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import os |
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import torch |
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from torchvision.utils import make_grid |
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import numpy as np |
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import pyrender |
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import trimesh |
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import cv2 |
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import torch.nn.functional as F |
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from .render_openpose import render_openpose |
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def create_raymond_lights(): |
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import pyrender |
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thetas = np.pi * np.array([1.0 / 6.0, 1.0 / 6.0, 1.0 / 6.0]) |
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phis = np.pi * np.array([0.0, 2.0 / 3.0, 4.0 / 3.0]) |
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nodes = [] |
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for phi, theta in zip(phis, thetas): |
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xp = np.sin(theta) * np.cos(phi) |
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yp = np.sin(theta) * np.sin(phi) |
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zp = np.cos(theta) |
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z = np.array([xp, yp, zp]) |
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z = z / np.linalg.norm(z) |
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x = np.array([-z[1], z[0], 0.0]) |
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if np.linalg.norm(x) == 0: |
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x = np.array([1.0, 0.0, 0.0]) |
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x = x / np.linalg.norm(x) |
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y = np.cross(z, x) |
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matrix = np.eye(4) |
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matrix[:3,:3] = np.c_[x,y,z] |
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nodes.append(pyrender.Node( |
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light=pyrender.DirectionalLight(color=np.ones(3), intensity=1.0), |
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matrix=matrix |
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)) |
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return nodes |
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class MeshRenderer: |
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def __init__(self, cfg, faces=None): |
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self.cfg = cfg |
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self.focal_length = cfg.EXTRA.FOCAL_LENGTH |
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self.img_res = cfg.MODEL.IMAGE_SIZE |
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self.renderer = pyrender.OffscreenRenderer(viewport_width=self.img_res, |
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viewport_height=self.img_res, |
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point_size=1.0) |
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self.camera_center = [self.img_res // 2, self.img_res // 2] |
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self.faces = faces |
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def visualize(self, vertices, camera_translation, images, focal_length=None, nrow=3, padding=2): |
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images_np = np.transpose(images, (0,2,3,1)) |
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rend_imgs = [] |
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for i in range(vertices.shape[0]): |
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fl = self.focal_length |
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rend_img = torch.from_numpy(np.transpose(self.__call__(vertices[i], camera_translation[i], images_np[i], focal_length=fl, side_view=False), (2,0,1))).float() |
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rend_img_side = torch.from_numpy(np.transpose(self.__call__(vertices[i], camera_translation[i], images_np[i], focal_length=fl, side_view=True), (2,0,1))).float() |
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rend_imgs.append(torch.from_numpy(images[i])) |
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rend_imgs.append(rend_img) |
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rend_imgs.append(rend_img_side) |
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rend_imgs = make_grid(rend_imgs, nrow=nrow, padding=padding) |
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return rend_imgs |
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def visualize_tensorboard(self, vertices, camera_translation, images, pred_keypoints, gt_keypoints, focal_length=None, nrow=5, padding=2): |
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images_np = np.transpose(images, (0,2,3,1)) |
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rend_imgs = [] |
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pred_keypoints = np.concatenate((pred_keypoints, np.ones_like(pred_keypoints)[:, :, [0]]), axis=-1) |
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pred_keypoints = self.img_res * (pred_keypoints + 0.5) |
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gt_keypoints[:, :, :-1] = self.img_res * (gt_keypoints[:, :, :-1] + 0.5) |
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keypoint_matches = [(1, 12), (2, 8), (3, 7), (4, 6), (5, 9), (6, 10), (7, 11), (8, 14), (9, 2), (10, 1), (11, 0), (12, 3), (13, 4), (14, 5)] |
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for i in range(vertices.shape[0]): |
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fl = self.focal_length |
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rend_img = torch.from_numpy(np.transpose(self.__call__(vertices[i], camera_translation[i], images_np[i], focal_length=fl, side_view=False), (2,0,1))).float() |
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rend_img_side = torch.from_numpy(np.transpose(self.__call__(vertices[i], camera_translation[i], images_np[i], focal_length=fl, side_view=True), (2,0,1))).float() |
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body_keypoints = pred_keypoints[i, :25] |
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extra_keypoints = pred_keypoints[i, -19:] |
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for pair in keypoint_matches: |
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body_keypoints[pair[0], :] = extra_keypoints[pair[1], :] |
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pred_keypoints_img = render_openpose(255 * images_np[i].copy(), body_keypoints) / 255 |
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body_keypoints = gt_keypoints[i, :25] |
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extra_keypoints = gt_keypoints[i, -19:] |
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for pair in keypoint_matches: |
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if extra_keypoints[pair[1], -1] > 0 and body_keypoints[pair[0], -1] == 0: |
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body_keypoints[pair[0], :] = extra_keypoints[pair[1], :] |
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gt_keypoints_img = render_openpose(255*images_np[i].copy(), body_keypoints) / 255 |
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rend_imgs.append(torch.from_numpy(images[i])) |
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rend_imgs.append(rend_img) |
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rend_imgs.append(rend_img_side) |
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rend_imgs.append(torch.from_numpy(pred_keypoints_img).permute(2,0,1)) |
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rend_imgs.append(torch.from_numpy(gt_keypoints_img).permute(2,0,1)) |
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rend_imgs = make_grid(rend_imgs, nrow=nrow, padding=padding) |
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return rend_imgs |
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def __call__(self, vertices, camera_translation, image, focal_length=5000, text=None, resize=None, side_view=False, baseColorFactor=(1.0, 1.0, 0.9, 1.0), rot_angle=90): |
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renderer = pyrender.OffscreenRenderer(viewport_width=image.shape[1], |
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viewport_height=image.shape[0], |
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point_size=1.0) |
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material = pyrender.MetallicRoughnessMaterial( |
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metallicFactor=0.0, |
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alphaMode='OPAQUE', |
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baseColorFactor=baseColorFactor) |
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camera_translation[0] *= -1. |
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mesh = trimesh.Trimesh(vertices.copy(), self.faces.copy()) |
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if side_view: |
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rot = trimesh.transformations.rotation_matrix( |
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np.radians(rot_angle), [0, 1, 0]) |
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mesh.apply_transform(rot) |
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rot = trimesh.transformations.rotation_matrix( |
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np.radians(180), [1, 0, 0]) |
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mesh.apply_transform(rot) |
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mesh = pyrender.Mesh.from_trimesh(mesh, material=material) |
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scene = pyrender.Scene(bg_color=[0.0, 0.0, 0.0, 0.0], |
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ambient_light=(0.3, 0.3, 0.3)) |
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scene.add(mesh, 'mesh') |
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camera_pose = np.eye(4) |
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camera_pose[:3, 3] = camera_translation |
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camera_center = [image.shape[1] / 2., image.shape[0] / 2.] |
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camera = pyrender.IntrinsicsCamera(fx=focal_length, fy=focal_length, |
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cx=camera_center[0], cy=camera_center[1]) |
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scene.add(camera, pose=camera_pose) |
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light_nodes = create_raymond_lights() |
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for node in light_nodes: |
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scene.add_node(node) |
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color, rend_depth = renderer.render(scene, flags=pyrender.RenderFlags.RGBA) |
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color = color.astype(np.float32) / 255.0 |
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valid_mask = (color[:, :, -1] > 0)[:, :, np.newaxis] |
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if not side_view: |
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output_img = (color[:, :, :3] * valid_mask + |
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(1 - valid_mask) * image) |
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else: |
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output_img = color[:, :, :3] |
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if resize is not None: |
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output_img = cv2.resize(output_img, resize) |
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output_img = output_img.astype(np.float32) |
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renderer.delete() |
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return output_img |
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