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demo.py
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demo.py
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# Copyright 2023 Adobe. All rights reserved.
# This file is licensed to you under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License. You may obtain a copy
# of the License at http:https://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software distributed under
# the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR REPRESENTATIONS
# OF ANY KIND, either express or implied. See the License for the specific language
# governing permissions and limitations under the License.
import pygame
import pygame_gui
from pygame_gui.windows.ui_file_dialog import UIFileDialog
from pygame_gui.elements.ui_button import UIButton
from pygame.rect import Rect
import matplotlib.pyplot as plt
import torch
import numpy as np
import matplotlib.pyplot
import torchvision.transforms as T
import torchvision.transforms.functional as F
import glob
import os
import PIL
from PIL import Image
import cv2
from model import Model_Composite_PL, Model_Composite
transforms_mask = T.Compose([T.Grayscale(), T.ToTensor()])
transform = T.Compose([T.ToTensor()])
resize = T.Resize((512, 512))
def get_concat_h(im1, im2):
dst = Image.new("RGB", (im1.width + im2.width, im1.height))
dst.paste(im1, (0, 0))
dst.paste(im2, (im1.width, 0))
return dst
def pilImageToSurface(pilImage):
return pygame.image.fromstring(
pilImage.tobytes(), pilImage.size, pilImage.mode
).convert()
window_dim = (1680, 1260)
pygame.init()
white = (255, 255, 255)
green = (0, 255, 0)
blue = (0, 0, 128)
window_surface = pygame.display.set_mode(window_dim)
background = pygame.Surface(window_dim)
# background.fill(pygame.Color('#000000'))
manager = pygame_gui.UIManager(window_dim)
clock = pygame.time.Clock()
file_selection_button = UIButton(
relative_rect=Rect(0, 0, 200, 100), manager=manager, text="Select Background"
)
foreground_selection_button = UIButton(
relative_rect=Rect(400, 0, 200, 100), manager=manager, text="Select Foreground"
)
model_selection_button = UIButton(
relative_rect=Rect(800, 0, 200, 100), manager=manager, text="Select Model"
)
run_button = UIButton(
relative_rect=Rect(1200, 0, 200, 100), manager=manager, text="Harmonize the image!"
)
font = pygame.font.Font("freesansbold.ttf", 16)
text = font.render("No background selected", True, green, blue)
textRect = text.get_rect()
textRect.center = (200 // 2, 300 // 2)
text_fore = font.render("No foreground selected", True, green, blue)
textRect_fore = text_fore.get_rect()
textRect_fore.center = (1000 // 2, 300 // 2)
text_model = font.render("No Model selected", True, green, blue)
textRect_model = text_model.get_rect()
textRect_model.center = (1800 // 2, 300 // 2)
image_selected = False
fore_selected = False
model_selected = False
flag_file_open = False
flag_fore_open = False
flag_model_open = False
output_exist = False
#### Zongze
flag_dialog = False
save_ind = 0
scale_sm = 0.55
while 1:
time_delta = clock.tick(60) / 1000.0
pygame.key.set_repeat(0, 0)
for event in pygame.event.get():
if event.type == pygame.QUIT:
quit()
if event.type == pygame.USEREVENT:
if event.user_type == pygame_gui.UI_BUTTON_PRESSED:
if event.ui_element == file_selection_button:
file_selection = UIFileDialog(
rect=Rect(0, 0, 300, 300),
manager=manager,
allow_picking_directories=True,
)
flag_file_open = True
if event.ui_element == foreground_selection_button:
fore_selection = UIFileDialog(
rect=Rect(0, 0, 300, 300),
manager=manager,
allow_picking_directories=True,
)
flag_fore_open = True
if event.ui_element == model_selection_button:
model_selection = UIFileDialog(
rect=Rect(0, 0, 300, 300),
manager=manager,
allow_picking_directories=True,
)
flag_model_open = True
if flag_file_open:
