initial commit

apps/svg_parse_test.py

@@ -0,0 +1,63 @@
+import pydiffvg
+import sys
+import numpy as np
+import torch
+sys.path.append("../pydiffvg")
+
+from optimize_svg import OptimizableSvg
+
+pydiffvg.set_use_gpu(False)
+
+"""
+for x in range(100000):
+    inmat=np.eye(3)
+    inmat[0:2,:]=(np.random.rand(2,3)-0.5)*2
+    decomp=OptimizableSvg.TransformTools.decompose(inmat)
+    outmat=OptimizableSvg.TransformTools.recompose(torch.tensor(decomp[0],dtype=torch.float32),torch.tensor(decomp[1],dtype=torch.float32),torch.tensor(decomp[2],dtype=torch.float32),torch.tensor(decomp[3],dtype=torch.float32)).numpy()
+    dif=np.linalg.norm(inmat-outmat)
+    if dif > 1e-3:
+        print(dif)
+        print(inmat)
+        print(outmat)
+        print(decomp)"""
+
+#infile='../../data/test_data/linear_grad_alpha_aspaths.svg'
+#infile='../../data/note_small.svg'
+infile='linux.svg'
+
+canvas_width, canvas_height, shapes, shape_groups = \
+	pydiffvg.svg_to_scene(infile)
+scene_args = pydiffvg.RenderFunction.serialize_scene(\
+	canvas_width, canvas_height, shapes, shape_groups)
+render = pydiffvg.RenderFunction.apply
+img = render(canvas_width, # width
+             canvas_height, # height
+             2,   # num_samples_x
+             2,   # num_samples_y
+             0,   # seed
+             *scene_args)
+# The output image is in linear RGB space. Do Gamma correction before saving the image.
+pydiffvg.imwrite(img.cpu(), 'test_old.png', gamma=1.0)
+
+#optim=OptimizableSvg('linux.svg',verbose=True)
+optim=OptimizableSvg(infile,verbose=True)
+
+scene=optim.build_scene()
+scene_args = pydiffvg.RenderFunction.serialize_scene(*scene)
+render = pydiffvg.RenderFunction.apply
+img = render(scene[0], # width
+             scene[1], # height
+             2,   # num_samples_x
+             2,   # num_samples_y
+             0,   # seed
+             *scene_args)
+
+
+
+with open("resaved.svg","w") as f:
+    f.write(optim.write_xml())
+
+# The output image is in linear RGB space. Do Gamma correction before saving the image.
+pydiffvg.imwrite(img.cpu(), 'test_new.png', gamma=1.0)
+
+print("Done!")