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visualize_tsgm.py
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visualize_tsgm.py
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import os
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
from tqdm import tqdm
import argparse, pickle
import joblib
from env_utils.custom_habitat_map import AGENT_IMGS, OBJECT_CATEGORY_NODES
from habitat.utils.visualizations import utils, maps
from habitat.tasks.utils import cartesian_to_polar
import imageio
from scipy.ndimage.interpolation import rotate
import cv2
import csv
from utils.statics import CATEGORIES, DETECTION_CATEGORIES
import matplotlib.patches as mpatches
import glob
from utils.vis_utils import colors_rgb
project_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
parser = argparse.ArgumentParser()
parser.add_argument(
"--tag",
default="tsgm_gibsontiny",
type=str,
)
parser.add_argument(
"--dataset",
default="gibson",
type=str,
)
parser.add_argument(
"--draw-obj-graph",
action='store_true',
default=False,
)
parser.add_argument(
"--draw-im-graph",
action='store_true',
default=False,
)
parser.add_argument(
"--attn-method",
default="curr",
type=str,
)
parser.add_argument(
"--data-dir",
default="/data4/nuri/tsgm_visualize",
type=str,
)
parser.add_argument(
"--file-idx",
default=-1,
type=int,
)
args = parser.parse_args()
fps = 20
args.data_dir = os.path.join(args.data_dir, args.tag)
os.makedirs(args.data_dir, exist_ok=True)
os.makedirs(os.path.join(args.data_dir, "output"), exist_ok=True)
os.makedirs(os.path.join(args.data_dir, "temp"), exist_ok=True)
GOAL_FLAG = imageio.imread(os.path.join(project_dir, "utils/assets/maps_topdown_flag/flag2_red.png"))
IMG_NODE_FLAG = imageio.imread(os.path.join(project_dir, "utils/assets/maps_topdown_node/flag2_red.png"))
OBJ_NODE_FLAG = imageio.imread(os.path.join(project_dir, "utils/assets/maps_topdown_node/yellow_circle.png"))
JACKAL_SPRITE = imageio.imread(os.path.join(project_dir, "utils/assets/maps_topdown_agent_sprite/jackal.png"))
JACKAL_SPRITE = np.ascontiguousarray(np.flipud(JACKAL_SPRITE))
initial_jackal_size = JACKAL_SPRITE.shape[0]
obj_thresh = 0.7
with open(f"./data/scene_info/{args.dataset}/{args.dataset}_bounds.txt", "rb") as fp: # Unpickling
bounds = pickle.load(fp)
files = list(np.sort(os.listdir(os.path.join(args.data_dir, "video"))))
if len(files) > 0:
files = np.stack(files)
if args.file_idx > -1:
file_indices = [args.file_idx]
else:
file_indices = list(np.arange(len(files)))
start_index = 0
collected_data = os.listdir(os.path.join(args.data_dir, "output"))
collected_ids = np.unique([int(aa.split(".")[0].split("_")[0]) for aa in collected_data])
for collected_id in collected_ids:
try:
file_indices.remove(collected_id)
except:
pass
render_configs = joblib.load(os.path.join("./data/floorplans", f"{args.dataset}_floorplans/render_config.pkl"))
def draw_graph(node_image, i, vis_data, vis_features, attn_method="curr", draw_im_graph=True, draw_obj_graph=True, use_detector=False, font_size=2, font_thickness=2,
im_node_size=10, obj_node_size=10, im_edge_size=3, obj_edge_size=1, rotated=False):
