File size: 2,232 Bytes
7f70f47 a2944d5 7f70f47 a2944d5 7f70f47 a2944d5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 |
---
license: cc
tags:
- tensorflow
- image-to-image
---
# Whitebox Cartoonizer
Whitebox Cartoonizer [1] model in the `SavedModel` format. The model was exported to the SavedModel format using
[this notebook](https://huggingface.co/sayakpaul/whitebox-cartoonizer/blob/main/export-saved-model.ipynb). Original model
repository can be found [here](https://github.com/SystemErrorWang/White-box-Cartoonization).
## Inference code
```py
import cv2
import numpy as np
import requests
import tensorflow as tf
from huggingface_hub import snapshot_download
from PIL import Image
def resize_crop(image):
h, w, c = np.shape(image)
if min(h, w) > 720:
if h > w:
h, w = int(720 * h / w), 720
else:
h, w = 720, int(720 * w / h)
image = cv2.resize(image, (w, h), interpolation=cv2.INTER_AREA)
h, w = (h // 8) * 8, (w // 8) * 8
image = image[:h, :w, :]
return image
def download_image(url):
image = Image.open(requests.get(url, stream=True).raw)
image = image.convert("RGB")
image = np.array(image)
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
return image
def preprocess_image(image):
image = resize_crop(image)
image = image.astype(np.float32) / 127.5 - 1
image = np.expand_dims(image, axis=0)
image = tf.constant(image)
return image
# Load the model and extract concrete function.
model_path = snapshot_download("sayakpaul/whitebox-cartoonizer")
loaded_model = tf.saved_model.load(model_path)
concrete_func = loaded_model.signatures["serving_default"]
# Download and preprocess image.
image_url = "https://huggingface.co/spaces/sayakpaul/cartoonizer-demo-onnx/resolve/main/mountain.jpeg"
image = download_image(image_url)
preprocessed_image = preprocess_image(image)
# Run inference.
result = concrete_func(preprocessed_image)["final_output:0"]
# Post-process the result and serialize it.
output = (result[0].numpy() + 1.0) * 127.5
output = np.clip(output, 0, 255).astype(np.uint8)
output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
output_image = Image.fromarray(output)
output_image.save("result.png")
```
## References
[1] Learning to Cartoonize Using White-box Cartoon Representations; Xinrui Wang and Jinze Yu; CVPR 2020. |