/
badjadataset.py
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/
badjadataset.py
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from numpy import random
from numpy.core.numeric import full
import torch
import numpy as np
import os
import scipy.ndimage
import torchvision.transforms as transforms
import torch.nn.functional as F
from PIL import Image
import random
from torch._C import dtype, set_flush_denormal
import glob
import json
import imageio
import cv2
import re
from torchvision.transforms import ColorJitter, GaussianBlur
np.random.seed(125)
torch.multiprocessing.set_sharing_strategy('file_system')
from enum import Enum
IGNORE_ANIMALS = [
# "bear.json",
# "camel.json",
"cat_jump.json"
# "cows.json",
# "dog.json",
# "dog-agility.json",
# "horsejump-high.json",
# "horsejump-low.json",
# "impala0.json",
# "rs_dog.json"
"tiger.json"
]
class SMALJointCatalog(Enum):
# body_0 = 0
# body_1 = 1
# body_2 = 2
# body_3 = 3
# body_4 = 4
# body_5 = 5
# body_6 = 6
# upper_right_0 = 7
upper_right_1 = 8
upper_right_2 = 9
upper_right_3 = 10
# upper_left_0 = 11
upper_left_1 = 12
upper_left_2 = 13
upper_left_3 = 14
neck_lower = 15
# neck_upper = 16
# lower_right_0 = 17
lower_right_1 = 18
lower_right_2 = 19
lower_right_3 = 20
# lower_left_0 = 21
lower_left_1 = 22
lower_left_2 = 23
lower_left_3 = 24
tail_0 = 25
# tail_1 = 26
# tail_2 = 27
tail_3 = 28
# tail_4 = 29
# tail_5 = 30
tail_6 = 31
jaw = 32
nose = 33 # ADDED JOINT FOR VERTEX 1863
# chin = 34 # ADDED JOINT FOR VERTEX 26
right_ear = 35 # ADDED JOINT FOR VERTEX 149
left_ear = 36 # ADDED JOINT FOR VERTEX 2124
class SMALJointInfo():
def __init__(self):
# These are the
self.annotated_classes = np.array([
8, 9, 10, # upper_right
12, 13, 14, # upper_left
15, # neck
18, 19, 20, # lower_right
22, 23, 24, # lower_left
25, 28, 31, # tail
32, 33, # head
35, # right_ear
36]) # left_ear
self.annotated_markers = np.array([
cv2.MARKER_CROSS, cv2.MARKER_STAR, cv2.MARKER_TRIANGLE_DOWN,
cv2.MARKER_CROSS, cv2.MARKER_STAR, cv2.MARKER_TRIANGLE_DOWN,
cv2.MARKER_CROSS,
cv2.MARKER_CROSS, cv2.MARKER_STAR, cv2.MARKER_TRIANGLE_DOWN,
cv2.MARKER_CROSS, cv2.MARKER_STAR, cv2.MARKER_TRIANGLE_DOWN,
cv2.MARKER_CROSS, cv2.MARKER_STAR, cv2.MARKER_TRIANGLE_DOWN,
cv2.MARKER_CROSS, cv2.MARKER_STAR,
cv2.MARKER_CROSS,
cv2.MARKER_CROSS])
self.joint_regions = np.array([
0, 0, 0, 0, 0, 0, 0,
1, 1, 1, 1,
2, 2, 2, 2,
3, 3,
4, 4, 4, 4,
5, 5, 5, 5,
6, 6, 6, 6, 6, 6, 6,
7, 7, 7,
8,
9])
self.annotated_joint_region = self.joint_regions[self.annotated_classes]
self.region_colors = np.array([
[250, 190, 190], # body, light pink
[60, 180, 75], # upper_right, green
[230, 25, 75], # upper_left, red
[128, 0, 0], # neck, maroon
[0, 130, 200], # lower_right, blue
[255, 255, 25], # lower_left, yellow
[240, 50, 230], # tail, majenta
[245, 130, 48], # jaw / nose / chin, orange
[29, 98, 115], # right_ear, turquoise
[255, 153, 204]]) # left_ear, pink
self.joint_colors = np.array(self.region_colors)[self.annotated_joint_region]
class BADJAData():
def __init__(self, data_root, complete=False):
annotations_path = os.path.join(data_root, "joint_annotations")
print('annotations_path', annotations_path)
self.animal_dict = {}
self.animal_count = 0
self.smal_joint_info = SMALJointInfo()
for animal_id, animal_json in enumerate(sorted(os.listdir(annotations_path))):
if animal_json not in IGNORE_ANIMALS:
json_path = os.path.join(annotations_path, animal_json)
with open(json_path) as json_data:
animal_joint_data = json.load(json_data)
filenames = []
segnames = []
joints = []
visible = []
print('number of annotated frames', len(animal_joint_data))
first_path = animal_joint_data[0]['segmentation_path']
last_path = animal_joint_data[-1]['segmentation_path']
first_frame = first_path.split('/')[-1]
last_frame = last_path.split('/')[-1]
if not 'extra_videos' in first_path:
# if 'extra_videos' in first_path:
# animal = first_path.split('/')[1]
# base =
# else:
animal = first_path.split('/')[-2]
# print('animal', animal)
# import ipdb; ipdb.set_trace()
# folder = first_path[:-len(first_frame)]
first_frame_int = int(first_frame.split('.')[0])
last_frame_int = int(last_frame.split('.')[0])
for fr in range(first_frame_int, last_frame_int+1):
ref_file_name = os.path.join(data_root, 'DAVIS/JPEGImages/Full-Resolution/%s/%05d.jpg' % (animal, fr))
ref_seg_name = os.path.join(data_root, 'DAVIS/Annotations/Full-Resolution/%s/%05d.png' % (animal, fr))
# print('ref_file_name', ref_file_name)
foundit = False
