triangles of gradient mesh
This commit is contained in:
Jun Zhou
GitHub
142
apps/test_triangle.py
Normal file
142
apps/test_triangle.py
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import pydiffvg
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import torch
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import skimage
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import numpy as np
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# Use GPU if available
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pydiffvg.set_use_gpu(torch.cuda.is_available())
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canvas_width, canvas_height = 256, 256
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num_control_points = torch.tensor([2, 2, 2])
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points = torch.tensor([[20.0, 30.0], # base
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[50.0, 60.0], # control point
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[ 90.0, 198.0], # control point
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[ 60.0, 218.0], # base
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[ 90.0, 180.0], # control point
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[200.0, 85.0], # control point
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[230.0, 90.0], # base
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[220.0, 70.0], # control point
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[130.0, 55.0]]) # control point
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path = pydiffvg.Path(num_control_points = num_control_points,
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points = points,
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is_closed = True)
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shapes = [path]
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path_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([0]),
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fill_color = torch.tensor([0.3, 0.6, 0.3, 1.0]))
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shape_groups = [path_group]
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scene_args = pydiffvg.RenderFunction.serialize_scene(\
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canvas_width, canvas_height, shapes, shape_groups)
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render = pydiffvg.RenderFunction.apply
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img = render(256, # width
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256, # height
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2, # num_samples_x
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2, # num_samples_y
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0, # seed
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None,
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*scene_args)
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# The output image is in linear RGB space. Do Gamma correction before saving the image.
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pydiffvg.imwrite(img.cpu(), 'results/test_curve/target.png', gamma=2.2)
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target = img.clone()
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# Load the obj file, get the vertices/control points
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obj = "imgs/Triangle.obj"
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vertices_tmp, faces_tmp = pydiffvg.obj_to_scene(obj)
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print(float(vertices_tmp[1][1]))
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print(faces_tmp)
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vertices = []
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faces = []
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for v in vertices_tmp:
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vertices.append([float(v[1]), float(v[2])])
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for f in faces_tmp:
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vs_count = len(f)
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tmp = []
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for v in f[1:]:
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tmp.append(int(v))
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faces.append(tmp)
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print(vertices)
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print(faces)
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# Ternary subdivision
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# Move the path to produce initial guess
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# normalize points for easier learning rate
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points_n = torch.tensor([[vertices[0][0]/256.0, vertices[0][1]/256.0], # base
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[70.0/256.0, 140.0/256.0], # control point
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[50.0/256.0, 100.0/256.0], # control point
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[vertices[1][0]/256.0, vertices[1][1]/256.0], # base
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[80.0/256.0, 40.0/256.0], # control point
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[120.0/256.0, 40.0/256.0], # control point
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[vertices[2][0]/256.0, vertices[2][1]/256.0], # base
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[150.0/256.0, 100.0/256.0], # control point
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[130.0/256.0, 140.0/256.0]], # control point
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requires_grad = True)
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color = torch.tensor([0.3, 0.2, 0.5, 1.0], requires_grad=True)
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path.points = points_n * 256
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path_group.fill_color = color
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scene_args = pydiffvg.RenderFunction.serialize_scene(\
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canvas_width, canvas_height, shapes, shape_groups)
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img = render(256, # width
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256, # height
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2, # num_samples_x
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2, # num_samples_y
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1, # seed
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None,
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*scene_args)
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pydiffvg.imwrite(img.cpu(), 'results/test_curve/init.png', gamma=2.2)
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# Optimize
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optimizer = torch.optim.Adam([points_n, color], lr=1e-2)
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# Run 100 Adam iterations.
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for t in range(100):
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print('iteration:', t)
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optimizer.zero_grad()
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# Forward pass: render the image.
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path.points = points_n * 256
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path_group.fill_color = color
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scene_args = pydiffvg.RenderFunction.serialize_scene(\
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canvas_width, canvas_height, shapes, shape_groups)
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img = render(256, # width
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256, # height
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2, # num_samples_x
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2, # num_samples_y
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t+1, # seed
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None,
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*scene_args)
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# Save the intermediate render.
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pydiffvg.imwrite(img.cpu(), 'results/test_curve/iter_{}.png'.format(t), gamma=2.2)
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# Compute the loss function. Here it is L2.
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loss = (img - target).pow(2).sum()
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print('loss:', loss.item())
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# Backpropagate the gradients.
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loss.backward()
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# Print the gradients
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print('points_n.grad:', points_n.grad)
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print('color.grad:', color.grad)
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# Take a gradient descent step.
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optimizer.step()
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# Print the current params.
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print('points:', path.points)
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print('color:', path_group.fill_color)
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# Render the final result.
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scene_args = pydiffvg.RenderFunction.serialize_scene(\
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canvas_width, canvas_height, shapes, shape_groups)
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img = render(256, # width
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256, # height
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2, # num_samples_x
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2, # num_samples_y
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102, # seed
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None,
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*scene_args)
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# Save the images and differences.
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pydiffvg.imwrite(img.cpu(), 'results/test_curve/final.png')
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# Convert the intermediate renderings to a video.
