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