| | import glob |
| | import numpy as np |
| | from PIL import Image |
| | from keras import Model |
| | from keras.layers import GlobalAveragePooling2D, GlobalMaxPooling2D |
| | from keras.applications.vgg19 import VGG19 |
| | from keras.applications.resnet50 import ResNet50 |
| | from keras.applications.inception_v3 import InceptionV3 |
| |
|
| |
|
| | def get_filenames(glob_pattern, recursive=True): |
| | """Extracts list of filenames (full paths) based on specific glob path pattern. |
| | |
| | Parameters |
| | ---------- |
| | glob_pattern : str |
| | Glob pattern for glob to extract filenames, eg. "directory/**/*.jpg" |
| | recursive : bool, optional |
| | Recursively search through subdirectories, by default True |
| | |
| | Returns |
| | ------- |
| | list |
| | List of file paths |
| | """ |
| | all_files = glob.glob(glob_pattern, recursive=recursive) |
| | print('Found %s files using pattern: %s' % (len(all_files), glob_pattern)) |
| | return all_files |
| |
|
| |
|
| | def expand2square(pil_img, background_color): |
| | """Function to pad an image to square using specific bg clr. |
| | |
| | Parameters |
| | ---------- |
| | pil_img : PIL.Image |
| | Pillow Image object that should be processed |
| | background_color : int |
| | Integer value representing bg color |
| | |
| | Returns |
| | ------- |
| | PIL.Image |
| | Square-padded image object |
| | """ |
| | width, height = pil_img.size |
| | if width == height: |
| | return pil_img |
| | elif width > height: |
| | result = Image.new(pil_img.mode, (width, width), background_color) |
| | result.paste(pil_img, (0, (width - height) // 2)) |
| | return result |
| | else: |
| | result = Image.new(pil_img.mode, (height, height), background_color) |
| | result.paste(pil_img, ((height - width) // 2, 0)) |
| | return result |
| |
|
| |
|
| | def get_images(filenames, target_size=(200,200), color='RGB', bg_clr=0): |
| | """Reads image files from provided file paths list, applies square-padding, |
| | resizes all images into target size and returns them as a single numpy array |
| | |
| | Parameters |
| | ---------- |
| | filenames : list |
| | List of image file paths |
| | target_size : tuple, optional |
| | Target size for all the images to be resized to, by default (200,200) |
| | color : str, optional |
| | Color mode strategy for PIL when loading images, by default 'RGB' |
| | bg_clr : int, optional |
| | Integer representing background color used for square-padding, by default 0 |
| | |
| | Returns |
| | ------- |
| | numpy.array |
| | Numpy array with resized images |
| | """ |
| | imgs_list = [] |
| | for filename in filenames: |
| | img = Image.open(filename).convert(color) |
| | im_square = expand2square(img, bg_clr) |
| | im_res = im_square.resize(target_size) |
| | imgs_list.append(np.array(im_res)) |
| |
|
| | return np.asarray(imgs_list) |
| |
|
| |
|
| | def create_feat_extractor(base_model, pooling_method='avg'): |
| | """Creates a features extractor based on the provided base network. |
| | |
| | Parameters |
| | ---------- |
| | base_model : keras.Model |
| | Base network for feature extraction |
| | pooling_method : str, optional |
| | Pooling method that will be used as the last layer, by default 'avg' |
| | |
| | Returns |
| | ------- |
| | keras.Model |
| | Ready to use feature extractor |
| | """ |
| | assert pooling_method in ['avg', 'max'] |
| | |
| | x = base_model.output |
| | if pooling_method=='avg': |
| | x = GlobalAveragePooling2D()(x) |
| | elif pooling_method=='max': |
| | x = GlobalMaxPooling2D()(x) |
| | model = Model(input=base_model.input, output=[x]) |
| |
|
| | return model |
| |
|
| |
|
| | def extract_features(imgs_np, pretrained_model="resnet50", pooling_method='avg'): |
| | """Takes in an array of fixed size images and returns features/embeddings |
| | returned by one of the selected pretrained networks. |
| | |
| | Parameters |
| | ---------- |
| | imgs_np : numpy.array |
| | Numpy array of images |
| | pretrained_model : str, optional |
| | Name of the pretrained model to be used, by default "resnet50" |
| | ['resnet50', 'inception_v3', 'vgg19'] |
| | pooling_method : str, optional |
| | Defines the last pooling layer that should be applied, by default 'avg' |
| | ['avg', 'max'] |
| | |
| | Returns |
| | ------- |
| | numpy.array |
| | Array of embeddings vectors. Each row represents embeddings for single input image |
| | """ |
| | print('Input images shape: ', imgs_np.shape) |
| | pretrained_model = pretrained_model.lower() |
| | assert pretrained_model in ['resnet50', 'inception_v3', 'vgg19'] |
| | assert pooling_method in ['avg', 'max'] |
| |
|
| | model_args={ |
| | 'weights': 'imagenet', |
| | 'include_top': False, |
| | 'input_shape': imgs_np[0].shape |
| | } |
| |
|
| | if pretrained_model=="resnet50": |
| | base = ResNet50(**model_args) |
| | from keras.applications.resnet50 import preprocess_input |
| | elif pretrained_model=="inception_v3": |
| | base = InceptionV3(**model_args) |
| | from keras.applications.inception_v3 import preprocess_input |
| | elif pretrained_model=="vgg19": |
| | base = VGG19(**model_args) |
| | from keras.applications.vgg19 import preprocess_input |
| |
|
| | feat_extractor = create_feat_extractor(base, pooling_method=pooling_method) |
| |
|
| | imgs_np = preprocess_input(imgs_np) |
| | embeddings_np = feat_extractor.predict(imgs_np) |
| | print('Features shape: ', embeddings_np.shape) |
| | |
| | return embeddings_np |
| |
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