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URDataset.py
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URDataset.py
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import os
import numpy as np
import tqdm
from PIL import Image
import data_utils
import json
from plyfile import PlyData
from data_utils import get_bbox2d, check_kp_bound
# Hand-craft 3d keypoint definition
kpt3d_ur = np.array([
# front
[-0.000354, -0.046325, 0.046288],
[ 0.024613, -0.014480, 0.040862],
[-0.024354, -0.015258, 0.041063],
# left
[0.044573, -0.030300, 0.005540],
[0.036552, -0.040553, -0.020870],
[0.027000, -0.037935, -0.030877],
# right
[-0.043479, -0.027063, 0.012919],
[-0.040687, -0.019993, -0.013801],
])
kpt3d_franka = np.array([
[0.083473, 0.005882, 0.043592],
[0.064259, -0.023191, 0.053959],
[0.089078, 0.007429, 0.006067],
[0.017245, -0.034455, 0.011027],
[-0.053517, -0.027140, 0.027207],
[0.003370, -0.048394, 0.000608],
[0.070592, 0.042233, -0.022286],
[0.089077, 0.006222, -0.026412],
])
kpt3d = kpt3d_franka
vis_iter = 0
def vis(rgb, model_2d=None, corner_2d=None, draw_box=False, kpt2d=None, bbox2d=None, mask=None):
global vis_iter
from matplotlib import pyplot as plt
import copy
plt.close()
rgb_vis = np.array(rgb)
fg_id = np.where(mask != 0)
rgb_vis[fg_id[0], fg_id[1], :] = 255
plt.imshow(rgb_vis)
if model_2d is not None:
plt.plot(model_2d[:, 0], model_2d[:, 1], 'b.')
if corner_2d is not None:
plt.plot(corner_2d[:, 0], corner_2d[:, 1], 'r.')
if draw_box:
from data_utils import draw_box3d
draw_box3d(corner_2d, plt=plt)
if kpt2d is not None:
plt.plot(kpt2d[:, 0], kpt2d[:, 1], 'g.')
if bbox2d is not None:
from data_utils import draw_box2d
draw_box2d(bbox2d, plt=plt)
plt.savefig('data/vis/{}.png'.format(vis_iter))
# plt.show()
vis_iter += 1
def read_ply_points(ply_path):
ply = PlyData.read(ply_path)
data = ply.elements[0].data
points = np.stack([data['x'], data['y'], data['z']], axis=1)
return points
def sample_fps_points(data_root):
ply_path = os.path.join(data_root, 'model.ply')
ply_points = read_ply_points(ply_path)
fps_points = fps_utils.farthest_point_sampling(ply_points, 8, True)
np.savetxt(os.path.join(data_root, 'fps.txt'), fps_points)
def get_model_corners(model):
min_x, max_x = np.min(model[:, 0]), np.max(model[:, 0])
min_y, max_y = np.min(model[:, 1]), np.max(model[:, 1])
min_z, max_z = np.min(model[:, 2]), np.max(model[:, 2])
corners_3d = np.array([
[min_x, min_y, min_z],
[min_x, min_y, max_z],
[min_x, max_y, min_z],
[min_x, max_y, max_z],
[max_x, min_y, min_z],
[max_x, min_y, max_z],
[max_x, max_y, min_z],
[max_x, max_y, max_z],
])
return corners_3d
def record_ann(model_meta, img_id, ann_id, images, annotations, render_mask=True):
data_root = model_meta['data_root']
corner_3d = model_meta['corner_3d']
center_3d = model_meta['center_3d']
fps_3d = model_meta['fps_3d']
cam_info = model_meta['cam_info']
meta_dir = os.path.join(data_root, 'frame')
rgb_dir = os.path.join(data_root, 'rgb')
mask_dir = os.path.join(data_root, 'mask', '{}_mask.png')
if not os.path.isdir(os.path.dirname(mask_dir)):
os.makedirs(os.path.dirname(mask_dir))
rgb_files = os.listdir(rgb_dir)
rgb_files = [fname for fname in rgb_files if '.png' in fname]
global vis_iter
inds = range(len(rgb_files))
if render_mask:
from render_utils import RenderingEngine
for cam_name in cam_info.keys():
model_path = model_meta['model_path']
K = np.array(cam_info[cam_name]['intrinsics'])
im_shape = cam_info[cam_name]['im_shape']
render_engine = RenderingEngine(K, im_shape, model_path)
cam_info[cam_name].update({'render_engine': render_engine})
else:
for cam_name in cam_info.keys():
cam_info[cam_name].update({'render_engine': None})
for ind in tqdm.tqdm(inds):
# Load metadata
meta_path = os.path.join(meta_dir, '{}.json'.format(ind))
frame_meta = json.load(open(meta_path, 'r'))
cam_name = frame_meta['cam_name']
# skip frame from invalid camera
if cam_name not in cam_info.keys():
