def calculate_test1_accuracy():
# testing on the labelled Test1 video
# vgg_model = final_model.Model().model
vgg_model = model77().model
# vgg_model.load_weights('weights-improvement-16-0.87.hdf5')
# vgg_model.load_weights('weights-improvement-555-0.87.hdf5')
vgg_model.load_weights(
r'C:\Users\Rani\Desktop\AI Pycharm Project\vgg_features_model\backup\smaller 77\\'
+ 'weights-improvement-607-0.88.hdf5')
adam = Adam(lr=0.00001,
beta_1=0.9,
beta_2=0.999,
epsilon=None,
decay=0.0,
amsgrad=False)
vgg_model.compile(loss='categorical_crossentropy',
optimizer=adam,
metrics=['accuracy'])
# test_folder = 'C:\\Users\\Rani\\Desktop\\AI Pycharm Project\\labelled data\\test\\'
test_folder = 'C:\\Users\\Rani\\Desktop\\AI Pycharm Project\\labelled data\\test2\\'
X_test1 = np.load(test_folder + 'image_array.npy')
y_test1 = np.load(test_folder + 'labels_array.npy')
y_test1[y_test1 == 'Real'] = 0
y_test1[y_test1 == 'Barca'] = 1
y_test1[y_test1 == 'Other'] = 2
y_test1 = to_categorical(y_test1, 3)
# #####################
# # for layer in vgg_model.layers[:-3]:
# # print(layer)
# # layer.trainable = False
#
# X_train = X_test1[:200]
# y_train = y_test1[:200]
# X_val = X_test1[200:250]
# y_val = y_test1[200:250]
# vgg = VGG16(include_top=False, input_shape=(288, 512, 3))
# vgg_model.fit(get_features(X_train, vgg, flip=True), y_train, epochs=200, validation_data=(get_features(X_val, vgg, flip=True), y_val), verbose=2)
#
# X_test1 = X_test1[250:]
# y_test1 = y_test1[250:]
# #####################
X_val = X_test1
y_val = y_test1
vgg = VGG16(include_top=False, input_shape=(288, 512, 3))
predictions = get_actual_labels(predict_aux(X_val, vgg_model, vgg))
# print('normal test accuracy=', vgg_model.evaluate(get_image_features(X_val, vgg), y_val, batch_size=4))
print('normal test accuracy=',
vgg_model.evaluate(get_features(X_val, vgg), y_val, batch_size=4))
print('non_neutral test accuracy=',
non_neutral_accuracy(get_actual_labels(y_val), predictions))
def predict_labels():
vgg = VGG16(include_top=False, input_shape=(288, 512, 3))
# testing on the labelled Test1 video
# vgg_model = final_model.Model().model
vgg_model = model77().model
# vgg_model.load_weights('weights-improvement-28-0.88.hdf5')
# vgg_model.load_weights('weights-improvement-555-0.87.hdf5')
vgg_model.load_weights(
r'C:\Users\Rani\Desktop\AI Pycharm Project\vgg_features_model\backup\smaller 77\\'
+ 'weights-improvement-607-0.88.hdf5')
cap = cv2.VideoCapture(
"C:\\Users\\Rani\\Desktop\\AI project\\data (video)\\Test2.mp4")
totalFrames = cap.get(cv2.CAP_PROP_FRAME_COUNT)
i = 0
labels = []
while i < totalFrames:
frames = []
for j in range(i, i + 100):
ret, frame = cap.read()
if ret:
frames += [frame]
else:
print('video read error (or finished reading)')
predictions = get_actual_labels(predict_aux(frames, vgg_model, vgg))
labels += list(predictions)
print(i)
i += 100
if (i > 0) and (i % 1000) == 0:
np.save('Test2_predicted_labels_new77.npy', np.array(labels))
np.save('Test2_predicted_labels_new77.npy', np.array(labels))
def predict_labels():
vgg_model = VGG16()
# re-structure the model
vgg_model.layers.pop()
vgg_model = Model(inputs=vgg_model.inputs,
outputs=vgg_model.layers[-1].output)
# testing on the labelled Test1 video
vgg_model = final_model.Model().model
# vgg_model.load_weights('weights-improvement-28-0.88.hdf5')
vgg_model.load_weights('weights-improvement-06-0.88.hdf5')
cap = cv2.VideoCapture(
'C:\\Users\\Rani\\Desktop\\AI project\\data (video)\\Test1.mp4')
totalFrames = cap.get(cv2.CAP_PROP_FRAME_COUNT)
i = 0
labels = []
while i < totalFrames:
frames = []
for j in range(i, i + 100):
ret, frame = cap.read()
