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import pydiffvg
import torch
import skimage
import numpy as np

# Use GPU if available
pydiffvg.set_use_gpu(torch.cuda.is_available())

canvas_width, canvas_height = 256, 256
num_control_points = torch.tensor([2])
# points = torch.tensor([[120.0,  30.0], # base
#                        [150.0,  60.0], # control point
#                        [ 90.0, 198.0], # control point
#                        [ 60.0, 218.0], # base
#                        [ 90.0, 180.0], # control point
#                        [200.0,  65.0], # control point
#                        [210.0,  98.0], # base
#                        [220.0,  70.0], # control point
#                        [130.0,  55.0]]) # control point
points = torch.tensor([[ 20.0, 128.0], # base
                       [ 50.0, 128.0], # control point
                       [170.0, 128.0], # control point
                       [200.0, 128.0]]) # base
path = pydiffvg.Path(num_control_points = num_control_points,
                     points = points,
                     is_closed = False,
                     stroke_width = torch.tensor(10.0))
shapes = [path]
path_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([0]),
                                 fill_color = None,
                                 stroke_color = torch.tensor([0.3, 0.6, 0.3, 1.0]))
shape_groups = [path_group]
scene_args = pydiffvg.RenderFunction.serialize_scene(\
    canvas_width, canvas_height, shapes, shape_groups,
    output_type = pydiffvg.OutputType.sdf)

render = pydiffvg.RenderFunction.apply
img = render(256, # width
             256, # height
             1,   # num_samples_x
             1,   # num_samples_y
             0,   # seed
             None, # background_image
             *scene_args)

path.points[:, 1] += 1e-3
scene_args = pydiffvg.RenderFunction.serialize_scene(\
    canvas_width, canvas_height, shapes, shape_groups,
    output_type = pydiffvg.OutputType.sdf)
img2 = render(256, # width
              256, # height
              1,   # num_samples_x
              1,   # num_samples_y
              0,   # seed
              None, # background_image
              *scene_args)

# diff = img2 - img
# diff = diff[:, :, 0] / 1e-3
# import matplotlib.pyplot as plt
# plt.imshow(diff)
# plt.show()

# # The output image is in linear RGB space. Do Gamma correction before saving the image.
# pydiffvg.imwrite(img.cpu(), 'results/single_curve_sdf/target.png', gamma=1.0)
# target = img.clone()

render_grad = pydiffvg.RenderFunction.render_grad
img = render_grad(torch.ones(256, 256, 1), # grad_img
                  256, # width
                  256, # height
                  1,   # num_samples_x
                  1,   # num_samples_y
                  0,   # seed
                  None, # background_image
                  *scene_args)
img = img[:, :, 0]
import matplotlib.pyplot as plt
plt.imshow(img)
plt.show()

# # Move the path to produce initial guess
# # normalize points for easier learning rate
# # points_n = torch.tensor([[100.0/256.0,  40.0/256.0], # base
# #                          [155.0/256.0,  65.0/256.0], # control point
# #                          [100.0/256.0, 180.0/256.0], # control point
# #                          [ 65.0/256.0, 238.0/256.0], # base
# #                          [100.0/256.0, 200.0/256.0], # control point
# #                          [170.0/256.0,  55.0/256.0], # control point
# #                          [220.0/256.0, 100.0/256.0], # base
# #                          [210.0/256.0,  80.0/256.0], # control point
# #                          [140.0/256.0,  60.0/256.0]], # control point
# #                         requires_grad = True) 
# points_n = torch.tensor([[118.4274/256.0,  32.0159/256.0],
#                          [174.9657/256.0,  28.1877/256.0],
#                          [ 87.6629/256.0, 175.1049/256.0],
#                          [ 57.8093/256.0, 232.8987/256.0],
#                          [ 80.1829/256.0, 165.4280/256.0],
#                          [197.3640/256.0,  83.4058/256.0],
#                          [209.3676/256.0,  97.9176/256.0],
#                          [219.1048/256.0,  72.0000/256.0],
#                          [143.1226/256.0,  57.0636/256.0]],
#                         requires_grad = True)
# color = torch.tensor([0.3, 0.2, 0.5, 1.0], requires_grad=True)
# path.points = points_n * 256
# path_group.fill_color = color
# scene_args = pydiffvg.RenderFunction.serialize_scene(\
#     canvas_width, canvas_height, shapes, shape_groups,
#     output_type = pydiffvg.OutputType.sdf)
# img = render(256, # width
#              256, # height
#              1,   # num_samples_x
#              1,   # num_samples_y
#              1,   # seed
#              None, # background_image
#              *scene_args)
# img /= 256.0
# pydiffvg.imwrite(img.cpu(), 'results/single_curve_sdf/init.png', gamma=1.0)

# # Optimize
# optimizer = torch.optim.Adam([points_n, color], lr=1e-3)
# # Run 100 Adam iterations.
# for t in range(2):
#     print('iteration:', t)
#     optimizer.zero_grad()
#     # Forward pass: render the image.
#     path.points = points_n * 256
#     path_group.fill_color = color
#     scene_args = pydiffvg.RenderFunction.serialize_scene(\
#         canvas_width, canvas_height, shapes, shape_groups,
#         output_type = pydiffvg.OutputType.sdf)
#     img = render(256,   # width
#                  256,   # height
#                  1,     # num_samples_x
#                  1,     # num_samples_y
#                  t+1,   # seed
#                  None, # background_image
#                  *scene_args)
#     img /= 256.0
#     # Save the intermediate render.
#     pydiffvg.imwrite(img.cpu(), 'results/single_curve_sdf/iter_{}.png'.format(t), gamma=1.0)
#     # Compute the loss function. Here it is L2.
#     loss = (img - target).pow(2).sum()
#     print('loss:', loss.item())

#     # Backpropagate the gradients.
#     loss.backward()
#     # Print the gradients
#     print('points_n.grad:', points_n.grad)
#     print('color.grad:', color.grad)

#     # Take a gradient descent step.
#     optimizer.step()
#     # Print the current params.
#     print('points:', path.points)
#     print('color:', path_group.fill_color)
# exit()

# # Render the final result.
# scene_args = pydiffvg.RenderFunction.serialize_scene(\
#     canvas_width, canvas_height, shapes, shape_groups,
#     output_type = pydiffvg.OutputType.sdf)
# img = render(256,   # width
#              256,   # height
#              1,     # num_samples_x
#              1,     # num_samples_y
#              102,    # seed
#              None, # background_image
#              *scene_args)
# img /= 256.0
# # Save the images and differences.
# pydiffvg.imwrite(img.cpu(), 'results/single_curve_sdf/final.png', gamma=1.0)

# # Convert the intermediate renderings to a video.
# from subprocess import call
# call(["ffmpeg", "-framerate", "24", "-i",
#     "results/single_curve_sdf/iter_%d.png", "-vb", "20M",
#     "results/single_curve_sdf/out.mp4"])