344 lines
12 KiB
Python
344 lines
12 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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import os
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import numpy as np
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import imageio
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import torch
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from matplotlib import cm
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import torch.nn.functional as F
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import torchvision.transforms as transforms
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import matplotlib.pyplot as plt
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from PIL import Image, ImageDraw
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def read_video_from_path(path):
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try:
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reader = imageio.get_reader(path)
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except Exception as e:
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print("Error opening video file: ", e)
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return None
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frames = []
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for i, im in enumerate(reader):
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frames.append(np.array(im))
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return np.stack(frames)
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def draw_circle(rgb, coord, radius, color=(255, 0, 0), visible=True):
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# Create a draw object
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draw = ImageDraw.Draw(rgb)
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# Calculate the bounding box of the circle
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left_up_point = (coord[0] - radius, coord[1] - radius)
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right_down_point = (coord[0] + radius, coord[1] + radius)
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# Draw the circle
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draw.ellipse(
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[left_up_point, right_down_point],
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fill=tuple(color) if visible else None,
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outline=tuple(color),
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)
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return rgb
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def draw_line(rgb, coord_y, coord_x, color, linewidth):
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draw = ImageDraw.Draw(rgb)
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draw.line(
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(coord_y[0], coord_y[1], coord_x[0], coord_x[1]),
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fill=tuple(color),
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width=linewidth,
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)
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return rgb
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def add_weighted(rgb, alpha, original, beta, gamma):
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return (rgb * alpha + original * beta + gamma).astype("uint8")
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class Visualizer:
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def __init__(
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self,
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save_dir: str = "./results",
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grayscale: bool = False,
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pad_value: int = 0,
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fps: int = 10,
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mode: str = "rainbow", # 'cool', 'optical_flow'
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linewidth: int = 2,
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show_first_frame: int = 10,
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tracks_leave_trace: int = 0, # -1 for infinite
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):
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self.mode = mode
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self.save_dir = save_dir
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if mode == "rainbow":
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self.color_map = cm.get_cmap("gist_rainbow")
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elif mode == "cool":
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self.color_map = cm.get_cmap(mode)
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self.show_first_frame = show_first_frame
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self.grayscale = grayscale
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self.tracks_leave_trace = tracks_leave_trace
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self.pad_value = pad_value
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self.linewidth = linewidth
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self.fps = fps
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def visualize(
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self,
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video: torch.Tensor, # (B,T,C,H,W)
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tracks: torch.Tensor, # (B,T,N,2)
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visibility: torch.Tensor = None, # (B, T, N, 1) bool
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gt_tracks: torch.Tensor = None, # (B,T,N,2)
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segm_mask: torch.Tensor = None, # (B,1,H,W)
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filename: str = "video",
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writer=None, # tensorboard Summary Writer, used for visualization during training
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step: int = 0,
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query_frame: int = 0,
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save_video: bool = True,
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compensate_for_camera_motion: bool = False,
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):
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if compensate_for_camera_motion:
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assert segm_mask is not None
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if segm_mask is not None:
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coords = tracks[0, query_frame].round().long()
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segm_mask = segm_mask[0, query_frame][coords[:, 1], coords[:, 0]].long()
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video = F.pad(
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video,
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(self.pad_value, self.pad_value, self.pad_value, self.pad_value),
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"constant",
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255,
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)
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tracks = tracks + self.pad_value
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if self.grayscale:
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transform = transforms.Grayscale()
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video = transform(video)
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video = video.repeat(1, 1, 3, 1, 1)
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res_video = self.draw_tracks_on_video(
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video=video,
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tracks=tracks,
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visibility=visibility,
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segm_mask=segm_mask,
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gt_tracks=gt_tracks,
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query_frame=query_frame,
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compensate_for_camera_motion=compensate_for_camera_motion,
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)
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if save_video:
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self.save_video(res_video, filename=filename, writer=writer, step=step)
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return res_video
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def save_video(self, video, filename, writer=None, step=0):
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if writer is not None:
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writer.add_video(
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filename,
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video.to(torch.uint8),
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global_step=step,
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fps=self.fps,
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)
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else:
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os.makedirs(self.save_dir, exist_ok=True)
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wide_list = list(video.unbind(1))
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wide_list = [wide[0].permute(1, 2, 0).cpu().numpy() for wide in wide_list]
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# Prepare the video file path
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save_path = os.path.join(self.save_dir, f"{filename}.mp4")
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# Create a writer object
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video_writer = imageio.get_writer(save_path, fps=self.fps)
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# Write frames to the video file
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for frame in wide_list[2:-1]:
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video_writer.append_data(frame)
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video_writer.close()
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print(f"Video saved to {save_path}")
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def draw_tracks_on_video(
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self,
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video: torch.Tensor,
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tracks: torch.Tensor,
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visibility: torch.Tensor = None,
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segm_mask: torch.Tensor = None,
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gt_tracks=None,
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query_frame: int = 0,
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compensate_for_camera_motion=False,
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):
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B, T, C, H, W = video.shape
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_, _, N, D = tracks.shape
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assert D == 2
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assert C == 3
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video = video[0].permute(0, 2, 3, 1).byte().detach().cpu().numpy() # S, H, W, C
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tracks = tracks[0].long().detach().cpu().numpy() # S, N, 2
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if gt_tracks is not None:
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gt_tracks = gt_tracks[0].detach().cpu().numpy()
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res_video = []
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# process input video
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for rgb in video:
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res_video.append(rgb.copy())
