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testing.py

+import os
+import cv2
+from skimage import io
+import numpy as np
+
+from keras.models import Sequential
+from keras.layers import Dense, Activation, Flatten, Dropout
+from keras.layers import Conv2D, MaxPooling2D
+
+data = np.load('Processed_Data/data.npy')
+target = np.load('Processed_Data/target.npy')
+
+# In[2]:
+
+
+from sklearn.model_selection import train_test_split
+
+train_data, test_data, train_target, test_target = train_test_split(data, target, test_size=0.1)
+
+# In[3]:
+
+
+
+model = Sequential()
+
+model.add(Conv2D(256, (3, 3), input_shape=data.shape[1:]))
+model.add(Activation('relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+
+model.add(Conv2D(128, (3, 3)))
+model.add(Activation('relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+
+model.add(Flatten())
+
+model.add(Dropout(0.5))
+model.add(Dense(64, activation='relu'))
+
+model.add(Dense(10, activation='softmax'))
+
+model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
+
+model.load_weights('model.h5')
+
+rome_data_dic = {0: 'VI', 1: 'II', 2: 'I', 3: 'VII', 4: 'VIII', 5: 'IV', 6: 'IX', 7: 'V', 8: 'X', 9: 'III'}
+img = cv2.imread('Data/i/i_001.png')
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+resized = cv2.resize(gray, (50, 50))
+normalized = resized / 255.0
+reshaped = np.reshape(normalized, (1, 50, 50, 1))
+
+result = model.predict(reshaped)
+label = np.argmax(result, axis=1)[0]
+prob = np.max(result, axis=1)[0]
+prob = round(prob, 2) * 100
+
+# print(result)
+print(np.argmax(result, axis=1))
+
+# print(np.max(result, axis=1)[0])
+print(rome_data_dic[np.argmax(result, axis=1)[0]])
+
+
+