if event.ui_element == file_selection.ok_button:
flag_dialog = True
# print(file_selection.current_file_path)
carImg = pygame.image.load(file_selection.current_file_path)
size_carimg = carImg.get_size()
carImg = pygame.transform.scale(carImg, (512, 512))
# img = cv2.imread(
# str(file_selection.current_file_path), cv2.IMREAD_UNCHANGED
# )
# # extract alpha channel
# alpha = img[:, :, 3]
# # threshold alpha channel
# alpha = cv2.threshold(alpha, 0, 255, cv2.THRESH_BINARY)[1]
# cv2.imwrite("object_alpha.png", alpha)
# carImg = pygame.image.load("object_alpha.png")
text = font.render(
str(file_selection.current_file_path).split("/")[-1],
True,
green,
blue,
)
image_selected = True
flag_file_open = False
if flag_fore_open:
if event.ui_element == fore_selection.ok_button:
flag_dialog = True
# print(fore_selection.current_file_path)
# ForeIMG = pygame.image.load(fore_selection.current_file_path)
# ForeIMG = pygame.transform.scale(ForeIMG, (512, 512))
img = cv2.imread(
str(fore_selection.current_file_path), cv2.IMREAD_UNCHANGED
)
img = cv2.resize(img, (512, 512))
# # extract alpha channel
alpha = img[:, :, 3]
# # threshold alpha channel
# alpha = cv2.threshold(alpha, 0, 255, cv2.THRESH_BINARY)[1]
cv2.imwrite("image_mask.png", alpha)
# carImg = pygame.image.load("object_alpha.png")
text_fore = font.render(
str(fore_selection.current_file_path).split("/")[-1],
True,
green,
blue,
)
if image_selected:
img_bg = Image.open(str(file_selection.current_file_path))
img_bg = img_bg.resize((512, 512))
img_mask = Image.open("image_mask.png")
img_foreground = Image.open(
str(fore_selection.current_file_path)
)
img_foreground = img_foreground.resize((512, 512))
img_composite = Image.composite(
img_foreground, img_bg, img_mask
)
img_composite.save("image_composite.png")
carImg = pilImageToSurface(img_composite)
fore_selected = True
flag_fore_open = False
if flag_model_open:
if event.ui_element == model_selection.ok_button:
# print(model_selection.current_file_path)
flag_dialog = True
text_model = font.render(
str(model_selection.current_file_path).split("/")[-1],
True,
green,
blue,
)
model_selected = True
flag_model_open = False
if event.ui_element == run_button:
if model_selected and image_selected:
model_path = str(model_selection.current_file_path)
image_path = str(file_selection.current_file_path)
word = image_path.split("_")[-1]
Model = Model_Composite_PL(
dim=32,
masking=True,
brush=True,
maskoffset=0.6,
swap=True,
Vit_bool=False,
onlyupsample=True,
aggupsample=True,
)
print("num of parameters:",sum(p.numel() for p in Model.parameters()))
device = "cpu"
# print("Loading Model")
checkpoint = torch.load(model_path, map_location=device)
Model.load_state_dict(checkpoint["state_dict"])
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
Model.to(device)
Model.eval()
image_bg = resize(Image.open(image_path))
image_composite = resize(Image.open("image_composite.png"))
image_mask = resize(Image.open("image_mask.png"))
# Load image
scale = 1
translate = [0, 0]
torch_bg = transform(image_bg).to(device)
torch_composite = transform(image_composite).to(device)
torch_mask = transforms_mask(image_mask).to(device)
torch_composite = F.affine(
torch_composite,
angle=0,
translate=[0, 0],
scale=scale,
shear=0,
)
torch_mask = F.affine(
torch_mask, angle=0, translate=[0, 0], scale=scale, shear=0
)
torch_composite = F.affine(
torch_composite,
angle=0,
translate=translate,
scale=1,
shear=0,
)
torch_mask = F.affine(
torch_mask, angle=0, translate=translate, scale=1, shear=0
)
torch_composite = (
torch_composite * torch_mask + (1 - torch_mask) * torch_bg
)
with torch.no_grad():
inter_composite, output_composite, par1, par2 = Model(
torch_bg[None, ...],
torch_composite[None, ...],
torch_mask[None, ...],
)
output_lr = T.ToPILImage()(output_composite[0, ...])
inter_lr = T.ToPILImage()(inter_composite[0, ...])