continue
# load rgb image
rgb_path = os.path.join(rgb_dir, '{}.png'.format(ind))
rgb = Image.open(rgb_path)
img_size = rgb.size
img_id += 1
info = {'file_name': rgb_path, 'height': img_size[1], 'width': img_size[0], 'id': img_id}
# Get metadata & annotations
Tcw = np.array(cam_info[cam_name]['pose'])
K = np.array(cam_info[cam_name]['intrinsics'])
Tow = frame_meta['pose']
pose_Toc = np.dot(np.linalg.inv(Tcw), Tow)[:3]
corner_2d = data_utils.project(corner_3d, K, pose_Toc)
center_2d = data_utils.project(center_3d[None], K, pose_Toc)[0]
fps_2d = data_utils.project(fps_3d, K, pose_Toc)
box = get_bbox2d(corner_2d, rgb.size[0], rgb.size[1])
invalid_count = check_kp_bound(fps_2d, rgb.size[0], rgb.size[1])
if invalid_count > fps_3d.shape[0] * 0.2:
img_id -= 1
continue
mask_path = ''
if render_mask:
render_engine = cam_info[cam_name]['render_engine']
import cv2
c, d = render_engine.render_depth(pose_Toc, need_color=True, convert=True)
mask = np.zeros(d.shape)
mask[np.where(d != 0)] = 255
mask_path = mask_dir.format(ind)
cv2.imwrite(mask_path, mask)
else:
mask = None
# vis(rgb, model_2d=None, corner_2d=None, draw_box=False, kpt2d=fps_2d, bbox2d=None, mask=mask)
ann_id += 1
anno = {'image_id': img_id, 'category_id': 1, 'id': ann_id}
anno.update({'mask_path': mask_path, 'cam_name': cam_name})
anno.update({'corner_3d': corner_3d.tolist(), 'corner_2d': corner_2d.tolist()})
anno.update({'center_3d': center_3d.tolist(), 'center_2d': center_2d.tolist()})
anno.update({'fps_3d': fps_3d.tolist(), 'fps_2d': fps_2d.tolist()})
anno.update({'K': K.tolist(), 'pose': pose_Toc.tolist()})
anno.update({'bbox': box, 'type': 'real', 'cls': 'guo'})
# Append annotation
images.append(info)
annotations.append(anno)
return img_id, ann_id
def _get_vrep_data(data_root, img_id, ann_id, images, annotations, cam_name_list=None, render_mask=True):
"""
Generate data annotation files for dataset in the given path
:param data_root: data directory, should include subdirectories [frame, rgb, 'meta.json']
:param img_id: current image id, will be none zero and matters
only when generating one annotation file for more than one data folder
:param ann_id: current annotation id, will be none zero and matters
only when generating one annotation file for more than one data folder
:param images: array to store image information, will be none empty and matters
only when generating one annotation file for more than one data folder
:param annotations: array to store annotations, will be none empty and matters
only when generating one annotation file for more than one data folder
:param cam_name_list: list of camera names to be used, ['ALL'] means use all cameras available
:param render_mask: whether or not to render mask with pyrender
:return:
"""
# Obtain meta information
meta = json.load(open(os.path.join(data_root, 'meta.json'), 'r'))
cam_info = meta['cam_info']
# remove invalid sensor
if cam_name_list is not None:
if cam_name_list[0] == 'ALL':
cam_name_list = list(cam_info.keys())
else:
pop_list = []
for cam_name in cam_info.keys():
if cam_name not in cam_name_list:
pop_list.append(cam_name)
for cam_name in pop_list:
cam_info.pop(cam_name)
model = read_ply_points(os.path.join(data_root, 'model.ply'))
model_path = os.path.join(data_root, 'model.ply')
corner_3d = get_model_corners(model)
center_3d = (np.max(corner_3d, 0) + np.min(corner_3d, 0)) / 2
fps_3d = kpt3d
model_meta = {
'cam_info': cam_info,
'corner_3d': corner_3d,
'center_3d': center_3d,
'fps_3d': fps_3d,
'data_root': data_root,
'model': model,
'model_path': model_path,
}
# process the frames to generate annotations and image information
img_id, ann_id = record_ann(model_meta, img_id, ann_id, images, annotations, render_mask=render_mask)
return img_id, ann_id
def get_vrep_data(camera_name_list, create_sub_dataset=True, render_mask=True):
"""
Generate data for VREP
:param camera_name_list: list of camera names to use.