node_list = vis_data['graph'][i]['global_memory_pose']
affinity = vis_data['graph'][i]['global_A']
obj_node_list = vis_data['graph'][i]['object_memory_pose']
obj_node_category_list = vis_data['graph'][i]['object_memory_category']
obj_node_score = vis_data['graph'][i]['object_memory_score']
ov_affinity = vis_data['graph'][i]['object_memory_A_OV']
global_step = vis_data['global_step'][i]['global_step']
curr_im_attn = vis_features['features'][global_step]['curr_attn'].reshape(-1).cpu().detach().numpy()
curr_obj_attn = np.mean(vis_features['features'][global_step]['curr_obj_attn'].squeeze(0).cpu().detach().numpy(), 0)
goal_obj_attn = vis_features['features'][global_step]['goal_obj_attn'].squeeze(0).cpu().detach().numpy()
draw_obj_point_list = []
draw_im_point_list = []
if draw_obj_graph:
h, w, _ = node_image.shape
for idx, node_position in enumerate(obj_node_list):
if obj_node_score[idx] > obj_thresh:
if (node_position[0] < upper_bound[0]) & (node_position[2] < upper_bound[2]) & (node_position[0] > lower_bound[0]) & (node_position[2] > lower_bound[2]):
try:
draw_obj_point_list.append([node_position, obj_node_category_list[idx], curr_obj_attn[idx], goal_obj_attn[idx]])
except:
draw_obj_point_list.append([node_position, obj_node_category_list[idx], 0, 0])
if draw_im_graph:
neighbors = np.where(ov_affinity[idx])[0]
for neighbor_idx in neighbors:
if use_detector:
node_color = maps.TOP_DOWN_MAP_COLORS[OBJECT_CATEGORY_NODES[DETECTION_CATEGORIES[int(obj_node_category_list[idx])]]]
else:
node_color = tuple(colors_rgb[int(obj_node_category_list[idx])])
node_color = (int(node_color[0]), int(node_color[1]), int(node_color[2]))
neighbor_position = node_list[neighbor_idx]
node_position_ = get_map_coord(node_position)[::-1]
neighbor_position_ = get_map_coord(neighbor_position)[::-1]
line = cv2.line(
node_image,
tuple(node_position_),
tuple(neighbor_position_),
node_color,
thickness=obj_edge_size
)
alpha = 0.8
node_image = cv2.addWeighted(line, alpha, node_image, 1 - alpha, 0)
if draw_im_graph:
for idx, node_position in enumerate(node_list):
try:
draw_im_point_list.append([node_position, [15, 119, 143], curr_im_attn[idx]])#, goal_im_attn[idx]])
except:
draw_im_point_list.append([node_position, [15, 119, 143], 0.])#, goal_im_attn[idx]])
neighbors = np.where(affinity[idx])[0]
for neighbor_idx in neighbors:
neighbor_position = node_list[neighbor_idx]
node_position_ = get_map_coord(node_position)
neighbor_position_ = get_map_coord(neighbor_position)
cv2.line(
node_image,
tuple(node_position_[::-1]),
tuple(neighbor_position_[::-1]),
[177, 232, 246],
thickness=im_edge_size,
)
for node_position, node_color, curr_att in draw_im_point_list:
graph_node_center = get_map_coord(node_position)[::-1]
frame_cpy = node_image.copy()
if attn_method == "curr":
att = curr_att
else:
att = 0
if att > 0.1:
cv2.circle(node_image, tuple(graph_node_center), im_node_size, node_color, -1)
att = float(att.reshape(-1))
# node_image = cv2.addWeighted(node_image, att, frame_cpy, 1 - att, gamma=0)
node_image = cv2.addWeighted(node_image, np.maximum(att, 0.5), frame_cpy, 1 - np.maximum(att, 0.5), gamma=0)
