Plik z algorytmem i uporządkowanie datasetu.

algorytm_biedronki.py

@@ -0,0 +1,135 @@
+#%%
+
+from tensorflow.keras.layers import Input, Lambda, Dense, Flatten,Dropout
+from tensorflow.keras.models import Model
+from tensorflow.keras.applications.vgg19 import VGG19
+from tensorflow.keras.applications.vgg19 import preprocess_input
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.models import Sequential
+import numpy as np
+import pandas as pd
+import os
+import cv2
+import matplotlib.pyplot as plt
+# %%
+os.chdir(r"C:\Users\pittb\Documents\biedronkapp\BiedronkApp\biedronki_dataset")
+train_path="train"
+test_path="test"
+# %%
+x_train=[]
+
+for folder in os.listdir(train_path):
+
+    sub_path=train_path+"/"+folder
+
+    for img in os.listdir(sub_path):
+        image_path=sub_path+"/"+img
+
+        img_arr=cv2.imread(image_path)
+
+        img_arr=cv2.resize(img_arr,(224,224))
+
+        x_train.append(img_arr)
+
+x_test=[]
+
+for folder in os.listdir(test_path):
+
+    sub_path=test_path+"/"+folder
+
+    for img in os.listdir(sub_path):
+
+        image_path=sub_path+"/"+img
+
+        img_arr=cv2.imread(image_path)
+
+        img_arr=cv2.resize(img_arr,(224,224))
+
+        x_test.append(img_arr)
+# %%
+train_x=np.array(x_train)
+test_x=np.array(x_test)
+
+train_x=train_x/255.0
+test_x=test_x/255.0
+# %%
+train_datagen = ImageDataGenerator(rescale = 1./255)
+test_datagen = ImageDataGenerator(rescale = 1./255)
+# %%
+training_set = train_datagen.flow_from_directory(train_path,
+                                                 target_size = (224, 224),
+                                                 batch_size = 32,
+                                                 class_mode = 'sparse')
+test_set = test_datagen.flow_from_directory(test_path,
+                                            target_size = (224, 224),
+                                            batch_size = 32,
+                                            class_mode = 'sparse')
+# %%
+train_y=training_set.classes
+test_y=test_set.classes
+
+training_set.class_indices
+
+train_y.shape,test_y.shape
+# %%
+IMAGE_SIZE = [224, 224]
+
+vgg = VGG19(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)
+
+for layer in vgg.layers:
+    layer.trainable = False
+
+# %%
+# our layers - you can add more if you want
+x = Flatten()(vgg.output)
+
+prediction = Dense(2, activation='softmax')(x)
+# %%
+# create a model object
+model = Model(inputs=vgg.input, outputs=prediction)
+
+# view the structure of the model
+model.summary()
+# %%
+model.compile(
+  loss='sparse_categorical_crossentropy',
+  optimizer="adam",
+  metrics=['accuracy']
+)
+# %%
+from tensorflow.keras.callbacks import EarlyStopping
+early_stop=EarlyStopping(monitor='val_loss',mode='min',verbose=1,patience=5)
+# %%
+history = model.fit(
+  train_x,
+  train_y,
+  epochs=10,
+  callbacks=[early_stop],
+  batch_size=32,shuffle=True)
+# %%
+plt.plot(history.history['loss'], label='train loss')
+plt.legend()
+
+plt.savefig('vgg-loss-rps-1.png')
+plt.show()
+# %%
+# accuracies
+plt.plot(history.history['accuracy'], label='train acc')
+plt.legend()
+
+plt.savefig('vgg-acc-rps-1.png')
+plt.show()
+# %%
+model.evaluate(test_x,test_y,batch_size=6)
+# %%
+from sklearn.metrics import accuracy_score,classification_report,confusion_matrix
+import numpy as np
+
+y_pred=model.predict(test_x)
+y_pred=np.argmax(y_pred,axis=1)
+
+accuracy_score(y_pred,test_y)
+
+print(classification_report(y_pred,test_y))
+# %%