set to ['ALL'] if you want to use all camera
:param create_sub_dataset: whether or not to make data obtained from each camera
as subdirectory
:param render_mask: whether or not to render mask with pyrender (dsiable it if pygl is not valid)
"""
_data_root = 'data'
img_id = 0
ann_id = 0
images = []
annotations = []
# sequences = [v for v in os.listdir(_data_root) if v.startswith('vrep_franka_link')]
sequences = ['vrep_franka_val']
for sequence in sequences:
data_root = os.path.join(_data_root, sequence)
img_id, ann_id = _get_vrep_data(data_root, img_id, ann_id, images, annotations,
camera_name_list, render_mask=render_mask)
# Sort frames from different sensor to separate sub-dataset (by sensor)
if create_sub_dataset:
im_anno_dict = dict()
for anno_idx, anno in enumerate(annotations):
im_anno_dict[anno['image_id']] = anno_idx
data_folder = os.path.join(data_root, 'subset_')
for cam_name in cam_name_list:
data_folder += '_' + cam_name
if os.path.isdir(data_folder):
os.system('rm -r {}'.format(data_folder))
os.system('mkdir -p {}'.format(data_folder))
if os.path.isfile(os.path.join(data_root, 'model.ply')):
os.system('cp {} {}'.format(os.path.join(data_root, 'model.ply'), os.path.join(data_folder, 'model.ply')))
init_checked = False
for im_anno in images:
rgb_path = im_anno['file_name']
im_id = im_anno['id']
mask_path = annotations[im_anno_dict[im_id]]['mask_path']
rgb_out_path = rgb_path.replace(data_root, data_folder)
mask_out_path = mask_path.replace(data_root, data_folder)
# create folder
if not init_checked:
for data_output_path in [rgb_out_path, mask_out_path]:
if os.path.isdir(os.path.dirname(data_output_path)):
os.system('rm -r {}'.format(os.path.dirname(data_output_path)))
os.system('mkdir -p {}'.format(os.path.dirname(data_output_path)))
init_checked = True
os.system('cp {} {}'.format(rgb_path, rgb_out_path))
os.system('cp {} {}'.format(mask_path, mask_out_path))
np.savetxt(os.path.join(data_folder, 'fps.txt'), kpt3d)
anno_path = os.path.join(data_folder, 'train.json')
categories = [{'supercategory': 'none', 'id': 1, 'name': 'ur'}]
instance = {'images': images, 'annotations': annotations, 'categories': categories}
with open(anno_path, 'w') as f:
json.dump(instance, f, indent=4)
else:
anno_path = os.path.join(data_root, 'train.json')
np.savetxt(os.path.join(data_root, 'fps.txt'), kpt3d)
categories = [{'supercategory': 'none', 'id': 1, 'name': 'ur'}]
instance = {'images': images, 'annotations': annotations, 'categories': categories}
with open(anno_path, 'w') as f:
json.dump(instance, f, indent=4)
def result_analysis(output_path):
f = open(output_path, 'r')
lines = f.readlines()
f.close()
init = False
line_id = 0
train_dict = {
'vote_loss':[],
'seg_loss':[],
'loss': []
}
val_dict = {
'epoch':[],
'vote_loss':[],
'seg_loss': [],
'loss': [],
'add': [],
'2dproj': [],
'mask_ap': [],
}
while(True):
line = lines[line_id]
if 'epoch' in line and not init:
init = True
if init:
if 'epoch' in line:
data = line.split(' ')
take_data = False
for id, item in enumerate(data):
if 'vote_loss' in item:
train_dict['vote_loss'].append(float(data[id+1]))