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from subprocess import call
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call(["ffmpeg", "-framerate", "24", "-i",
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"results/test_curve/iter_%d.png", "-vb", "20M",
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"results/test_curve/out.mp4"])
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144
apps/test_triangles.py
Normal file
144
apps/test_triangles.py
Normal file
@@ -0,0 +1,144 @@
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import pydiffvg
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import torch
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import skimage
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import numpy as np
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# Use GPU if available
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pydiffvg.set_use_gpu(torch.cuda.is_available())
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canvas_width, canvas_height = 256, 256
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num_control_points = torch.tensor([2, 2, 2])
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points = torch.tensor([[20.0, 30.0], # base
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[50.0, 60.0], # control point
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[ 90.0, 198.0], # control point
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[ 60.0, 218.0], # base
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[ 90.0, 180.0], # control point
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[200.0, 85.0], # control point
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[230.0, 90.0], # base
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[220.0, 70.0], # control point
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[130.0, 55.0]]) # control point
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path = pydiffvg.Path(num_control_points = num_control_points,
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points = points,
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is_closed = True)
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shapes = [path]
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path_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([0]),
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fill_color = torch.tensor([0.3, 0.6, 0.3, 1.0]))
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shape_groups = [path_group]
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scene_args = pydiffvg.RenderFunction.serialize_scene(\
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canvas_width, canvas_height, shapes, shape_groups)
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render = pydiffvg.RenderFunction.apply
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img = render(256, # width
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256, # height
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2, # num_samples_x
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2, # num_samples_y
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0, # seed
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None,
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*scene_args)
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# The output image is in linear RGB space. Do Gamma correction before saving the image.
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pydiffvg.imwrite(img.cpu(), 'results/test_triangles/target.png', gamma=2.2)
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target = img.clone()
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# Load the obj file, get the vertices/control points
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obj = "imgs/Triangles.obj"
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vertices_tmp, faces_tmp = pydiffvg.obj_to_scene(obj)
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print(float(vertices_tmp[1][1]))
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print(faces_tmp)
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vertices = []
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faces = []
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for v in vertices_tmp:
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vertices.append([float(v[1]), float(v[2])])
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for f in faces_tmp:
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vs_count = len(f)
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tmp = []
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for v in f[1:]:
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tmp.append(int(v))
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faces.append(tmp)
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print(vertices)
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print(faces)
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quit()
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# Ternary subdivision
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# Move the path to produce initial guess
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# normalize points for easier learning rate
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points_n = torch.tensor([[vertices[0][0]/256.0, vertices[0][1]/256.0], # base
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[70.0/256.0, 140.0/256.0], # control point
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[50.0/256.0, 100.0/256.0], # control point
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[vertices[1][0]/256.0, vertices[1][1]/256.0], # base
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[80.0/256.0, 40.0/256.0], # control point
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[120.0/256.0, 40.0/256.0], # control point
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[vertices[2][0]/256.0, vertices[2][1]/256.0], # base
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[150.0/256.0, 100.0/256.0], # control point
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[130.0/256.0, 140.0/256.0]], # control point
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requires_grad = True)
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color = torch.tensor([0.3, 0.2, 0.5, 1.0], requires_grad=True)
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path.points = points_n * 256
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path_group.fill_color = color
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scene_args = pydiffvg.RenderFunction.serialize_scene(\
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canvas_width, canvas_height, shapes, shape_groups)
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img = render(256, # width
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256, # height
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2, # num_samples_x
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2, # num_samples_y
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1, # seed
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None,
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*scene_args)
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pydiffvg.imwrite(img.cpu(), 'results/test_triangles/init.png', gamma=2.2)
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# Optimize
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optimizer = torch.optim.Adam([points_n, color], lr=1e-2)
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# Run 100 Adam iterations.
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for t in range(100):
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print('iteration:', t)
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optimizer.zero_grad()
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# Forward pass: render the image.
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path.points = points_n * 256
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path_group.fill_color = color
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scene_args = pydiffvg.RenderFunction.serialize_scene(\
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canvas_width, canvas_height, shapes, shape_groups)
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img = render(256, # width
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256, # height
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2, # num_samples_x
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2, # num_samples_y
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t+1, # seed
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None,
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*scene_args)
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# Save the intermediate render.
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pydiffvg.imwrite(img.cpu(), 'results/test_triangles/iter_{}.png'.format(t), gamma=2.2)
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# Compute the loss function. Here it is L2.
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loss = (img - target).pow(2).sum()
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print('loss:', loss.item())
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# Backpropagate the gradients.
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loss.backward()
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# Print the gradients
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print('points_n.grad:', points_n.grad)
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print('color.grad:', color.grad)
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# Take a gradient descent step.
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optimizer.step()
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# Print the current params.
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print('points:', path.points)
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print('color:', path_group.fill_color)
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# Render the final result.
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scene_args = pydiffvg.RenderFunction.serialize_scene(\
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canvas_width, canvas_height, shapes, shape_groups)
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img = render(256, # width
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256, # height
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2, # num_samples_x
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2, # num_samples_y
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102, # seed
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None,
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*scene_args)
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# Save the images and differences.
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pydiffvg.imwrite(img.cpu(), 'results/test_triangles/final.png')
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# Convert the intermediate renderings to a video.
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from subprocess import call
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call(["ffmpeg", "-framerate", "24", "-i",
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"results/test_triangles/iter_%d.png", "-vb", "20M",
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"results/test_triangles/out.mp4"])
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Reference in New Issue
Block a user