composite_lr = T.ToPILImage()(torch_composite)
image_gainmap = T.ToPILImage()(
(
Model.output_final[0, ...].cpu()
* torch_mask[0, ...].cpu()
).repeat(3, 1, 1)
).resize((256, 256))
# print(Model.output_final[0, ...].cpu().shape)
# print(output_lr)
curves = par2.cpu().detach().numpy()
red_curve = curves[0, 0, 0, 0, :]
green_curve = curves[0, 1, 0, :, 0]
blue_curve = curves[0, 2, :, 0, 0]
plt.figure()
plt.plot(np.linspace(0, 1, 32), red_curve, "r")
plt.plot(np.linspace(0, 1, 32), green_curve, "g")
plt.plot(np.linspace(0, 1, 32), blue_curve, "b")
plt.ylim(0, 1)
plt.legend(["Reg", "Green", "Blue"])
plt.title("Learned Color Curves")
plt.savefig("tmp.jpg")
plt.close()
curveplot = pygame.image.load("tmp.jpg")
curveplot = pygame.transform.scale(curveplot, (256, 256))
### Cubic fitting
global idxx
idxx = np.linspace(0, 1, 32)
p_red = np.polyfit(idxx,red_curve,3)
p_red = p_red[3] + p_red[2]*idxx + p_red[1]*idxx*idxx + p_red[0]*idxx*idxx*idxx
p_blue = np.polyfit(idxx,blue_curve,3)
p_blue = p_blue[3] + p_blue[2]*idxx + p_blue[1]*idxx*idxx + p_blue[0]*idxx*idxx*idxx
p_green = np.polyfit(idxx,green_curve,3)
p_green = p_green[3] + p_green[2]*idxx + p_green[1]*idxx*idxx + p_green[0]*idxx*idxx*idxx
# print(p_red)
red_curve_plot = [(100 + ((i/32)*260), 980 - (p_red[i]*260)) for i in range(32)]
green_curve_plot = [(100 + (i/32)*260, 980 - p_green[i]*260) for i in range(32)]
blue_curve_plot = [(100 + (i/32)*260, 980 - p_blue[i]*260) for i in range(32)]
# pygame.draw.lines(window_surface, (255,0,0), False, [(100,980),(360,720)])
red_control = [red_curve_plot[0],red_curve_plot[10],red_curve_plot[20],red_curve_plot[31]]
green_control = [green_curve_plot[0],green_curve_plot[10],green_curve_plot[20],green_curve_plot[31]]
blue_control = [blue_curve_plot[0],blue_curve_plot[10],blue_curve_plot[20],blue_curve_plot[31]]
# print(red_control)
# print(red_curve_plot)
if not output_exist:
file_selection_red = UIButton(
relative_rect=Rect(400, 700, 100, 50), manager=manager, text="Red"
)
file_selection_green = UIButton(
relative_rect=Rect(400, 820, 100, 50), manager=manager, text="Green"
)
file_selection_blue = UIButton(
relative_rect=Rect(400, 940, 100, 50), manager=manager, text="Blue"
)
output_exist = True
red_check = False
green_check = False
blue_check = False
flag_dialog = False
save_ind = 0
if output_exist:
if event.ui_element == file_selection_red:
red_check = True
green_check = False
blue_check = False
# print(red_check)
if event.ui_element == file_selection_green:
red_check = False
green_check = True
blue_check = False
if event.ui_element == file_selection_blue:
red_check = False
green_check = False
blue_check = True
if event.type == pygame.MOUSEBUTTONDOWN:
drag = False
if output_exist:
if red_check:
xMouse = event.pos[0]
yMouse = event.pos[1]
# print(xMouse,yMouse)
for ix, points_red in enumerate(red_control):
if ((xMouse-points_red[0])**2 + (yMouse-points_red[1])**2) < 50:
drag = True
ipx = ix
break
if green_check:
xMouse = event.pos[0]
yMouse = event.pos[1]
# print(xMouse,yMouse)
for ix, points_green in enumerate(green_control):
if ((xMouse-points_green[0])**2 + (yMouse-points_green[1])**2) < 50:
drag = True
ipx = ix
break
if blue_check:
xMouse = event.pos[0]
yMouse = event.pos[1]
# print(xMouse,yMouse)
for ix, points_blue in enumerate(blue_control):
if ((xMouse-points_blue[0])**2 + (yMouse-points_blue[1])**2) < 50:
drag = True
ipx = ix
break
if event.type == pygame.MOUSEBUTTONUP:
if output_exist:
if red_check and drag:
xMouse = event.pos[0]
yMouse = event.pos[1]
if ((xMouse-red_control[ipx][0])**2 + (yMouse-red_control[ipx][1])**2) >= 50:
# print(red_control[ipx])
red_control[ipx] = (xMouse,yMouse)
k0 = np.array(red_control)
p_red = np.polyfit((k0[:,0]-100)/260,(980-k0[:,1])/260,3)
# red_curve_plot = [(100 + ((i/32)*260), 980 - (p_red[i]*260)) for i in range(32)]
# print(p_red)
p_red = p_red[3] + p_red[2]*idxx + p_red[1]*idxx*idxx + p_red[0]*idxx*idxx*idxx
red_curve_plot = [(100 + ((i/32)*260), 980 - (p_red[i]*260)) for i in range(32)]
with torch.no_grad():
inter_composite, output_composite, par2,= Model.forward_input(
torch_bg[None, ...],
torch_composite[None, ...],
torch_mask[None, ...],
[p_red,p_green,p_blue],
scale_sm
)
output_lr = T.ToPILImage()(output_composite[0, ...])
