xxxxxxxxxx#!python3 -m pip install tensorflow_hub --user --quiet --no-warn-script-locationpath='/home/sc_work/.sage/local/lib/python3.9/site-packages'import sys,warnings; sys.path.append(path); warnings.filterwarnings('ignore')from IPython.display import display,HTML#import tensorflow as tf,tensorflow_hub as hubimport os,h5py,urllib,numpy as np,pandas as pd,pylab as plfrom skimage.transform import resize as skresize#os.environ['TFHUB_MODEL_LOAD_FORMAT']='COMPRESSED'#from tensorflow.keras.applications import vgg19,VGG19#mvgg19=VGG19(include_top=True,weights='imagenet')file_path='https://raw.githubusercontent.com/OlgaBelitskaya/data_kitchen/main/'file_name='HorseBreeds160.h5'img_path='https://raw.githubusercontent.com/OlgaBelitskaya/data/main/horses/'img_files=['00_05_001.png','00_06_001.png']img_size=int(64); data_img_size=int(128); steps=int(30); max_img_size=int(224)from IPython.display import HTMLHTML('<style>.sagecell_input {text-shadow:4px 4px 4px #aaa;}</style>')xxxxxxxxxxdef get_data(file_path,file_name,img_size=data_img_size): input_file=urllib.request.urlopen(file_path+file_name) output_file=open(file_name,'wb') output_file.write(input_file.read()) output_file.close(); input_file.close() with h5py.File(file_name,'r') as f: keys=list(f.keys()) print('file keys: '+', '.join(keys)) images=np.array(f[keys[0]]) images_out=np.zeros((images.shape[0],img_size,img_size,3)) for i in range(len(images)): images_out[i,:,:,:]=skresize( images[i,:,:,:],(img_size,img_size,3),anti_aliasing=True) images=images_out labels=np.array(f[keys[1]]) names=[el.decode('utf-8') for el in f[keys[2]]] f.close() return images,labels,namesimages,labels,names=get_data(file_path,file_name)xxxxxxxxxxN=labels.shape[0]; n=int(.1*N); num_classes=len(names) shuffle_ids=np.arange(N)np.random.RandomState(12).shuffle(shuffle_ids)images=images[shuffle_ids]; labels=labels[shuffle_ids]x_test,x_valid,x_train=images[:n],images[n:2*n],images[2*n:]y_test,y_valid,y_train=labels[:n],labels[n:2*n],labels[2*n:]df=pd.DataFrame( [[x_train.shape,x_valid.shape,x_test.shape], [x_train.dtype,x_valid.dtype,x_test.dtype], [y_train.shape,y_valid.shape,y_test.shape], [y_train.dtype,y_valid.dtype,y_test.dtype]], columns=['train','valid','test'], index=['image shape','image type','label shape','label type'])def display_imgs(images,labels,names): fig=pl.figure(figsize=(8,3)); randi=randint(0,200) for i in range(randi,randi+8): ax=fig.add_subplot(2,4,i-randi+1,xticks=[],yticks=[]) ax.set_title(names[labels[i]],color='slategray', fontdict={'fontsize':'large'}) ax.imshow((images[i])) pl.tight_layout(); pl.show()display_imgs(images,labels,names); display(df)xxxxxxxxxxdef esrgantf2_superresolution(img,img_size=int(50)): model=hub.load('https://tfhub.dev/captain-pool/esrgan-tf2/1') func=model.signatures[tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY] func.inputs[int(0)].set_shape([int(1),img_size,img_size,int(3)]) converter=tf.lite.TFLiteConverter.from_concrete_functions([func]) converter.optimizations=[tf.lite.Optimize.DEFAULT] tflite_model=converter.convert() with tf.io.gfile.GFile('ESRGAN.tflite','wb') as