)
|
|
|
|
print(dir_list)
|
|
|
|
# %%
|
|
from sklearn.preprocessing import OneHotEncoder, LabelEncoder
|
|
tf.keras.utils.load_img
|
|
|
|
actual=[]
|
|
pred=[]
|
|
for i in dir_list:
|
|
actual.append(i.split('_')[0])
|
|
test_image = tf.keras.utils.load_img('asl_alphabet_test/asl_alphabet_test/'+i, target_size = (256, 256))
|
|
test_image = tf.keras.utils.img_to_array(test_image)
|
|
test_image = np.expand_dims(test_image, axis = 0)
|
|
result = model.predict(test_image)
|
|
pred.append(class_names[np.argmax(result)])
|
|
|
|
# %%
|
|
from sklearn.metrics import confusion_matrix, classification_report
|
|
from sklearn.metrics import accuracy_score
|
|
|
|
print("Test accuracy=",accuracy_score(pred,actual))
|
|
print("Classification report:\n",classification_report(pred,actual))
|
|
|
|
|
|
# %%
|
|
|
|
|
|
|