inter_lr = T.ToPILImage()(inter_composite[0, ...])
composite_lr = T.ToPILImage()(torch_composite)
image_gainmap = T.ToPILImage()(
(
Model.output_final[0, ...].cpu() * torch_mask[0, ...].cpu()
).repeat(3, 1, 1)
).resize((256, 256))
curves = par2.cpu().detach().numpy()
red_curve = curves[0, 0, 0, 0, :]
green_curve = curves[0, 1, 0, :, 0]
blue_curve = curves[0, 2, :, 0, 0]
plt.figure()
plt.plot(np.linspace(0, 1, 32), red_curve, "r")
plt.plot(np.linspace(0, 1, 32), green_curve, "g")
plt.plot(np.linspace(0, 1, 32), blue_curve, "b")
plt.ylim(0, 1)
plt.legend(["Reg", "Green", "Blue"])
plt.title("Learned Color Curves")
plt.savefig("tmp.jpg")
plt.close()
curveplot = pygame.image.load("tmp.jpg")
curveplot = pygame.transform.scale(curveplot, (256, 256))
drag = False
if green_check and drag:
xMouse = event.pos[0]
yMouse = event.pos[1]
if ((xMouse-green_control[ipx][0])**2 + (yMouse-green_control[ipx][1])**2) >= 50:
# print(red_control[ipx])
green_control[ipx] = (xMouse,yMouse)
k0 = np.array(green_control)
p_green = np.polyfit((k0[:,0]-100)/260,(980-k0[:,1])/260,3)
# red_curve_plot = [(100 + ((i/32)*260), 980 - (p_red[i]*260)) for i in range(32)]
# print(p_red)
p_green = p_green[3] + p_green[2]*idxx + p_green[1]*idxx*idxx + p_green[0]*idxx*idxx*idxx
green_curve_plot = [(100 + ((i/32)*260), 980 - (p_green[i]*260)) for i in range(32)]
with torch.no_grad():
inter_composite, output_composite, par2,= Model.forward_input(
torch_bg[None, ...],
torch_composite[None, ...],
torch_mask[None, ...],
[p_red,p_green,p_blue],
scale_sm
)
output_lr = T.ToPILImage()(output_composite[0, ...])
inter_lr = T.ToPILImage()(inter_composite[0, ...])
composite_lr = T.ToPILImage()(torch_composite)
image_gainmap = T.ToPILImage()(
(
Model.output_final[0, ...].cpu() * torch_mask[0, ...].cpu()
).repeat(3, 1, 1)
).resize((256, 256))
curves = par2.cpu().detach().numpy()
red_curve = curves[0, 0, 0, 0, :]
green_curve = curves[0, 1, 0, :, 0]
blue_curve = curves[0, 2, :, 0, 0]
plt.figure()
plt.plot(np.linspace(0, 1, 32), red_curve, "r")
plt.plot(np.linspace(0, 1, 32), green_curve, "g")
plt.plot(np.linspace(0, 1, 32), blue_curve, "b")
plt.ylim(0, 1)
plt.legend(["Reg", "Green", "Blue"])
plt.title("Learned Color Curves")
plt.savefig("tmp.jpg")
plt.close()
curveplot = pygame.image.load("tmp.jpg")
curveplot = pygame.transform.scale(curveplot, (256, 256))
drag = False
if blue_check and drag:
xMouse = event.pos[0]
yMouse = event.pos[1]
if ((xMouse-blue_control[ipx][0])**2 + (yMouse-blue_control[ipx][1])**2) >= 50:
# print(red_control[ipx])
blue_control[ipx] = (xMouse,yMouse)
k0 = np.array(blue_control)
p_blue = np.polyfit((k0[:,0]-100)/260,(980-k0[:,1])/260,3)
# red_curve_plot = [(100 + ((i/32)*260), 980 - (p_red[i]*260)) for i in range(32)]
# print(p_red)
p_blue = p_blue[3] + p_blue[2]*idxx + p_blue[1]*idxx*idxx + p_blue[0]*idxx*idxx*idxx
blue_curve_plot = [(100 + ((i/32)*260), 980 - (p_blue[i]*260)) for i in range(32)]
with torch.no_grad():
inter_composite, output_composite, par2,= Model.forward_input(
torch_bg[None, ...],
torch_composite[None, ...],
torch_mask[None, ...],
[p_red,p_green,p_blue],
scale_sm
)
output_lr = T.ToPILImage()(output_composite[0, ...])