elif 'seg_loss' in item:
train_dict['seg_loss'].append(float(data[id+1]))
elif 'loss' in item:
train_dict['loss'].append(float(data[id+1]))
else:
curr_epoch = len(train_dict['loss']) - 1
val_dict['epoch'].append(curr_epoch)
data = line.split('\'')
for id, item in enumerate(data):
if 'vote_loss' in item:
loss_val = float(item.split(':')[-1])
val_dict['vote_loss'].append(loss_val)
elif 'seg_loss' in item:
loss_val = float(item.split(':')[-1])
val_dict['seg_loss'].append(loss_val)
elif 'loss' in item:
loss_val = float(item.split(':')[-1])
val_dict['loss'].append(loss_val)
for i in range(1, 5):
line = lines[line_id + i]
if i == 1:
val_dict['2dproj'].append(float(line.split(':')[1]))
elif i == 2:
val_dict['add'].append(float(line.split(':')[1]))
elif i == 4:
val_dict['mask_ap'].append(float(line.split(':')[1]))
line_id += i
line_id += 1
if line_id >= len(lines):
break
# for train: train loss vs. val loss
# for val: change of metrics
# plot result
from matplotlib import pyplot as plt
save_dir = 'data/train_img.pdf'
SMALL_SIZE = 7
BIGGER_SIZE = 10
plt.rc('font', size=BIGGER_SIZE) # controls default text sizes
plt.rc('axes', titlesize=SMALL_SIZE) # fontsize of the axes title
plt.rc('axes', labelsize=BIGGER_SIZE) # fontsize of the x and y labels
plt.rc('xtick', labelsize=BIGGER_SIZE) # fontsize of the tick labels
plt.rc('ytick', labelsize=BIGGER_SIZE) # fontsize of the tick labels
plt.rc('legend', fontsize=BIGGER_SIZE) # legend fontsize
plt.rc('figure', titlesize=BIGGER_SIZE) # fontsize of the figure title
# plt.plot(time_rcnn_baseline, ap_baseline, color='green', marker='o', linestyle='dashed', linewidth=2, markersize=12, label='rcnn_baseline')
# style_label = 'fivethirtyeight'
style_label = 'seaborn-dark'
style_label = 'seaborn-colorblind'
with plt.style.context(style_label):
# if True:
# plt.figure(figsize=(12, 6))
# plt.xlim(100, 45)
plt.ylim(0, 0.1)
# TODO: adjust here
train_epoch = list(range(len(train_dict['vote_loss'])))
plt.plot(train_epoch, train_dict['vote_loss'], color='lightskyblue', linestyle='dashed', linewidth=2,
label='heatmap_loss(train)')
plt.plot(train_epoch, train_dict['seg_loss'], color='turquoise', linestyle='dashed', linewidth=1,
label='seg_loss(train)')
plt.plot(val_dict['epoch'], val_dict['vote_loss'], color='tomato', linestyle='dashed', linewidth=2,
label='heatmap_loss(val)')
plt.plot(val_dict['epoch'], val_dict['seg_loss'], color='sandybrown', linestyle='dashed', linewidth=1,
label='seg_loss(val)')
# plt.plot(time_overall_baseline, ap_baseline_hard, color='darkviolet', marker='x', linestyle='dashed', linewidth=2, markersize=12, label='PRCNN hard')
# plt.plot(time_overall_ours, ap_ours_hard, color='darkslateblue', marker='x', linestyle='dashed', linewidth=2, markersize=12, label='ours hard')
# plt.legend(prop={'size': 12})
plt.legend()
plt.xlabel('Number of Epoch')
plt.ylabel('Loss Value')
plt.axis('on')
plt.grid(b=True)
plt.rc('axes', titlesize=30)
plt.tight_layout()
plt.savefig(save_dir)
plt.show()
save_dir = 'data/eval_metrics.pdf'
SMALL_SIZE = 7
BIGGER_SIZE = 10
