def parse_data(self, path, keras_mode=False):
print(keras_mode)
file_list = glob(os.path.join(path, 'figs_0', '*.png'))
ids = list(set([x[:-4].split(os.sep)[-1] for x in file_list]))
# ids.remove('Thumb')
images = []
for id in tqdm(ids):
image_path = [
x for x in file_list if x.find(id) > -1 and x.endswith('png')
][0]
if keras_mode:
img = imread(image_path, mode='RGB')
img = np.expand_dims(img, axis=0)
img = vgg16.preprocess_input(img)
else:
img = imread(image_path)
img = img / 255
img = img.reshape([self.imsize, self.imsize, 1])
images.append(img)
return images, ids
def G_CAM(self, img_path, IMG_HIGH, IMG_WIDTH, layer_name):
IMG = image.load_img(img_path, target_size=(IMG_HIGH, IMG_WIDTH))
plt.show()
img = image.img_to_array(IMG)
img = np.expand_dims(img, axis=0)
img = preprocess_input(img)
predictions = self.model.predict_generator(self.data.test_generator)
filenames = self.data.test_generator.filenames
print(filenames)
r = np.argmax(predictions, axis=-1) # multiple categories
print('label: ', r)
img_output = self.model.output[:, r]
conv_layer = self.model.get_layer(layer_name)
# conv_layer = model.layers[1].get_layer(layer_name)
grads = k.gradients(img_output, conv_layer.output)[0]
print('grads shape:', grads.shape)
pooled_grads = k.mean(grads, axis=(0, 1, 2))
print('mean gradiant: ', pooled_grads.shape)
iterate = k.function([self.model.input],
[pooled_grads, conv_layer.output[0]])
pooled_grades_value, conv_layer_output_value = iterate([img])
print(pooled_grades_value.shape)
print(conv_layer_output_value.shape)
output = conv_layer_output_value[0, :]
weights = pooled_grades_value #np.mean(grads_val, axis=(0, 1))
cam = np.ones(output.shape[0:2], dtype=np.float32)
for i, w in enumerate(weights):
cam += w * output[:, :, i]
cam /= np.max(cam)
w, h, j = IMG.shape
cam = cv2.resize(cam, (h, w))
heatmap = cv2.applyColorMap(np.uint8(255 * cam), cv2.COLORMAP_RAINBOW)
# print(heatmap)
heatmap[np.where(cam < 0.02)] = 0
# img = heatmap * 0.5 + img
img = img[0, :, :, :]
print(heatmap.shape)
print(IMG.shape)
img = cv2.addWeighted(heatmap, 0.5, IMG, 0.9, 0)
plt.imshow(heatmap)
plt.show()
plt.imshow(img)
plt.show()
def extract_features(self, input_image):
'''
:param input_image: RGB image array of size w x h x 3
:return: 1-D feature vector of size: 7x7x512 = 25,088
'''
img_data = preprocess_input(input_image)
features = self.model.predict(img_data)
features = features.flatten()
return features
def display_heatmap(self, img_path, img_high, img_width, layer_name,
load_data):
IMG = image.load_img(img_path, target_size=(img_high, img_width))
plt.show()
img = image.img_to_array(IMG)
img = np.expand_dims(img, axis=0)
img = preprocess_input(img)
hmap = heatmap()
img_heatmap = hmap.create_heat_map(self.model, layer_name, img,
img_high, img_width, 3, load_data)
print('heatmap')
plt.imshow(img_heatmap)
plt.show()
img_heatmap = hmap.super_impose_heatmap(img_path, img_heatmap)
plt.imshow(img_heatmap)
plt.show()
def preprocess_input(*args, **kwargs):
return vgg16.preprocess_input(*args, **kwargs)
x = (width - square_size) // 2 y = (height - square_size) // 2 image = image.crop((x, y, square_size, square_size)) plt.imshow(np.asarray(image)) # Resizes to the networks required input size. target_square_size = max(dimension for dimension in model.layers[0].input_shape if dimension) image = image.resize((target_square_size, target_square_size), Image.ANTIALIAS) plt.imshow(np.asarray(image)) numpy_image = img.img_to_array(image) print(numpy_image.shape) image_batch = np.expand_dims(numpy_image, axis=0) print(image_batch.shape) pre_processed = preprocess_input(image_batch, data_format='channels_last') print(pre_processed.shape) features = model.predict(pre_processed) print(features.shape) import pprint pprint.pprint(decode_predictions(features, top=10)) FLCIKR_KEY = os.environ['FLICKR_KEY'] FLICKR_SECRET = os.environ['FLICKR_SECRET'] flickr = flickrapi.FlickrAPI(FLCIKR_KEY, FLICKR_SECRET, format='parsed-json') results = flickr.photos.search(text='cat', per_page='10', sort='relevance')