inter_lr = T.ToPILImage()(inter_composite[0, ...])
composite_lr = T.ToPILImage()(torch_composite)
image_gainmap = T.ToPILImage()(
(
Model.output_final[0, ...].cpu() * torch_mask[0, ...].cpu()
).repeat(3, 1, 1)
).resize((256, 256))
curves = par2.cpu().detach().numpy()
red_curve = curves[0, 0, 0, 0, :]
green_curve = curves[0, 1, 0, :, 0]
blue_curve = curves[0, 2, :, 0, 0]
plt.figure()
plt.plot(np.linspace(0, 1, 32), red_curve, "r")
plt.plot(np.linspace(0, 1, 32), green_curve, "g")
plt.plot(np.linspace(0, 1, 32), blue_curve, "b")
plt.ylim(0, 1)
plt.legend(["Reg", "Green", "Blue"])
plt.title("Learned Color Curves")
plt.savefig("tmp.jpg")
plt.close()
curveplot = pygame.image.load("tmp.jpg")
curveplot = pygame.transform.scale(curveplot, (256, 256))
drag = False
if event.type == pygame.KEYDOWN:
if output_exist:
if event.key == pygame.K_a:
scale_sm -= 0.03
with torch.no_grad():
inter_composite, output_composite, par2,= Model.forward_input(
torch_bg[None, ...],
torch_composite[None, ...],
torch_mask[None, ...],
[p_red,p_green,p_blue],
scale_sm
)
output_lr = T.ToPILImage()(output_composite[0, ...])
inter_lr = T.ToPILImage()(inter_composite[0, ...])
composite_lr = T.ToPILImage()(torch_composite)
image_gainmap = T.ToPILImage()(
(
((Model.output_final[0, ...].cpu()-0.6)*(1-scale_sm)/(0.4)+scale_sm) * torch_mask[0, ...].cpu()
).repeat(3, 1, 1)
).resize((256, 256))
if event.key == pygame.K_s:
save_ind += 1
bg_name = str(file_selection.current_file_path).split("/")[-1].split('.')[0]
fg_name = str(fore_selection.current_file_path).split("/")[-1].split('.')[0]
print("size:",size_carimg)
compite_save = T.ToPILImage()(torch_composite).resize((512,int(512*size_carimg[1]/size_carimg[0])))
# compite_save
compite_save.save("results_moving_position/%s_%s_%d_composite.jpg"%(bg_name,fg_name,save_ind))
mask_save = T.ToPILImage()(torch_mask).resize((512,int(512*size_carimg[1]/size_carimg[0])))
# mask_save.resize((512,int(512*size_carimg[1]/size_carimg[0])))
mask_save.save("results_moving_position/%s_%s_%d_mask.jpg"%(bg_name,fg_name,save_ind))
bg_save = T.ToPILImage()(torch_bg).resize((512,int(512*size_carimg[1]/size_carimg[0])))
# bg_save.resize((512,int(512*size_carimg[1]/size_carimg[0])))
bg_save.save("results_moving_position/%s_%s_%d_bg.jpg"%(bg_name,fg_name,save_ind))
output_save = T.ToPILImage()(output_composite[0,...]).resize((512,int(512*size_carimg[1]/size_carimg[0])))
# output_save.resize((512,int(512*size_carimg[1]/size_carimg[0])))
output_save.save("results_moving_position/%s_%s_%d_results.jpg"%(bg_name,fg_name,save_ind))
plt.figure()
plt.plot(np.linspace(0, 1, 32), red_curve, "r")
plt.plot(np.linspace(0, 1, 32), green_curve, "g")
plt.plot(np.linspace(0, 1, 32), blue_curve, "b")
plt.ylim(0, 1)
plt.legend(["Reg", "Green", "Blue"])
plt.title("Learned Color Curves")
plt.savefig("results_moving_position/%s_%s_%d_colorcurves.jpg"%(bg_name,fg_name,save_ind))
plt.close()
image_gainmap = T.ToPILImage()(
(
((Model.output_final[0, ...].cpu()-0.6)*(1-scale_sm)/(0.4)+scale_sm) * torch_mask[0, ...].cpu()
).repeat(3, 1, 1)
).resize((512, 512)).resize((512,int(512*size_carimg[1]/size_carimg[0])))
image_gainmap.save("results_moving_position/%s_%s_%d_gainmap.jpg"%(bg_name,fg_name,save_ind))
image_gainmap = image_gainmap.resize((256,256))
if event.key == pygame.K_d:
scale_sm += 0.03
with torch.no_grad():