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vector_colors = np.zeros((T, N, 3))
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if self.mode == "optical_flow":
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import flow_vis
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vector_colors = flow_vis.flow_to_color(tracks - tracks[query_frame][None])
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elif segm_mask is None:
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if self.mode == "rainbow":
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y_min, y_max = (
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tracks[query_frame, :, 1].min(),
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tracks[query_frame, :, 1].max(),
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)
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norm = plt.Normalize(y_min, y_max)
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for n in range(N):
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color = self.color_map(norm(tracks[query_frame, n, 1]))
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color = np.array(color[:3])[None] * 255
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vector_colors[:, n] = np.repeat(color, T, axis=0)
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else:
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# color changes with time
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for t in range(T):
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color = np.array(self.color_map(t / T)[:3])[None] * 255
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vector_colors[t] = np.repeat(color, N, axis=0)
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else:
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if self.mode == "rainbow":
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vector_colors[:, segm_mask <= 0, :] = 255
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y_min, y_max = (
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tracks[0, segm_mask > 0, 1].min(),
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tracks[0, segm_mask > 0, 1].max(),
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)
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norm = plt.Normalize(y_min, y_max)
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for n in range(N):
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if segm_mask[n] > 0:
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color = self.color_map(norm(tracks[0, n, 1]))
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color = np.array(color[:3])[None] * 255
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vector_colors[:, n] = np.repeat(color, T, axis=0)
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else:
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# color changes with segm class
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segm_mask = segm_mask.cpu()
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color = np.zeros((segm_mask.shape[0], 3), dtype=np.float32)
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color[segm_mask > 0] = np.array(self.color_map(1.0)[:3]) * 255.0
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color[segm_mask <= 0] = np.array(self.color_map(0.0)[:3]) * 255.0
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vector_colors = np.repeat(color[None], T, axis=0)
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# draw tracks
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if self.tracks_leave_trace != 0:
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for t in range(query_frame + 1, T):
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first_ind = (
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max(0, t - self.tracks_leave_trace) if self.tracks_leave_trace >= 0 else 0
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)
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curr_tracks = tracks[first_ind : t + 1]
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curr_colors = vector_colors[first_ind : t + 1]
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if compensate_for_camera_motion:
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diff = (
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tracks[first_ind : t + 1, segm_mask <= 0]
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- tracks[t : t + 1, segm_mask <= 0]
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).mean(1)[:, None]
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curr_tracks = curr_tracks - diff
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curr_tracks = curr_tracks[:, segm_mask > 0]
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curr_colors = curr_colors[:, segm_mask > 0]
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res_video[t] = self._draw_pred_tracks(
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res_video[t],
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curr_tracks,
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curr_colors,
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)
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if gt_tracks is not None:
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res_video[t] = self._draw_gt_tracks(res_video[t], gt_tracks[first_ind : t + 1])
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# draw points
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for t in range(query_frame, T):
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img = Image.fromarray(np.uint8(res_video[t]))
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for i in range(N):
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coord = (tracks[t, i, 0], tracks[t, i, 1])
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visibile = True
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if visibility is not None:
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visibile = visibility[0, t, i]
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if coord[0] != 0 and coord[1] != 0:
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if not compensate_for_camera_motion or (
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compensate_for_camera_motion and segm_mask[i] > 0
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):
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img = draw_circle(
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img,
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coord=coord,
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radius=int(self.linewidth * 2),
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color=vector_colors[t, i].astype(int),
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visible=visibile,
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)
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res_video[t] = np.array(img)
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# construct the final rgb sequence
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if self.show_first_frame > 0:
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res_video = [res_video[0]] * self.show_first_frame + res_video[1:]
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return torch.from_numpy(np.stack(res_video)).permute(0, 3, 1, 2)[None].byte()
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def _draw_pred_tracks(
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self,
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rgb: np.ndarray, # H x W x 3
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tracks: np.ndarray, # T x 2
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vector_colors: np.ndarray,
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alpha: float = 0.5,
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):
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T, N, _ = tracks.shape
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rgb = Image.fromarray(np.uint8(rgb))
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for s in range(T - 1):
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vector_color = vector_colors[s]
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original = rgb.copy()
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alpha = (s / T) ** 2
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for i in range(N):
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coord_y = (int(tracks[s, i, 0]), int(tracks[s, i, 1]))
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coord_x = (int(tracks[s + 1, i, 0]), int(tracks[s + 1, i, 1]))
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if coord_y[0] != 0 and coord_y[1] != 0:
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rgb = draw_line(
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rgb,
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coord_y,
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coord_x,
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vector_color[i].astype(int),
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self.linewidth,
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)
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if self.tracks_leave_trace > 0:
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rgb = Image.fromarray(
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np.uint8(add_weighted(np.array(rgb), alpha, np.array(original), 1 - alpha, 0))
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)
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rgb = np.array(rgb)
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return rgb
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def _draw_gt_tracks(
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self,
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rgb: np.ndarray, # H x W x 3,
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gt_tracks: np.ndarray, # T x 2
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):
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T, N, _ = gt_tracks.shape
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color = np.array((211, 0, 0))
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rgb = Image.fromarray(np.uint8(rgb))
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for t in range(T):
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for i in range(N):
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gt_tracks = gt_tracks[t][i]
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# draw a red cross
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if gt_tracks[0] > 0 and gt_tracks[1] > 0:
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length = self.linewidth * 3
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coord_y = (int(gt_tracks[0]) + length, int(gt_tracks[1]) + length)
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coord_x = (int(gt_tracks[0]) - length, int(gt_tracks[1]) - length)
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rgb = draw_line(
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rgb,
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coord_y,
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coord_x,
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color,
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self.linewidth,
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)
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coord_y = (int(gt_tracks[0]) - length, int(gt_tracks[1]) + length)
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coord_x = (int(gt_tracks[0]) + length, int(gt_tracks[1]) - length)
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rgb = draw_line(
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rgb,
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coord_y,
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coord_x,
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color,
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self.linewidth,
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)
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rgb = np.array(rgb)
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return rgb
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