plt.rc('font', size=BIGGER_SIZE) # controls default text sizes
plt.rc('axes', titlesize=SMALL_SIZE) # fontsize of the axes title
plt.rc('axes', labelsize=BIGGER_SIZE) # fontsize of the x and y labels
plt.rc('xtick', labelsize=BIGGER_SIZE) # fontsize of the tick labels
plt.rc('ytick', labelsize=BIGGER_SIZE) # fontsize of the tick labels
plt.rc('legend', fontsize=BIGGER_SIZE) # legend fontsize
plt.rc('figure', titlesize=BIGGER_SIZE) # fontsize of the figure title
# plt.plot(time_rcnn_baseline, ap_baseline, color='green', marker='o', linestyle='dashed', linewidth=2, markersize=12, label='rcnn_baseline')
# style_label = 'fivethirtyeight'
style_label = 'seaborn-dark'
style_label = 'seaborn-colorblind'
with plt.style.context(style_label):
# if True:
# plt.figure(figsize=(12, 6))
# plt.xlim(100, 45)
# plt.ylim(0, 0.1)
# TODO: adjust here
plt.plot(val_dict['epoch'], val_dict['add'], color='lightskyblue', linestyle='dashed', linewidth=1.5,
label='ADD')
plt.plot(val_dict['epoch'], val_dict['2dproj'], color='turquoise', linestyle='dashed', linewidth=1.5,
label='2D Proj')
# plt.plot(val_dict['epoch'], val_dict['mask_ap'], color='darkslateblue', linestyle='dashed', linewidth=1.5,
# label='Mask AP')
plt.legend()
plt.xlabel('Number of Epoch')
plt.ylabel('Precision')
plt.axis('on')
plt.grid(b=True)
plt.rc('axes', titlesize=30)
plt.tight_layout()
plt.savefig(save_dir)
plt.show()
import cv2
import os
import numpy as np
click_record = []
IM_DICT = {
1: (380, 460),
2: (1160, 1180),
3: (1390, 1440),
4: (1960, 2020),
5: (2410, 2450),
6: (2780, 2900),
7: (3200, 3220),
8: (3540, 3580),
9: (3910, 3940),
10: (4260, 4280),
11: (4610, 4630),
12: (4840, 4910)
}
MODEL_DICT = {
1: 3,
2: 9,
3: 5,
4: -5,
5: -6.1,
6: 8,
7: -4.1,
8: 9.1,
9: 8.1,
10: 5.1,
11: 4.1,
12: 2.1
}
IM_RANGE = ([640, 108], [1279, 587])
vis_iter = 0
def output_video(im_list, im_shape):
output_path = "./out.mp4"
height, width, layers = im_shape
out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*'mp4v'), 10, (width, height))
from tqdm import tqdm
for i in tqdm(range(len(im_list))):
# if i % 100 == 0:
# print(f"Writting:{i}")
out.write(im_list[i])
out.release()
def dict_to_list():
fid_pairs = IM_DICT.values()
fid_list = []
for val in fid_pairs:
# fid_list.append(int(val[0]))
fid_list.append(int(val[1]))
return fid_list
def dict_to_range():
fid_pairs = IM_DICT.values()
fid_list = []
for val in fid_pairs:
fid_list.append(val)
return fid_list
def in_range(range_dict, fid):
for fid_range in range_dict:
if fid >= fid_range[0] and fid <= fid_range[1]:
return True
return False
def mouse_callback(event, x, y, flags, param):
global click_record
if event == cv2.EVENT_LBUTTONDOWN:
# print(f"[{x}, {y}]")
click_record.append([x, y])
click_rc = np.array(click_record)
if len(click_rc) > 1:
print(f"Shape: {click_rc[-1] - click_rc[-2]}")
def load_process():
video_path = "input.mkv"
cap = cv2.VideoCapture(video_path)
frame_id = 0
im_shape = None
im_list = []
id_list = dict_to_list()
range_dict = dict_to_range()
output_dir = './out_imgs'
if not os.path.isdir(output_dir):
os.makedirs(output_dir)