inter_composite, output_composite, par2,= Model.forward_input(
torch_bg[None, ...],
torch_composite[None, ...],
torch_mask[None, ...],
[p_red,p_green,p_blue],
scale_sm
)
output_lr = T.ToPILImage()(output_composite[0, ...])
inter_lr = T.ToPILImage()(inter_composite[0, ...])
composite_lr = T.ToPILImage()(torch_composite)
image_gainmap = T.ToPILImage()(
(
((Model.output_final[0, ...].cpu()-0.6)*(1-scale_sm)/(0.4)+scale_sm) * torch_mask[0, ...].cpu()
).repeat(3, 1, 1)
).resize((256, 256))
if event.key == pygame.K_LEFT:
translate[0] -= 25
torch_bg = transform(image_bg)
torch_composite = transform(image_composite)
torch_mask = transforms_mask(image_mask)
torch_composite = F.affine(
torch_composite, angle=0, translate=[0, 0], scale=scale, shear=0
)
torch_mask = F.affine(
torch_mask, angle=0, translate=[0, 0], scale=scale, shear=0
)
torch_composite = F.affine(
torch_composite, angle=0, translate=translate, scale=1, shear=0
)
torch_mask = F.affine(
torch_mask, angle=0, translate=translate, scale=1, shear=0
)
torch_composite = (
torch_composite * torch_mask + (1 - torch_mask) * torch_bg
)
composite_lr = T.ToPILImage()(torch_composite)
with torch.no_grad():
inter_composite, output_composite, par1, par2 = Model(
torch_bg[None, ...],
torch_composite[None, ...],
torch_mask[None, ...],
)
output_lr = T.ToPILImage()(output_composite[0, ...])
inter_lr = T.ToPILImage()(inter_composite[0, ...])
composite_lr = T.ToPILImage()(torch_composite)
image_gainmap = T.ToPILImage()(
(
Model.output_final[0, ...].cpu() * torch_mask[0, ...].cpu()
).repeat(3, 1, 1)
).resize((256, 256))
curves = par2.cpu().detach().numpy()
red_curve = curves[0, 0, 0, 0, :]
green_curve = curves[0, 1, 0, :, 0]
blue_curve = curves[0, 2, :, 0, 0]
plt.figure()
plt.plot(np.linspace(0, 1, 32), red_curve, "r")
plt.plot(np.linspace(0, 1, 32), green_curve, "g")
plt.plot(np.linspace(0, 1, 32), blue_curve, "b")
plt.ylim(0, 1)
plt.legend(["Reg", "Green", "Blue"])
plt.title("Learned Color Curves")
plt.savefig("tmp.jpg")
plt.close()
curveplot = pygame.image.load("tmp.jpg")
curveplot = pygame.transform.scale(curveplot, (256, 256))
# print("what")
# print(pygame.key.get_repeat())
idxx = np.linspace(0, 1, 32)
p_red = np.polyfit(idxx,red_curve,3)
p_red = p_red[3] + p_red[2]*idxx + p_red[1]*idxx*idxx + p_red[0]*idxx*idxx*idxx
p_blue = np.polyfit(idxx,blue_curve,3)
p_blue = p_blue[3] + p_blue[2]*idxx + p_blue[1]*idxx*idxx + p_blue[0]*idxx*idxx*idxx
p_green = np.polyfit(idxx,green_curve,3)
p_green = p_green[3] + p_green[2]*idxx + p_green[1]*idxx*idxx + p_green[0]*idxx*idxx*idxx
# print(p_red)
red_curve_plot = [(100 + ((i/32)*260), 980 - (p_red[i]*260)) for i in range(32)]
# print(red_curve_plot)
green_curve_plot = [(100 + (i/32)*260, 980 - p_blue[i]*260) for i in range(32)]
blue_curve_plot = [(100 + (i/32)*260, 980 - p_green[i]*260) for i in range(32)]
red_control = [red_curve_plot[0],red_curve_plot[10],red_curve_plot[20],red_curve_plot[31]]
green_control = [green_curve_plot[0],green_curve_plot[10],green_curve_plot[20],green_curve_plot[31]]
blue_control = [blue_curve_plot[0],blue_curve_plot[10],blue_curve_plot[20],blue_curve_plot[31]]
break
if event.key == pygame.K_RIGHT:
translate[0] += 25
torch_bg = transform(image_bg).to(device)
torch_composite = transform(image_composite).to(device)
torch_mask = transforms_mask(image_mask).to(device)