cv2.circle(node_image, tuple(graph_node_center), im_node_size // 3, node_color, -1)
if att > 0.1:
if rotated:
H, W = node_image.shape[:2]
font_image = np.zeros_like(cv2.rotate(node_image, cv2.ROTATE_90_COUNTERCLOCKWISE)).astype(np.uint8)
font_image = cv2.putText(font_image, "%.1f" % (att * 100.), (graph_node_center[1], W - graph_node_center[0]), cv2.FONT_HERSHEY_SIMPLEX, font_size, [255, 255, 255], thickness=font_thickness, lineType=cv2.LINE_AA)
font_image = cv2.rotate(font_image, cv2.ROTATE_90_CLOCKWISE)
node_image[font_image.sum(-1) > 0] = 0
else:
node_image = cv2.putText(node_image, "%.1f" % (att * 100.), tuple(graph_node_center), cv2.FONT_HERSHEY_SIMPLEX, font_size, [0, 0, 0], thickness=font_thickness, lineType=cv2.LINE_AA)
if draw_obj_graph:
cnt = 0
arg_idx = list(np.argsort(curr_obj_attn)[::-1][:3])
for node_position, node_category, curr_obj_attn_, curr_goal_attn in reversed(draw_obj_point_list):
if use_detector:
node_color = maps.TOP_DOWN_MAP_COLORS[OBJECT_CATEGORY_NODES[DETECTION_CATEGORIES[int(node_category)]]]
else:
# node_color = maps.TOP_DOWN_MAP_COLORS[OBJECT_CATEGORY_NODES[CATEGORIES[dn][int(node_category)]]]
node_color = tuple(colors_rgb[int(node_category)])
node_color = (int(node_color[0]), int(node_color[1]), int(node_color[2]))
if attn_method == "curr":
obj_att = curr_obj_attn_
elif attn_method == "goal":
obj_att = curr_goal_attn
else:
obj_att = 0.0
graph_node_center = get_map_coord(node_position)[::-1]
frame_cpy = node_image.copy()
if attn_method != "none":
obj_att = float(obj_att)
if cnt in arg_idx:
cv2.circle(node_image, tuple(graph_node_center), obj_node_size, node_color, -1)
node_image = cv2.addWeighted(node_image, np.maximum(obj_att, 0.5), frame_cpy, 1 - np.maximum(obj_att, 0.5), gamma=0)
cv2.circle(node_image, tuple(graph_node_center), obj_node_size // 2, node_color, -1)
cnt += 1
cnt = 0
for node_position, node_category, curr_obj_attn, curr_goal_attn in reversed(draw_obj_point_list):
if attn_method == "curr":
obj_att = curr_obj_attn
elif attn_method == "goal":
obj_att = curr_goal_attn
else:
obj_att = 0.0
if cnt in arg_idx:
graph_node_center = get_map_coord(node_position)[::-1]
if rotated:
H, W = node_image.shape[:2]
font_image = np.zeros_like(cv2.rotate(node_image, cv2.ROTATE_90_COUNTERCLOCKWISE)).astype(np.uint8)
font_image = cv2.putText(font_image, "%.1f" % (obj_att * 100.), (graph_node_center[1], W - graph_node_center[0]), cv2.FONT_HERSHEY_SIMPLEX, font_size, [255, 255, 255], thickness=font_thickness, lineType=cv2.LINE_AA)
font_image = cv2.rotate(font_image, cv2.ROTATE_90_CLOCKWISE)
node_image[font_image.sum(-1) > 0] = 0
else:
node_image = cv2.putText(node_image, "%.1f" % (obj_att * 100.), tuple(graph_node_center), cv2.FONT_HERSHEY_SIMPLEX, font_size, [0, 0, 0], thickness=font_thickness, lineType=cv2.LINE_AA)
cnt += 1
return node_image
def get_floor(position, scan_name):
floor = int(np.argmin([abs(float(render_configs[scan_name][i]['z_low']) - position[1]) for i in render_configs[scan_name].keys()]))
return floor
def get_map_coord(position, lower_bound, upper_bound, imgWidth, imgHeight):
A = [position[0] - (upper_bound[0] + lower_bound[0]) / 2, position[2] - (upper_bound[2] + lower_bound[2]) / 2, 1, 1]