for ind, image_annotation in enumerate(animal_joint_data):
file_name = os.path.join(data_root, image_annotation['image_path'])
seg_name = os.path.join(data_root, image_annotation['segmentation_path'])
# print('file_name', file_name)
if file_name == ref_file_name:
foundit = True
label_ind = ind
if foundit:
image_annotation = animal_joint_data[label_ind]
file_name = os.path.join(data_root, image_annotation['image_path'])
seg_name = os.path.join(data_root, image_annotation['segmentation_path'])
joint = np.array(image_annotation['joints'])
vis = np.array(image_annotation['visibility'])
else:
file_name = ref_file_name
seg_name = ref_seg_name
# seg_name = None
joint = None
vis = None
filenames.append(file_name)
segnames.append(seg_name)
joints.append(joint)
visible.append(vis)
# print('filenames', filenames)
# print('segnames', segnames)
if len(filenames):
# print('adding this (original) id:', animal_id)
self.animal_dict[self.animal_count] = (filenames, segnames, joints, visible)
self.animal_count += 1
print ("Loaded BADJA dataset")
def get_loader(self):
for idx in range(int(1e6)):
animal_id = np.random.choice(len(self.animal_dict.keys()))
# print('choosing animal_id', animal_id)
filenames, segnames, joints, visible = self.animal_dict[animal_id]
# print('filenames', filenames)
image_id = np.random.randint(0, len(filenames))
seg_file = segnames[image_id]
image_file = filenames[image_id]
joints = joints[image_id].copy()
joints = joints[self.smal_joint_info.annotated_classes]
visible = visible[image_id][self.smal_joint_info.annotated_classes]
rgb_img = imageio.imread(image_file)#, mode='RGB')
sil_img = imageio.imread(seg_file)#, mode='RGB')
rgb_h, rgb_w, _ = rgb_img.shape
sil_img = cv2.resize(sil_img, (rgb_w, rgb_h), cv2.INTER_NEAREST)
yield rgb_img, sil_img, joints, visible, image_file
def get_video(self, animal_id):
# print('choosing animal_id', animal_id)
filenames, segnames, joint, visible = self.animal_dict[animal_id]
# print('filenames', filenames)
rgbs = []
segs = []
joints = []
visibles = []
for s in range(len(filenames)):
image_file = filenames[s]
rgb_img = imageio.imread(image_file)#, mode='RGB')
rgb_h, rgb_w, _ = rgb_img.shape
seg_file = segnames[s]
sil_img = imageio.imread(seg_file)#, mode='RGB')
sil_img = cv2.resize(sil_img, (rgb_w, rgb_h), cv2.INTER_NEAREST)
jo = joint[s]
# print('image_file', image_file)
# print('seg_file', seg_file)
if jo is not None:
joi = joint[s].copy()
joi = joi[self.smal_joint_info.annotated_classes]
vis = visible[s][self.smal_joint_info.annotated_classes]
else:
joi = None
vis = None
rgbs.append(rgb_img)
segs.append(sil_img)
joints.append(joi)
visibles.append(vis)
return rgbs, segs, joints, visibles, filenames[0]
class BadjaDataset(torch.utils.data.Dataset):
def __init__(self, data_root='../badja'):
self.data_root = data_root
self.badja_data = BADJAData(data_root)
print('found %d unique videos in %s' % (self.badja_data.animal_count, self.data_root))
def __getitem__(self, index):
rgbs, segs, joints, visibles, file0 = self.badja_data.get_video(index)
S = len(rgbs)
H, W, C = rgbs[0].shape
H, W, mystery = segs[0].shape
# print('joints[0]', joints[0].shape)
N, D = joints[0].shape
# let's eliminate the Nones
# note the first one is guaranteed present
for s in range(1,S):
if joints[s] is None:
# segs[s] = np.zeros_like(segs[0])
joints[s] = np.zeros_like(joints[0])
visibles[s] = np.zeros_like(visibles[0])
# eliminate the mystery dim
segs = [seg[:,:,0] for seg in segs]
# print('rgb', rgbs[0].shape)
# print('seg', segs[0].shape)
rgbs = np.stack(rgbs, 0)
segs = np.stack(segs, 0)
trajs = np.stack(joints, 0)
visibles = np.stack(visibles, 0)
# print('rgbs', rgbs.shape)
# print('segs', segs.shape)
# print('trajs', trajs.shape)
# print('visibles', visibles.shape)
rgbs = torch.from_numpy(rgbs).reshape(S, H, W, 3).permute(0,3,1,2)
segs = torch.from_numpy(segs).reshape(S, 1, H, W)
trajs = torch.from_numpy(trajs).reshape(S, N, 2)
visibles = torch.from_numpy(visibles).reshape(S, N)
# apparently the coords are in yx order
trajs = torch.flip(trajs, [2])
# trajs = torch.cat([trajs[:,:,1],trajs[:,:,0]], dim=2)
# invs = torch.from_numpy(np.stack(invs, 0)).reshape(S, H, W, 1).permute(0,3,1,2)
# flows = torch.from_numpy(np.stack(flows, 0)).reshape(S, H, W, 2).permute(0,3,1,2)
return_dict = {
'file0': file0,
'rgbs': rgbs,
'segs': segs,
'trajs': trajs,
'visibles': visibles,
}
return return_dict
def __len__(self):
# return 10
# return len(self.rgb_paths)
return self.badja_data.animal_count