vis_iter = 0
while(cap.isOpened()):
if frame_id % 50 == 0:
print(f"Processing frame:{frame_id}")
frame_id += 1
ret, img = cap.read()
if ret == False or img is None:
break
if frame_id not in id_list:
continue
# if frame_id % 10 != 0:
# continue
text = "ID:" + str(frame_id)
im_y0 = 108
im_y1 = 587
im_x0 = 640
im_x1 = 1279
img_crop, catch_success = detector(img[im_y0:im_y1, im_x0:im_x1,:])
vis_path = os.path.join(output_dir, '{}.png'.format(vis_iter))
cv2.imwrite(vis_path, img_crop)
vis_iter += 1
img[im_y0:im_y1, im_x0:im_x1, :] = img_crop
im_shape = img.shape
im_list.append(img)
# output_video(im_list, im_shape)
def detector(img):
# RPN stage: Get region proposal with pre-defined parameters
x_margin = 10
y_margin = 10
x_size = 200
y_size = 200
height, width, layers = img.shape
x0, y0, x1, y1 = bbox_from_shape(width, height)
# Draw the original red box
box_pts = [
[(x0, y0), (x1, y0)],
[(x1, y0), (x1, y1)],
[(x1, y1), (x0, y1)],
[(x0, y1), (x0, y0)]
]
for pts in box_pts:
pt1, pt2 = pts
cv2.line(img, pt1, pt2, (0, 0, 255), thickness=1)
x0 = x0 + x_margin
x1 = x0 + x_size
y0 = y0 + y_margin
y1 = y0 + y_size
# Crop RoI with region proposals, apply canny's edge detector
im_crop = img[y0:y1, x0:x1, :]
gaus = cv2.GaussianBlur(im_crop, (3, 3), 0)
gray = cv2.cvtColor(gaus, cv2.COLOR_BGR2GRAY)
gradx = cv2.Sobel(gray, cv2.CV_16SC1, 1, 0)
grady = cv2.Sobel(gray, cv2.CV_16SC1, 0, 1)
edge_out = cv2.Canny(gradx, grady, 50, 150)
# RCNN stage: refine bounding box, produce objectness socre
bbox = detection_head(edge_out)
# Draw overlaid bounding box
if bbox is not None:
box_pts = [
[(x0, y0), (x1, y0)],
[(x1, y0), (x1, y1)],
[(x1, y1), (x0, y1)],
[(x0, y1), (x0, y0)]
]
# for pts in box_pts:
# pt1, pt2 = pts
# cv2.line(img, pt1, pt2, (255, 0, 0), thickness=2)
x_offset = x0
y_offset = y0
x0, y0, x1, y1 = bbox
x0 = x0 + x_offset
x1 = x1 + x_offset
y0 = y0 + y_offset
y1 = y1 + y_offset
box_pts = [
[(x0, y0), (x1, y0)],
[(x1, y0), (x1, y1)],
[(x1, y1), (x0, y1)],
[(x0, y1), (x0, y0)]
]
for pts in box_pts:
pt1, pt2 = pts
cv2.line(img, pt1, pt2, (0, 255, 0), thickness=2)
return img, True
else:
box_pts = [
[(x0, y0), (x1, y0)],
[(x1, y0), (x1, y1)],
[(x1, y1), (x0, y1)],
[(x0, y1), (x0, y0)]
]
for pts in box_pts:
pt1, pt2 = pts
cv2.line(img, pt1, pt2, (0, 255, 0), thickness=2)
return img, False
def procecss():
im_list = os.listdir('data/vis')
im_list = [i for i in im_list if '.png' in i]
im_list.sort()
img_list = []
from tqdm import tqdm
for im_id in range(len(im_list)):
im_path = str(im_id) + '.png'
im_path = os.path.join('data/vis', im_path)
img = cv2.imread(im_path)
# if im_id % 50 == 0:
if True:
print(f"Processing frame:{im_id}")
img = img[61:423, 85:570, :]
# cv2.imshow("Annotate", img)
# key = cv2.waitKey(0)
img_list.append(img)
im_shape = img.shape
out_path = os.path.join('data/vis_clean', str(im_path))
cv2.imwrite(out_path, img)
print(f"processing num images:{len(img_list)}")
output_video(img_list, im_shape)
if __name__ == "__main__":
cam_name_list = ['ALL']
get_vrep_data(cam_name_list, create_sub_dataset=False, render_mask=True)