grid_x, grid_y = np.array([imgWidth / 2, imgHeight / 2]) * np.matmul(P, A)[:2] + np.array([imgWidth / 2, imgHeight / 2])
return tuple(np.array([int(grid_y), int(grid_x)]))
def draw_bbox(object_info, rgb, mode="obs"):
if mode == "obs":
bboxes = object_info['object']
object_mask = bboxes.sum(1) > 0
bboxes = bboxes[object_mask]
object_score = object_info['object_score'][object_mask]
object_category = object_info['object_category'][object_mask].astype(np.int32)
# object_pose = object_info['object_pose'][object_mask]
elif mode == "target":
bboxes = object_info['target_object']
object_mask = bboxes.sum(1) > 0
bboxes = bboxes[object_mask]
object_score = object_info['target_object_score'][object_mask]
object_category = object_info['target_object_category'][object_mask].astype(np.int32)
# object_pose = object_info['target_object_pose'][object_mask]
else:
raise NotImplementedError
if len(bboxes) > 0:
H, W = rgb.shape[:2]
if bboxes.max() <= 1:
bboxes[:, 0] = bboxes[:, 0] * W
bboxes[:, 1] = bboxes[:, 1] * H
bboxes[:, 2] = bboxes[:, 2] * W
bboxes[:, 3] = bboxes[:, 3] * H
bboxes_mask = ((bboxes[:, 2] - bboxes[:, 0]) < W - 2) & ((bboxes[:, 3] - bboxes[:, 1]) < H - 2)
for bbox_i, bbox in enumerate(bboxes):
if object_score[bbox_i] > obj_thresh and bboxes_mask[bbox_i]:
color = tuple(colors_rgb[int(object_category[bbox_i])])
cv2.rectangle(rgb, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])),
(float(color[0]), float(color[1]), float(color[2])), thickness=3)
for bbox_i, bbox in enumerate(bboxes):
if object_score[bbox_i] > obj_thresh and bboxes_mask[bbox_i]:
if len(object_category) > 0:
label = CATEGORIES[args.dataset.split("_")[0]][object_category[bbox_i]]
imgHeight, imgWidth, _ = rgb.shape
cv2.putText(rgb, label, (int(bbox[0]), int(bbox[1]) + 10), 0, 5e-3 * imgHeight, (255, 255, 0), 1)
return rgb
def create_gif(path_to_images, name_gif):
filenames = glob.glob(path_to_images)
filenames = sorted(filenames)
images = []
for filename in tqdm(filenames):
images.append(imageio.imread(filename))
kargs = {"duration": 0.25}
# kargs = {'fps': 5.0, 'quantizer': 'nq'}
imageio.mimsave(name_gif, images, **kargs)#, "GIF-FI", **kargs)
def load_file(file_idx):
video_name = os.path.join(args.data_dir, "video", files[file_idx])
data_name = os.path.join(args.data_dir, "others", files[file_idx].replace("_success", "_data_success").replace(".mp4", ".dat.gz"))
feat_name = os.path.join(args.data_dir, "others", files[file_idx].replace("_success", "_global_success").replace(".mp4", ".dat.gz"))
vis_data = joblib.load(data_name)
vis_features = joblib.load(feat_name)
scan_name = video_name.split("_")[-4]
try:
floor = get_floor(vis_data['position'][-2][0], scan_name)
except:
floor = get_floor(vis_data['position'][0][0], scan_name)
P = render_configs[scan_name][floor]['Projection']
imgWidth = round(float(render_configs[scan_name][floor]['width']))
imgHeight = round(float(render_configs[scan_name][floor]['height']))
world_min_width = float(render_configs[scan_name][floor]['x_low'])
world_max_width = float(render_configs[scan_name][floor]['x_high'])