https://colab.research.google.com/drive/1kLOtfIlx8gpJamwqZSXZiJ67Tz7f1Pah
"""
import tensorflow
from keras_preprocessing.image import load_img, img_to_array
from keras_applications.vgg16 import preprocess_input, decode_predictions, VGG16
model = VGG16()
images = load_img("speaker.jpg", target_size=(224, 224))
img_array = img_to_array(images)
img_array
img_array = img_array.reshape(
(1, img_array.shape[0], img_array.shape[1], img_array.shape[2]))
img_array.shape
image = preprocess_input(img_array)
image
img_pred = model.predict(image)
d_img_pred = decode_predictions(img_pred)
d_img_pred
def batch_preprocess_input(x_batch, network):
if network == 'Xception':
x_batch = xception.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif network == 'VGG16':
x_batch = vgg16.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif network == 'VGG19':
x_batch = vgg19.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif network == 'ResNet50' or network == 'ResNet101' or network == 'ResNet152':
x_batch = resnet.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif network == 'ResNet50V2' or network == 'ResNet101V2' or network == 'ResNet152V2':
x_batch = resnet_v2.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif network == 'ResNeXt50' or network == 'ResNeXt101':
x_batch = resnext.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif network == 'InceptionV3':
x_batch = inception_v3.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif network == 'InceptionResNetV2':
x_batch = inception_resnet_v2.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif network == 'MobileNet':
x_batch = mobilenet.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif network == 'MobileNetV2':
x_batch = mobilenet_v2.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif network == 'DenseNet121' or network == 'DenseNet169' or network == 'DenseNet201':
x_batch = densenet.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif network == 'NASNetMobile' or network == 'NASNetLarge':
x_batch = nasnet.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif 'EfficientNet' in network:
x_batch = efficientnet.preprocess_input(x_batch)
else:
return None
return x_batch
def get_labels(output):
predicted_class_indices = np.argmax(output, axis=1)
predicted_classes = [
'Real' if x == 0 else 'Barca' for x in predicted_class_indices
]
return predicted_classes
test_dir = 'C:\\Users\\Rani\\Desktop\\AI Pycharm Project\\labelled data 2\\1st half test\\'
images = np.load(test_dir + 'test_images.npy')
labels = np.load(test_dir + 'test_labels.npy')
X_test = []
for image in images:
cv2.rectangle(image, (40, 16), (70, 21), (255, 255, 255), 10)
X_test += [preprocess_input(image)]
X_test = np.array(X_test)
X_test = X_test[labels != 'Other']
labels = labels[labels != 'Other']
real_images = X_test[labels == 'Real']
barca_images = X_test[labels == 'Barca']
print('real_images.shape=', real_images.shape)
model = TeamClf()
model = model.model
for layer in model.layers[0].layers:
layer.trainable = False
saved_weights_path = 'C:\\Users\\Rani\\Desktop\\AI Pycharm Project\\fine_tuning_approach\\transfer\\weights\\'
model.load_weights(saved_weights_path + 'weights-improvement-21-0.85.hdf5')