f: f.write(tflite_model) esrgan_model_path='./ESRGAN.tflite' if img.mean()<float(1): img=img*float(255) lr=tf.image.resize(img,[img_size,img_size]) lr=tf.expand_dims(lr.numpy()[:,:,:int(3)],axis=int(0)) lr=tf.cast(lr,tf.float32) interpreter=tf.lite.Interpreter(model_path=esrgan_model_path) interpreter.allocate_tensors() input_details=interpreter.get_input_details() output_details=interpreter.get_output_details() interpreter.set_tensor(input_details[int(0)]['index'],lr) interpreter.invoke() output_data=interpreter.get_tensor(output_details[int(0)]['index']) sr=tf.squeeze(output_data,axis=int(0)) sr=tf.clip_by_value(sr,int(0),int(255)) sr=tf.round(sr); sr=tf.cast(sr,tf.uint8) lr=tf.cast(tf.squeeze(lr,axis=int(0)),tf.uint8) return lr,srimg=images[int(0)]lr,sr=esrgantf2_superresolution(img,img_size)xxxxxxxxxxdef low2superbicubic_imgs(img,lr,sr,img_size=img_size): pl.figure(figsize=(10,3)) pl.subplot(1,4,1); pl.title('HR'); pl.imshow(img) pl.subplot(1,4,2); pl.title('LR'); pl.imshow(lr.numpy()) pl.subplot(1,4,3); pl.title(f'ESRGAN x4'); pl.imshow(sr.numpy()) bicubic=tf.image.resize( lr,[img_size*int(4),img_size*int(4)],tf.image.ResizeMethod.BICUBIC) bicubic_contrast=tf.image.adjust_contrast(bicubic,float(.8)) bicubic_contrast=tf.cast(bicubic_contrast,tf.uint8) pl.subplot(1,4,4); pl.title('Bicubic & Contrast') pl.imshow(bicubic_contrast.numpy()) pl.tight_layout(); pl.show()low2superbicubic_imgs(img,lr,sr)xxxxxxxxxxdef hyper_interpolate(v1,v2,steps): v1norm=tf.norm(v1); v2norm=tf.norm(v2) vectors=[]; v2normalized=v2*(v1norm/v2norm) for step in range(steps): interpolated=v1+(v2normalized-v1)*step/(steps-int(1)) interpolated_norm=tf.norm(interpolated) interpolated_normalized=interpolated*(v1norm/interpolated_norm) vectors.append(interpolated_normalized) return tf.stack(vectors).numpy()imgs=tf.concat([hyper_interpolate(x_test[0],x_valid[0],steps), hyper_interpolate(x_valid[0],x_test[0],steps)],axis=int(0))animate([matrix_plot(imgs[i],figsize=(3,3),frame=False) for i in range(2*steps)])xxxxxxxxxxdef load_img(img_file,max_img_size=max_img_size): img=tf.io.read_file(img_file) img=tf.image.decode_image(img,channels=int(3)) img=tf.image.convert_image_dtype(img,tf.float32) shape=tf.cast(tf.shape(img)[:-int(1)],tf.float32) scale=max_img_size/max(shape) new_shape=tf.cast(shape*scale,tf.int32) img=tf.image.resize(img,new_shape) return img[tf.newaxis,:]def tensor2img(tensor): if np.ndim(tensor)>int(3): assert tensor.shape[int(0)]==int(1) tensor=tensor[int(0)] return matrix_plot(tensor,figsize=(3,3),frame=False)for f in img_files: input_file=urllib.request.urlopen(img_path+f) output_file=open(f,'wb') output_file.write(input_file.read()) output_file.close(); input_file.close()xxxxxxxxxxfor i in range(len(img_files)): content_img=load_img(img_files[i]) x=vgg19.preprocess_input(content_img*(int(255))) prediction_probabilities=mvgg19(x) predict_top5=vgg19.decode_predictions( prediction_probabilities.numpy())[int(0)] pretty_print('example #%d'%(i+1)) display(table( [[class_name,prob] for (number,class_name,prob) in predict_top5])) tensor2img(content_img).show()