if ret:
frames += [cv2.resize(frame, (0, 0), fx=0.4, fy=0.4)]
else:
print('video read error (or finished reading)')
predictions = get_actual_labels(predict_aux(frames, vgg_model, vgg))
labels += list(predictions)
print(i)
i += 100
if (i > 0) and (i % 1000) == 0:
np.save('Test1_predicted_labels_new.npy', np.array(labels))
np.save('Test1_predicted_labels_new.npy', np.array(labels))
def calculate_test1_accuracy():
# testing on the labelled Test1 video
vgg_model = final_model().model
# vgg_model.load_weights('weights-improvement-16-0.87.hdf5')
vgg_model.load_weights('weights-improvement-257-0.80.hdf5')
adam = Adam(lr=0.0001,
beta_1=0.9,
beta_2=0.999,
epsilon=None,
decay=0.0,
amsgrad=False)
vgg_model.compile(loss='categorical_crossentropy',
optimizer=adam,
metrics=['accuracy'])
# test_folder = 'C:\\Users\\Rani\\Desktop\\AI Pycharm Project\\labelled data\\test\\'
test_folder = 'C:\\Users\\Rani\\Desktop\\AI Pycharm Project\\labelled data 2\\test1\\'
X_test1 = np.load(test_folder + 'image_array.npy')
y_test1 = np.load(test_folder + 'labels_array.npy')
y_test1[y_test1 == 'Real'] = 0
y_test1[y_test1 == 'Barca'] = 1
y_test1[y_test1 == 'Other'] = 2
y_test1 = to_categorical(y_test1, 3)
X_val = X_test1
y_val = y_test1
vgg = VGG16()
# re-structure the model
vgg.layers.pop()
vgg.layers.pop()
vgg = Model(inputs=vgg.inputs, outputs=vgg.layers[-1].output)
# testing on the labelled Test1 video
predictions = get_actual_labels(predict_aux(X_val, vgg_model, vgg))
print('normal test accuracy=',
vgg_model.evaluate(get_image_features(X_val, vgg), y_val))
print('non_neutral test accuracy=',
non_neutral_accuracy(get_actual_labels(y_val), predictions))
predicted_class_indices = np.argmax(pred, axis=1)
labels = train_generator.class_indices
labels = dict((v, k) for k, v in labels.items())
predictions = np.array([labels[k] for k in predicted_class_indices])
print('manual acc calculation: ',
sum(predictions == y_test) / y_test.shape[0])
print('sklearn acc calculation: ', accuracy_score(y_test, predictions))
y_test[y_test == 'Real'] = 0
y_test[y_test == 'Barca'] = 1
y_test[y_test == 'Other'] = 2
y_test = keras.utils.to_categorical(y_test, 3)
print('model.evaluate: ', model.evaluate(X_test, y_test, batch_size=8))
print('non_neutral test accuracy=',
non_neutral_accuracy(get_actual_labels(y_test), predictions))
# print('non_neutral test accuracy=', non_neutral_accuracy(get_actual_labels(y_test), get_actual_labels(model.predict(X_test, batch_size=8))))
exit()
X_test = np.array([
preprocess_input(x) for x in np.load(test_folder + 'image_array.npy')
])
for image in X_test:
cv2.rectangle(image, (40, 16), (70, 21), (255, 255, 255), 10)
y_test = np.load(test_folder + 'labels_array.npy')
y_test[y_test == 'Real'] = 0
y_test[y_test == 'Barca'] = 1
y_test[y_test == 'Other'] = 2
y_test = keras.utils.to_categorical(y_test, 3)
metrics=['accuracy'])
# print(model.model.summary())
# exit()
weights_file_name = "weights-improvement-{epoch:02d}-{val_acc:.2f}.hdf5"
checkpoint = ModelCheckpoint(saved_weights_path + weights_file_name,
monitor='val_acc',
verbose=1,
save_best_only=True,
mode='max')
callbacks_list = [checkpoint]
model.model.load_weights('weights-improvement-06-0.81.hdf5')
##############
# print(model.model.summary())
# exit()
print('normal validation accuracy=', model.model.evaluate(X_val, y_val))
print(
'non_neutral validation accuracy=',
non_neutral_accuracy(get_actual_labels(y_val),
get_actual_labels(model.model.predict(X_val))))
exit()
##############
model.model.fit(X_train,
y_train,
batch_size=64,
epochs=50,
callbacks=callbacks_list,
validation_data=(X_val, y_val),
verbose=2)