world_min_height = float(render_configs[scan_name][floor]['y_low'])
world_max_height = float(render_configs[scan_name][floor]['y_high'])
worldWidth = abs(world_min_width) + abs(world_max_width)
worldHeight = abs(world_min_height) + abs(world_max_height)
imgWidth = round(float(render_configs[scan_name][floor]['width']))
imgHeight = round(float(render_configs[scan_name][floor]['height']))
lower_bound = bounds[scan_name][0]
upper_bound = bounds[scan_name][1]
return vis_data, vis_features, video_name, scan_name, floor, imgWidth, imgHeight, P, lower_bound, upper_bound
def get_polar_angle(ref_rotation=None):
vq = np.quaternion(0, 0, 0, 0)
vq.imag = np.array([0, 0, -1])
heading_vector = (ref_rotation.inverse() * vq * ref_rotation).imag
phi = cartesian_to_polar(-heading_vector[2], heading_vector[0])[1]
x_y_flip = -np.pi / 2
return np.array(phi) + x_y_flip
existing_categories = {}
cats = ["skateboard", "bottle", "bowl", "bench", "suitcase", "handbag", "couch", "sports ball", "chair", "bed", "tv",
"microwave", "sink", "clock", "dining table", "laptop", "keyboard", "oven", "refrigerator", "vase", "potted plant",
"toilet", "cell phone", "book"]
for i, name in enumerate(CATEGORIES["gibson"]):
if name in cats:
existing_categories[name] = tuple(colors_rgb[CATEGORIES[args.dataset.split("_")[0]].index(name)]) # maps.TOP_DOWN_MAP_COLORS[OBJECT_CATEGORY_NODES[name]] # (int(color[0]), int(color[1]),int(color[2]))
for file_idx in tqdm(file_indices):
vis_data, vis_features, video_name, scan_name, floor, imgWidth, imgHeight, P, lower_bound, upper_bound = load_file(file_idx)
map_name = os.path.join(project_dir, "..", "{}_floorplans/out_dir_rgb_png/output_{}_level_{}.0.png".format(args.dataset, scan_name, floor))
ortho_map = cv2.imread(map_name)[..., ::-1][..., :3]
ortho_map = cv2.imread(os.path.join(project_dir, "data/{}_floorplans/rgb/{}_level_{}.png".format(args.dataset, scan_name, floor, args.dataset)))[...,::-1][...,:3]
ortho_mask = cv2.imread(os.path.join(project_dir, "data/{}_floorplans/mask/{}_level_{}.png".format(args.dataset, scan_name, floor, args.dataset)), 0)
# map_name = os.path.join(args.data_dir, "..", "{}_floorplans/out_dir_depth_png/output_{}_level_{}.0.png".format(args.dataset, scan_name, floor))
# ortho_depth = cv2.imread(map_name, 0)
aa = np.stack(np.where(ortho_mask == 0), 1)
ortho_map[ortho_mask == 1] = 255
x1, y1 = aa[:, 0].min(), aa[:, 1].min()
x2, y2 = aa[:, 0].max(), aa[:, 1].max()
map_width, map_height = ortho_mask.shape
pixel_per_meter = np.maximum((x2 - x1 + 1) / (upper_bound - lower_bound)[0], (y2 - y1 + 1) / (upper_bound - lower_bound)[2])
jackal_radius_px = int(pixel_per_meter * 0.2)
goal_size_px = int(pixel_per_meter * 1.0)
im_node_size = int(pixel_per_meter * 0.3)
obj_node_size = int(pixel_per_meter * 0.4)
im_edge_size = int(pixel_per_meter * 0.16)
obj_edge_size = int(pixel_per_meter * 0.07)
font_size = np.max([int((x2 - x1 + 1) / 600.), int((y2 - y1 + 1) / 600.), 1])
font_thickness = np.max([int((x2 - x1 + 1) / 200.), int((y2 - y1 + 1) / 200.), 3])
# font_size = np.max([int(pixel_per_meter*0.01), 1])
# font_thickness = np.max([int(pixel_per_meter*0.024),3])
"""
Save video
"""
