102 lines
4.1 KiB
Python
102 lines
4.1 KiB
Python
# check if upscaler is better then filling
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import os
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from models.upscaler import UpscalerBasic as Upscaler
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os.environ["CUDA_VISIBLE_DEVICES"] = "3"
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import tqdm
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import numpy as np
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import plotly.graph_objects as go
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from custom_utils.custom_utils import UV2Mesh, _maskFaces, load_model
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# from custom_utils.custom_utils import UV2Mesh, maskFacesBatch, _maskFaces
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if __name__ == '__main__':
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folder = '/home/workspace/local-data/train_100'
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mask = np.load('custom_utils/mask_256x512.npy')
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F = np.load("custom_utils/smpl_faces.npy")
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uv2mesh = UV2Mesh(res=256)
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res = 32
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res_modifier = '_' + str(res)
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upscale_enhancer = Upscaler(512, res)
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load_model(upscale_enhancer, 'checkpoints/up_2_3')
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frames = os.listdir(os.path.join(folder, 'pose_shape'))
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visited = set()
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meshes = []
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count = 0
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for frame in tqdm.tqdm(frames):
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id, frame_nr = frame.split('_')
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frame_nr = frame_nr.split('.')[0]
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if id in visited:
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continue
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visited.add(id)
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print(id)
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uv_unposed_no_shape_low = np.load(os.path.join(folder, 'uv_unposed_no_shape' + res_modifier, id + '_' + frame_nr + '.npz')); uv_unposed_no_shape_low = uv_unposed_no_shape_low['uv_unposed_no_shape']
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uv_unposed_no_shape_filled_low = np.load(os.path.join(folder, 'uv_unposed_no_shape_filled' + res_modifier, id + '_' + frame_nr + '.npz')); uv_unposed_no_shape_filled_low = uv_unposed_no_shape_filled_low['uv_unposed_no_shape_filled']
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mesh_mask = np.load(os.path.join(folder, 'mesh_mask', id + '.npy'))
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uv_mask = np.load(os.path.join(folder, 'uv_mask' + res_modifier, id + '.npz')); uv_mask = uv_mask['uv_mask']
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mesh = uv2mesh(uv_unposed_no_shape_low[np.newaxis,:,:,:])[0].numpy()
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mesh_filled = uv2mesh(uv_unposed_no_shape_filled_low[np.newaxis,:,:,:])[0].numpy()
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mesh_orig = np.load(os.path.join(folder, 'mesh_unposed_no_shape', id + '_' + frame_nr + '.npz')); mesh_orig = mesh_orig['mesh_unposed_no_shape']
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uv_enhanced = upscale_enhancer([uv_unposed_no_shape_low[np.newaxis,:,:,:], uv_mask[np.newaxis,:,:].astype(np.float32)], training=False)
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mesh_enhanced = uv2mesh(uv_enhanced)[0].numpy()
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metric = np.sum(np.sqrt(np.sum(mesh_mask[:,np.newaxis] * (mesh_orig - mesh) ** 2, axis=-1))) / np.sum(mesh_mask)
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metric_filled = np.sum(np.sqrt(np.sum(mesh_mask[:,np.newaxis] * (mesh_orig - mesh_filled) ** 2, axis=-1))) / np.sum(mesh_mask)
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metric_enhanced = np.sum(np.sqrt(np.sum(mesh_mask[:,np.newaxis] * (mesh_orig - mesh_enhanced) ** 2, axis=-1))) / np.sum(mesh_mask)
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print(metric)
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print(metric_filled)
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print(metric_enhanced)
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print()
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mesh, faces = _maskFaces(mesh, F, mesh_mask)
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mesh_filled, _ = _maskFaces(mesh_filled, F, mesh_mask)
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mesh_enhanced, _ = _maskFaces(mesh_enhanced, F, mesh_mask)
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x = mesh
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y = mesh_filled
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z = mesh_enhanced
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y[:, 2] += 0.5
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z[:, 2] += 1
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meshes = [go.Mesh3d(
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x=x[:,0],
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y=x[:,2],
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z=x[:,1],
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i = faces[:,0],
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j = faces[:,1],
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k = faces[:,2]
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),
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go.Mesh3d(
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x=y[:,0],
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y=y[:,2],
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z=y[:,1],
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i = faces[:,0],
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j = faces[:,1],
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k = faces[:,2]
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),
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go.Mesh3d(
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x=z[:,0],
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y=z[:,2],
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z=z[:,1],
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i = faces[:,0],
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j = faces[:,1],
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k = faces[:,2]
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)]
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fig = go.Figure(data=meshes)
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fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=2, z=1))
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fig.update_layout(
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scene = dict(
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xaxis = dict(nticks=4, range=[-1,1],),
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yaxis = dict(nticks=4, range=[-1,3],),
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zaxis = dict(nticks=4, range=[-1,1],),)
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)
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fig.show()
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print() |