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7b53d75 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 | import os
import numpy as np
import cv2
import torch
from torch.utils.data.dataset import Dataset
from torchvision import transforms
from PIL import Image, ImageFilter
class Gaze360(Dataset):
def __init__(self, path, root, transform, angle, binwidth, train=True):
self.transform = transform
self.root = root
self.orig_list_len = 0
self.angle = angle
if train==False:
angle=90
self.binwidth=binwidth
self.lines = []
if isinstance(path, list):
for i in path:
with open(i) as f:
print("here")
line = f.readlines()
line.pop(0)
self.lines.extend(line)
else:
with open(path) as f:
lines = f.readlines()
lines.pop(0)
self.orig_list_len = len(lines)
for line in lines:
gaze2d = line.strip().split(" ")[5]
label = np.array(gaze2d.split(",")).astype("float")
if abs((label[0]*180/np.pi)) <= angle and abs((label[1]*180/np.pi)) <= angle:
self.lines.append(line)
print("{} items removed from dataset that have an angle > {}".format(self.orig_list_len-len(self.lines), angle))
def __len__(self):
return len(self.lines)
def __getitem__(self, idx):
line = self.lines[idx]
line = line.strip().split(" ")
face = line[0]
lefteye = line[1]
righteye = line[2]
name = line[3]
gaze2d = line[5]
label = np.array(gaze2d.split(",")).astype("float")
label = torch.from_numpy(label).type(torch.FloatTensor)
pitch = label[0]* 180 / np.pi
yaw = label[1]* 180 / np.pi
img = Image.open(os.path.join(self.root, face))
# fimg = cv2.imread(os.path.join(self.root, face))
# fimg = cv2.resize(fimg, (448, 448))/255.0
# fimg = fimg.transpose(2, 0, 1)
# img=torch.from_numpy(fimg).type(torch.FloatTensor)
if self.transform:
img = self.transform(img)
# Bin values
bins = np.array(range(-1*self.angle, self.angle, self.binwidth))
binned_pose = np.digitize([pitch, yaw], bins) - 1
labels = binned_pose
cont_labels = torch.FloatTensor([pitch, yaw])
return img, labels, cont_labels, name
class Mpiigaze(Dataset):
def __init__(self, pathorg, root, transform, train, angle,fold=0):
self.transform = transform
self.root = root
self.orig_list_len = 0
self.lines = []
path=pathorg.copy()
if train==True:
path.pop(fold)
else:
path=path[fold]
if isinstance(path, list):
for i in path:
with open(i) as f:
lines = f.readlines()
lines.pop(0)
self.orig_list_len += len(lines)
for line in lines:
gaze2d = line.strip().split(" ")[7]
label = np.array(gaze2d.split(",")).astype("float")
if abs((label[0]*180/np.pi)) <= angle and abs((label[1]*180/np.pi)) <= angle:
self.lines.append(line)
else:
with open(path) as f:
lines = f.readlines()
lines.pop(0)
self.orig_list_len += len(lines)
for line in lines:
gaze2d = line.strip().split(" ")[7]
label = np.array(gaze2d.split(",")).astype("float")
if abs((label[0]*180/np.pi)) <= 42 and abs((label[1]*180/np.pi)) <= 42:
self.lines.append(line)
print("{} items removed from dataset that have an angle > {}".format(self.orig_list_len-len(self.lines),angle))
def __len__(self):
return len(self.lines)
def __getitem__(self, idx):
line = self.lines[idx]
line = line.strip().split(" ")
name = line[3]
gaze2d = line[7]
head2d = line[8]
lefteye = line[1]
righteye = line[2]
face = line[0]
label = np.array(gaze2d.split(",")).astype("float")
label = torch.from_numpy(label).type(torch.FloatTensor)
pitch = label[0]* 180 / np.pi
yaw = label[1]* 180 / np.pi
img = Image.open(os.path.join(self.root, face))
# fimg = cv2.imread(os.path.join(self.root, face))
# fimg = cv2.resize(fimg, (448, 448))/255.0
# fimg = fimg.transpose(2, 0, 1)
# img=torch.from_numpy(fimg).type(torch.FloatTensor)
if self.transform:
img = self.transform(img)
# Bin values
bins = np.array(range(-42, 42,3))
binned_pose = np.digitize([pitch, yaw], bins) - 1
labels = binned_pose
cont_labels = torch.FloatTensor([pitch, yaw])
return img, labels, cont_labels, name
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