cap = cv2.VideoCapture(video_name)
input_rgb = []
while (cap.isOpened()):
ret, frame = cap.read()
if ret == True:
input_rgb.append(frame[:, :, ::-1])
else:
break
cap.release()
input_rgb = np.array(np.stack(input_rgb).astype(np.uint8))
# GOAL_FLAG
rotated = False
video = []
fog_of_war_mask = (ortho_mask.astype(np.int32)).copy()[..., None]
for i in tqdm(np.arange(len(vis_data['map']))):
map_image = ortho_map.copy()
map_image[ortho_mask] = 255
gray_image = cv2.cvtColor(map_image, cv2.COLOR_BGR2GRAY)
gray_image = gray_image[..., None]
agent_rotation = vis_data['map'][i]['agent_angle']
agent_loc = np.array(vis_data['map'][i]['agent_loc'])
agent_center_coord = get_map_coord(agent_loc, lower_bound, upper_bound, imgWidth, imgHeight)
map_image = draw_graph(map_image, i, vis_data, vis_features, draw_obj_graph=args.draw_obj_graph, attn_method=args.attn_method,
font_size=font_size, font_thickness=font_thickness, im_node_size=im_node_size, obj_node_size=obj_node_size, im_edge_size=im_edge_size, obj_edge_size=obj_edge_size, rotated=rotated)
# path = np.stack([vis_data['map'][j]['agent_loc'] for j in range(1, len(vis_data['map']))])[:i]
# map_image = utils.paste_overlapping_image(map_image, goal_flag, target_coord_map)
rotated_jackal = rotate(JACKAL_SPRITE, agent_rotation * 180 / np.pi)
new_size = rotated_jackal.shape[0]
jackal_size_px = max(1, int(jackal_radius_px * 2 * new_size / initial_jackal_size))
resized_jackal = cv2.resize(rotated_jackal, (jackal_size_px, jackal_size_px), interpolation=cv2.INTER_LINEAR)
map_image = utils.paste_overlapping_image(map_image, resized_jackal, agent_center_coord)
map_image = map_image[x1:x2, y1:y2]
if args.draw_obj_graph:
input_rgb_i = draw_bbox(vis_data['objects'][i], input_rgb[i])
else:
input_rgb_i = input_rgb[i]
fig, ax = plt.subplots(3, 2, figsize=(9, 5), gridspec_kw={'width_ratios': [2, 1]})
gs = ax[0, 1].get_gridspec()
# remove the underlying axes
ax[0, 1].remove()
ax[1, 1].remove()
ax[2, 1].remove()
axbig = fig.add_subplot(gs[:, 1])
ax[0][0].imshow(target_rgb)
# ax[0][0].set_title(f"Target: {vis_data['episode']['goal_name']}")
ax[0][0].axis("off")
ax[1][0].imshow(input_rgb_i)
ax[1][0].set_title(f'Observation at time step {i}')
ax[1][0].axis("off")
ax[2][0].legend([mpatches.Patch(color=(v[0] / 255., v[1] / 255., v[2] / 255.)) for k, v in existing_categories.items()],
['{}'.format(k) for k, v in existing_categories.items()], fontsize=1.2, loc='upper left', # , bbox_to_anchor=(0., 1.0)
fancybox=True, ncol=4, prop={'family': 'monospace', 'size': 8}) # , 'size': 2
ax[2][0].set_title(f'Object Category Legend')
ax[2][0].margins(0)
ax[2][0].axis("off")
if map_image.shape[1] > map_image.shape[0]:
map_image = cv2.rotate(map_image, cv2.ROTATE_90_COUNTERCLOCKWISE)
axbig.imshow(map_image)
axbig.set_title(f'Graph Attention')
axbig.axis("off")
fig.tight_layout()
fig.savefig(os.path.join(args.data_dir, f"temp/{file_idx:03d}_{i:03d}.png"))
# fig.savefig(os.path.join(args.data_dir, f"temp/{file_idx:03d}_{i:03d}.svg"), format="svg")
plt.close()
create_gif(os.path.join(args.data_dir, f"temp/{file_idx:03d}_*.png"), os.path.join(args.data_dir, f"output/{file_idx:03d}.gif"))