torch_composite = F.affine(
torch_composite, angle=0, translate=[0, 0], scale=scale, shear=0
)
torch_mask = F.affine(
torch_mask, angle=0, translate=[0, 0], scale=scale, shear=0
)
torch_composite = F.affine(
torch_composite, angle=0, translate=translate, scale=1, shear=0
)
torch_mask = F.affine(
torch_mask, angle=0, translate=translate, scale=1, shear=0
)
torch_composite = (
torch_composite * torch_mask + (1 - torch_mask) * torch_bg
)
composite_lr = T.ToPILImage()(torch_composite)
with torch.no_grad():
inter_composite, output_composite, par1, par2 = Model(
torch_bg[None, ...],
torch_composite[None, ...],
torch_mask[None, ...],
)
print("b")
output_lr = T.ToPILImage()(output_composite[0, ...])
inter_lr = T.ToPILImage()(inter_composite[0, ...])
composite_lr = T.ToPILImage()(torch_composite)
image_gainmap = T.ToPILImage()(
(
Model.output_final[0, ...].cpu() * torch_mask[0, ...].cpu()
).repeat(3, 1, 1)
).resize((256, 256))
curves = par2.cpu().detach().numpy()
red_curve = curves[0, 0, 0, 0, :]
green_curve = curves[0, 1, 0, :, 0]
blue_curve = curves[0, 2, :, 0, 0]
plt.figure()
plt.plot(np.linspace(0, 1, 32), red_curve, "r")
plt.plot(np.linspace(0, 1, 32), green_curve, "g")
plt.plot(np.linspace(0, 1, 32), blue_curve, "b")
plt.ylim(0, 1)
plt.legend(["Reg", "Green", "Blue"])
plt.title("Learned Color Curves")
plt.savefig("tmp.jpg")
plt.close()
curveplot = pygame.image.load("tmp.jpg")
curveplot = pygame.transform.scale(curveplot, (256, 256))
print("ca")
idxx = np.linspace(0, 1, 32)
p_red = np.polyfit(idxx,red_curve,3)
p_red = p_red[3] + p_red[2]*idxx + p_red[1]*idxx*idxx + p_red[0]*idxx*idxx*idxx
p_blue = np.polyfit(idxx,blue_curve,3)
p_blue = p_blue[3] + p_blue[2]*idxx + p_blue[1]*idxx*idxx + p_blue[0]*idxx*idxx*idxx
p_green = np.polyfit(idxx,green_curve,3)
p_green = p_green[3] + p_green[2]*idxx + p_green[1]*idxx*idxx + p_green[0]*idxx*idxx*idxx
# print(p_red)
red_curve_plot = [(100 + ((i/32)*260), 980 - (p_red[i]*260)) for i in range(32)]
# print(red_curve_plot)
green_curve_plot = [(100 + (i/32)*260, 980 - p_blue[i]*260) for i in range(32)]
blue_curve_plot = [(100 + (i/32)*260, 980 - p_green[i]*260) for i in range(32)]
red_control = [red_curve_plot[0],red_curve_plot[10],red_curve_plot[20],red_curve_plot[31]]
green_control = [green_curve_plot[0],green_curve_plot[10],green_curve_plot[20],green_curve_plot[31]]
blue_control = [blue_curve_plot[0],blue_curve_plot[10],blue_curve_plot[20],blue_curve_plot[31]]
break
if event.key == pygame.K_UP:
translate[1] -= 25
torch_bg = transform(image_bg).to(device)
torch_composite = transform(image_composite).to(device)
torch_mask = transforms_mask(image_mask).to(device)
torch_composite = F.affine(
torch_composite, angle=0, translate=[0, 0], scale=scale, shear=0
)
torch_mask = F.affine(
torch_mask, angle=0, translate=[0, 0], scale=scale, shear=0
)
torch_composite = F.affine(
torch_composite, angle=0, translate=translate, scale=1, shear=0
)
torch_mask = F.affine(
torch_mask, angle=0, translate=translate, scale=1, shear=0
)
torch_composite = (
torch_composite * torch_mask + (1 - torch_mask) * torch_bg
)
composite_lr = T.ToPILImage()(torch_composite)
with torch.no_grad():
inter_composite, output_composite, par1, par2 = Model(
torch_bg[None, ...],
torch_composite[None, ...],
torch_mask[None, ...],
)
output_lr = T.ToPILImage()(output_composite[0, ...])
