def simpple_image_data_generator(batch_size, imagepaths, classes):
class_indices = dict(zip(classes, range(len(classes))))
while True:
for start in np.arange(0, len(imagepaths), batch_size):
x_batch = []
y_batch = []
end = min(start + batch_size, len(imagepaths))
train_batch = imagepaths[start:end]
for imagepath in train_batch:
img = pil_image.imread(imagepath)
class_name = os.path.basename(os.path.dirname(imagepaths[i]))
x_batch.append(img)
y_batch.append(class_indices[class_name])
x_batch = np.array(x_batch, np.float32) / 255.
y_batch = np.array(y_batch, np.uint8)
yield x_batch, y_batch
import PIL import numpy as np img_path = 'data/images_train/data/123033/0e27621e3f8b484c993619abd064948be27f60a2.jpg' img = PIL.imread(img_path) img_array = np.asarray(img) print(img_array.shape)
def fetch_card_img_s3(name, bucket=os.environ.get('POKEDEXR_S3')):
s3 = boto3.resource('s3')
x = s3.Object(bucket, f'card_images/original/{name}').get()
src_img = PIL.imread(BytesIO(x['Body'].read()), 0)
return src_img
