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import torch
from torch import nn
import logging
from pathlib import Path
import gradio as gr
import numpy as np
import cv2
import model_utils
from models.SSN import SSN
config_file = 'configs/SSN.yaml'
weight = 'weights/SSN/0000001760.pt'
device = torch.device('cuda:0')
device = torch.device('cpu')
model = model_utils.load_model(config_file, weight, SSN, device)
DEFAULT_INTENSITY = 0.9
DEFAULT_GAMMA = 2.0
logging.info('Model loading succeed')
cur_rgba = None
cur_shadow = None
cur_intensity = DEFAULT_INTENSITY
cur_gamma = DEFAULT_GAMMA
def resize(img, size):
h, w = img.shape[:2]
if h > w:
newh = size
neww = int(w / h * size)
else:
neww = size
newh = int(h / w * size)
resized_img = cv2.resize(img, (neww, newh), interpolation=cv2.INTER_AREA)
if len(img.shape) != len(resized_img.shape):
resized_img = resized_img[..., none]
return resized_img
def ibl_normalize(ibl, energy=30.0):
total_energy = np.sum(ibl)
if total_energy < 1e-3:
# print('small energy: ', total_energy)
h,w = ibl.shape
return np.zeros((h,w))
return ibl * energy / total_energy
def padding_mask(rgba_input: np.array):
""" Padding the mask input so that it fits the training dataset view range
If the rgba does not have enough padding area, we need to pad the area
:param rgba_input: H x W x 4 inputs, the first 3 channels are RGB, the last channel is the alpha
:returns: H x W x 4 padded RGBAD
"""
padding = 50
padding_size = 256 - padding * 2
h, w = rgba_input.shape[:2]
rgb = rgba_input[:, :, :3]
alpha = rgba_input[:, :, -1:]
zeros = np.where(alpha==0)
hh, ww = zeros[0], zeros[1]
h_min, h_max = hh.min(), hh.max()
w_min, w_max = ww.min(), ww.max()
# if the area already has enough padding
if h_max - h_min < padding_size and w_max - w_min < padding_size:
return rgba_input
padding_output = np.zeros((256, 256, 4))
padding_output[..., :3] = 1.0
padded_rgba = resize(rgba_input, padding_size)
new_h, new_w = padded_rgba.shape[:2]
padding_output[padding:padding+new_h, padding:padding+new_w, :] = padded_rgba
return padding_output
def shadow_composite(rgba, shadow, intensity, gamma):
rgb = rgba[..., :3]
mask = rgba[..., 3:]
if len(shadow.shape) == 2:
shadow = shadow[..., None]
new_shadow = 1.0 - shadow ** gamma * intensity
ret = rgb * mask + (1.0 - mask) * new_shadow
return ret, new_shadow[..., 0]
def render_btn_fn(mask, ibl):
global cur_rgba, cur_shadow, cur_gamma, cur_intensity
print("Button clicked!")
mask = mask / 255.0
ibl = ibl/ 255.0
# smoothing ibl
ibl = cv2.GaussianBlur(ibl, (11, 11), 0)
# padding mask
mask = padding_mask(mask)
cur_rgba = np.copy(mask)
print('mask shape: {}/{}/{}/{}, ibl shape: {}/{}/{}/{}'.format(mask.shape, mask.dtype, mask.min(), mask.max(),
ibl.shape, ibl.dtype, ibl.min(), ibl.max()))
# ret = np.random.randn(256, 256, 3)
# ret = (ret - ret.min()) / (ret.max() - ret.min() + 1e-8)
rgb, mask = mask[..., :3], mask[..., 3]
ibl = ibl_normalize(cv2.resize(ibl, (32, 16)))
# ibl = 1.0 - ibl
x = {
'mask': mask,
'ibl': ibl
}
shadow = model.inference(x)
cur_shadow = np.copy(shadow)
ret, shadow = shadow_composite(cur_rgba, shadow, cur_intensity, cur_gamma)
# print('IBL range: {}/{} Shadow range: {} {}'.format(ibl.min(), ibl.max(), shadow.min(), shadow.max()))
return ret, shadow
def intensity_change(x):
global cur_rgba, cur_shadow, cur_gamma, cur_intensity
cur_intensity = x
ret, shadow = shadow_composite(cur_rgba, cur_shadow, cur_intensity, cur_gamma)
return ret, shadow
def gamma_change(x):
global cur_rgba, cur_shadow, cur_gamma, cur_intensity
cur_gamma = x
ret, shadow = shadow_composite(cur_rgba, cur_shadow, cur_intensity, cur_gamma)
return ret, shadow
ibl_h = 128
ibl_w = ibl_h * 2
with gr.Blocks() as demo:
with gr.Row():
mask_input = gr.Image(shape=(256, 256), image_mode="RGBA", label="Mask")
ibl_input = gr.Sketchpad(shape=(ibl_w, ibl_h), image_mode="L", label="IBL", tool='sketch', invert_colors=True)
output = gr.Image(shape=(256, 256), height=256, width=256, image_mode="RGB", label="Output")
shadow_output = gr.Image(shape=(256, 256), height=256, width=256, image_mode="L", label="Shadow Layer")
with gr.Row():
intensity_slider = gr.Slider(0.0, 1.0, value=DEFAULT_INTENSITY, step=0.1, label="Intensity", info="Choose between 0.0 and 1.0")
gamma_slider = gr.Slider(1.0, 4.0, value=DEFAULT_GAMMA, step=0.1, label="Gamma", info="Gamma correction for shadow")
render_btn = gr.Button(label="Render")
render_btn.click(render_btn_fn, inputs=[mask_input, ibl_input], outputs=[output, shadow_output])
intensity_slider.release(intensity_change, inputs=[intensity_slider], outputs=[output, shadow_output])
gamma_slider.release(gamma_change, inputs=[gamma_slider], outputs=[output, shadow_output])
logging.info('Finished')
demo.launch()
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