wehub-resource-sync
87 lines
2.5 KiB
Python
87 lines
2.5 KiB
Python
# Tensorflow Computer Vision Helper
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import tensorflow as tf
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from tensorflow import keras
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import numpy as np
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import matplotlib.pyplot as plt
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from PIL import Image
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import glob
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import os
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def plot_convolution(data,t,title=''):
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fig, ax = plt.subplots(2,len(data)+1,figsize=(8,3))
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fig.suptitle(title,fontsize=16)
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tt = np.expand_dims(np.expand_dims(t,2),2)
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for i,im in enumerate(data):
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ax[0][i].imshow(im)
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ximg = np.expand_dims(np.expand_dims(im,2),0)
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cim = tf.nn.conv2d(ximg,tt,1,'SAME')
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ax[1][i].imshow(cim[0][:,:,0])
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ax[0][i].axis('off')
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ax[1][i].axis('off')
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ax[0,-1].imshow(t)
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ax[0,-1].axis('off')
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ax[1,-1].axis('off')
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#plt.tight_layout()
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plt.show()
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def plot_results(hist):
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fig,ax = plt.subplots(1,2,figsize=(15,3))
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ax[0].set_title('Accuracy')
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ax[1].set_title('Loss')
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for x in ['acc','val_acc']:
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ax[0].plot(hist.history[x])
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for x in ['loss','val_loss']:
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ax[1].plot(hist.history[x])
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plt.show()
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def display_dataset(dataset, labels=None, n=10, classes=None):
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fig,ax = plt.subplots(1,n,figsize=(15,3))
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for i in range(n):
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ax[i].imshow(dataset[i])
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ax[i].axis('off')
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if classes is not None and labels is not None:
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ax[i].set_title(classes[labels[i][0]])
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def check_image(fn):
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try:
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im = Image.open(fn)
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im.verify()
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return im.format=='JPEG'
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except:
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return False
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def check_image_dir(path):
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for fn in glob.glob(path):
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if not check_image(fn):
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print("Corrupt image or wrong format: {}".format(fn))
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os.remove(fn)
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def load_cats_dogs_dataset(batch_size=64):
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if not os.path.exists('data/PetImages'):
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print("Extracting the dataset")
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with zipfile.ZipFile('data/kagglecatsanddogs_3367a.zip', 'r') as zip_ref:
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zip_ref.extractall('data')
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print("Checking dataset")
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check_image_dir('data/PetImages/Cat/*.jpg')
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check_image_dir('data/PetImages/Dog/*.jpg')
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data_dir = 'data/PetImages'
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print("Loading dataset")
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ds_train = keras.preprocessing.image_dataset_from_directory(
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data_dir,
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validation_split = 0.2,
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subset = 'training',
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seed = 13,
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image_size = (224,224),
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batch_size = batch_size
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)
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ds_test = keras.preprocessing.image_dataset_from_directory(
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data_dir,
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validation_split = 0.2,
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subset = 'validation',
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seed = 13,
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image_size = (224,224),
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batch_size = batch_size
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)
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return ds_train,ds_test
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