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soho-clip-embeddings-explorer

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move api helpers to api_helper.py

44fc764 about 1 year ago

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	from PIL import Image
	import numpy as np
	import base64
	import json
	from torchvision.transforms import Compose, Resize, CenterCrop

	# support sending images as base64

	def encode_numpy_array(image_np):
	# Flatten the numpy array and convert it to bytes
	image_bytes = image_np.tobytes()

	# Encode the byte data as base64
	encoded_image = base64.b64encode(image_bytes).decode()
	payload = {
	"encoded_image": encoded_image,
	"width": image_np.shape[1],
	"height": image_np.shape[0],
	"channels": image_np.shape[2],
	}
	payload_json = json.dumps(payload)
	return payload_json

	def decode_numpy_array(payload):
	payload_json = json.loads(payload)
	# payload_json = payload.json()
	encoded_image = payload_json["encoded_image"]
	width = payload_json["width"]
	height = payload_json["height"]
	channels = payload_json["channels"]
	# Decode the base64 data
	decoded_image = base64.b64decode(encoded_image)

	# Convert the byte data back to a NumPy array
	image_np = np.frombuffer(decoded_image, dtype=np.uint8).reshape(height, width, channels)

	return image_np


	def preprocess_image(image_np, max_size=224):
	# Convert the numpy array to a PIL image
	image = Image.fromarray(image_np)

	# Define the transformation pipeline
	transforms = Compose([
	Resize(max_size, interpolation=Image.BICUBIC),
	CenterCrop(max_size),
	])

	# Apply the transformations to the image
	image = transforms(image)

	# Convert the PIL image back to a numpy array
	image_np = np.array(image)

	return image_np