inter_lr = T.ToPILImage()(inter_composite[0, ...])
composite_lr = T.ToPILImage()(torch_composite)
image_gainmap = T.ToPILImage()(
(
Model.output_final[0, ...].cpu() * torch_mask[0, ...].cpu()
).repeat(3, 1, 1)
).resize((256, 256))
curves = par2.cpu().detach().numpy()
red_curve = curves[0, 0, 0, 0, :]
green_curve = curves[0, 1, 0, :, 0]
blue_curve = curves[0, 2, :, 0, 0]
plt.figure()
plt.plot(np.linspace(0, 1, 32), red_curve, "r")
plt.plot(np.linspace(0, 1, 32), green_curve, "g")
plt.plot(np.linspace(0, 1, 32), blue_curve, "b")
plt.ylim(0, 1)
plt.legend(["Reg", "Green", "Blue"])
plt.title("Learned Color Curves")
plt.savefig("tmp.jpg")
plt.close()
curveplot = pygame.image.load("tmp.jpg")
curveplot = pygame.transform.scale(curveplot, (256, 256))
idxx = np.linspace(0, 1, 32)
p_red = np.polyfit(idxx,red_curve,3)
p_red = p_red[3] + p_red[2]*idxx + p_red[1]*idxx*idxx + p_red[0]*idxx*idxx*idxx
p_blue = np.polyfit(idxx,blue_curve,3)
p_blue = p_blue[3] + p_blue[2]*idxx + p_blue[1]*idxx*idxx + p_blue[0]*idxx*idxx*idxx
p_green = np.polyfit(idxx,green_curve,3)
p_green = p_green[3] + p_green[2]*idxx + p_green[1]*idxx*idxx + p_green[0]*idxx*idxx*idxx
# print(p_red)
red_curve_plot = [(100 + ((i/32)*260), 980 - (p_red[i]*260)) for i in range(32)]
# print(red_curve_plot)
green_curve_plot = [(100 + (i/32)*260, 980 - p_blue[i]*260) for i in range(32)]
blue_curve_plot = [(100 + (i/32)*260, 980 - p_green[i]*260) for i in range(32)]
red_control = [red_curve_plot[0],red_curve_plot[10],red_curve_plot[20],red_curve_plot[31]]
green_control = [green_curve_plot[0],green_curve_plot[10],green_curve_plot[20],green_curve_plot[31]]
blue_control = [blue_curve_plot[0],blue_curve_plot[10],blue_curve_plot[20],blue_curve_plot[31]]
break
if event.key == pygame.K_DOWN:
translate[1] += 25
torch_bg = transform(image_bg).to(device)
torch_composite = transform(image_composite).to(device)
torch_mask = transforms_mask(image_mask).to(device)
torch_composite = F.affine(
torch_composite, angle=0, translate=[0, 0], scale=scale, shear=0
)
torch_mask = F.affine(
torch_mask, angle=0, translate=[0, 0], scale=scale, shear=0
)
torch_composite = F.affine(
torch_composite, angle=0, translate=translate, scale=1, shear=0
)
torch_mask = F.affine(
torch_mask, angle=0, translate=translate, scale=1, shear=0