wip

.gitignore

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+venv

README.md

@@ -1,2 +1,6 @@
 # projekt_widzenie
 
+## Run apllication
+1. `pip install -r requirements.txt`
+2. `streamlit run main.py`
+3. On http://localhost:8501/ you should see the app

main.py

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+import streamlit as st
+
+
+if __name__ == "__main__":
+
+    st.set_page_config(
+        page_title="Projekt widzenie"
+    )
+    st.title("Projekt rozpoznawanie liter z alfabetu znaków migowych z wideo")
+
+    st.write('Hello world')

model_pred/pred.py

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+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+
+model = keras.models.load_model('sign_car_detection_model')
+
+
+class_names = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', 'del', 'nothing', 'space']
+
+
+def pred_sign_char(path_to_img: str):
+
+    pred = []
+    test_image = tf.keras.utils.load_img(path_to_img, 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)
+
+
+    print(result)
+    pred.append(class_names[np.argmax(result)])
+    print(pred)
+    return pred[0]

requirements.txt

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+streamlit
+pandas
+tensorflow
+numpy

training_model/projekt_widzenie.py

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+# %% [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))
+
+
+# %%
+
+
+