def preprocess_image(image, target_size): img = image.resize(target_size) img = img_to_array(img) img = preprocess_input(img) img = np.expand_dims(img, axis=0) return img
def preprocess_image(image): #image = tf.image.decode_jpeg(image, channels=3) #image = tf.image.resize(image, [224, 224]) #image = keras.applications.xception.preprocess_input(image) ## 수정해야함 image = tf.image.decode_jpeg(image, channels=3) image = tf.image.resize(image, [224, 224]) # antialias = True image = preprocess_input(image) return image
def format_image(self, image): """Resize the images to a fixed input size, and rescale the input channels to a range of [-1, 1]. (According to https://www.tensorflow.org/tutorials/images/transfer_learning) """ image = tf.cast(image, tf.float32) # \/ does the same # image = (image / 127.5) - 1 image = preprocess_input( image ) # https://github.com/keras-team/keras-applications/blob/master/keras_applications/imagenet_utils.py#L152 image = tf.image.resize(image, (self.IMG_SIZE, self.IMG_SIZE)) return image
def matching_dog(model, img_input_path, embedding_path, merged_path, target_size, top_n=15): """Returns list of top_n similar dog objects""" img = image.load_img(img_input_path, target_size=target_size) img_data = image.img_to_array(img) img_data = np.expand_dims(img_data, axis=0) img_data = preprocess_input(img_data) emb_input = model.predict(img_data)[0] merged = pd.read_csv(merged_path) emb_matrix = np.loadtxt(embedding_path) scores = [] for i in range(len(emb_matrix)): scores.append(cos_d(emb_input, list(emb_matrix[i]))) ranks = [[index, merged.photo_url[index], merged.links[index], merged.titles[index], score] for index, score in enumerate(scores, 0)] ranks.sort(key=lambda x: x[-1]) dogs = [] seen = set() ind = 0 while len(dogs) < top_n: instance = ranks[ind] if instance[1] not in seen: seen.add(instance[1]) else: ind += 1 continue profile = PetProfile(instance[2],instance[1]) dogs.append(profile) ind += 1 return dogs
def preprocess_image(image): image = tf.image.decode_jpeg(image, channels=3) image = tf.image.resize(image, [IMG_SIZE, IMG_SIZE]) # antialias = True image = preprocess_input(image) return image
def preprocess_input(image): image = efn.preprocess_input(image) image = tf.cast(tf.reshape(1, 300, 300, 3), tf.float32) return image
import os import sys import numpy as np from skimage.io import imread import matplotlib.pyplot as plt sys.path.append('..') # if you use tensorflow.keras: from efficientnet.tfkeras import EfficientNetB0 from efficientnet.tfkeras import center_crop_and_resize, preprocess_input from tensorflow.keras.applications.imagenet_utils import decode_predictions # test image test_b_2 = "../predict/pics/ROI.png" image = imread(test_b_2) plt.figure(figsize=(10, 10)) plt.imshow(image) plt.show() # loading pretrained model model = EfficientNetB0(weights='imagenet') # preprocess input image_size = model.input_shape[1] x = center_crop_and_resize(image, image_size=image_size) x = preprocess_input(x) print(x.shape) x = np.expand_dims(x, 0) # make prediction and decode y = model.predict(x) print(decode_predictions(y))
def _get_panda_input(input_shape): image = imread(PANDA_PATH) image = efn.center_crop_and_resize(image, input_shape[1]) image = efn.preprocess_input(image) image = np.expand_dims(image, 0) return image
def preprocess_image(self, inputs): """ Takes as input an image and prepares it for being passed through the network. """ return efn.preprocess_input(inputs)
def preprocess_image(image): image = tf.image.decode_jpeg(image, channels=3) image = tf.image.resize(image, [224, 224]) image = preprocess_input(image) return image
def preprocess_image(image): img = tf.image.resize(image, (331, 331)) img = preprocess_input(img) return img
def load_images(img_path): img = load_img(img_path, target_size=(224, 224)) img = img_to_array(img) img = preprocess_input(img) return img
def load_image(image_path): img = tf.io.read_file(image_path) img = tf.image.decode_png(img, channels=3) img = tf.image.resize(img, (299, 299)) img = efn.preprocess_input(img) return img, image_path
def load_image(image_path): img = tf.io.read_file(image_gcs_path+image_path) img = tf.image.decode_jpeg(img, channels=3) img = tf.image.resize(img, (331, 331)) img = preprocess_input(img) return img, image_path
from tensorflow.keras.models import Model, load_model from tensorflow.keras.preprocessing.image import ImageDataGenerator from efficientnet.tfkeras import preprocess_input from tensorflow.keras.preprocessing import image import numpy as np # This is the path from where the trained model will be loaded from trained_model_dir = 'models/trained/effnetb4_retrainedpt9.h5' # This is the directory containing the test data test_dir = 'data/test' # Load model model = load_model(trained_model_dir) img = image.load_img(imgPath, target_size=(380, 380)) img = image.img_to_array(img) img = preprocess_input(img) img = np.expand_dims(img, axis=0) classes = ['akiec', 'bcc', 'bkl', 'df', 'mel', 'nv', 'vasc'] resultset = model.predict(img) prediction_string = '' index = 0 for result in resultset[0]: prediction_string += '{} {}\n'.format(classes[index], result) index += 1 print(prediction_string)
def read_image(img_file): return preprocess_input(square_crop(cv2.cvtColor(cv2.imread(img_file), cv2.COLOR_BGR2RGB)))