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# -----------------------------------------------------
# Copyright (c) Shanghai Jiao Tong University. All rights reserved.
# Written by Jiefeng Li ([email protected])
# -----------------------------------------------------
from utils.img import (load_image, drawGaussian, cropBox, transformBox, flip, shuffleLR, cv_rotate)
import torch
import numpy as np
import random
from opt import opt
def rnd(x):
return max(-2 * x, min(2 * x, np.random.randn(1)[0] * x))
def generateSampleBox(img_path, bndbox, part, nJoints, imgset, scale_factor, dataset, train=True, nJoints_coco=17):
img = load_image(img_path)
if train:
img[0].mul_(random.uniform(0.7, 1.3)).clamp_(0, 1)
img[1].mul_(random.uniform(0.7, 1.3)).clamp_(0, 1)
img[2].mul_(random.uniform(0.7, 1.3)).clamp_(0, 1)
img[0].add_(-0.406)
img[1].add_(-0.457)
img[2].add_(-0.480)
upLeft = torch.Tensor((int(bndbox[0][0]), int(bndbox[0][1])))
bottomRight = torch.Tensor((int(bndbox[0][2]), int(bndbox[0][3])))
ht = bottomRight[1] - upLeft[1]
width = bottomRight[0] - upLeft[0]
imght = img.shape[1]
imgwidth = img.shape[2]
scaleRate = random.uniform(*scale_factor)
upLeft[0] = max(0, upLeft[0] - width * scaleRate / 2)
upLeft[1] = max(0, upLeft[1] - ht * scaleRate / 2)
bottomRight[0] = min(imgwidth - 1, bottomRight[0] + width * scaleRate / 2)
bottomRight[1] = min(imght - 1, bottomRight[1] + ht * scaleRate / 2)
# Doing Random Sample
if opt.addDPG:
PatchScale = random.uniform(0, 1)
if PatchScale > 0.85:
ratio = ht / width
if (width < ht):
patchWidth = PatchScale * width
patchHt = patchWidth * ratio
else:
patchHt = PatchScale * ht
patchWidth = patchHt / ratio
xmin = upLeft[0] + random.uniform(0, 1) * (width - patchWidth)
ymin = upLeft[1] + random.uniform(0, 1) * (ht - patchHt)
xmax = xmin + patchWidth + 1
ymax = ymin + patchHt + 1
else:
xmin = max(
1, min(upLeft[0] + np.random.normal(-0.0142, 0.1158) * width, imgwidth - 3))
ymin = max(
1, min(upLeft[1] + np.random.normal(0.0043, 0.068) * ht, imght - 3))
xmax = min(max(
xmin + 2, bottomRight[0] + np.random.normal(0.0154, 0.1337) * width), imgwidth - 3)
ymax = min(
max(ymin + 2, bottomRight[1] + np.random.normal(-0.0013, 0.0711) * ht), imght - 3)
upLeft[0] = xmin
upLeft[1] = ymin
bottomRight[0] = xmax
bottomRight[1] = ymax
# Counting Joints number
jointNum = 0
if imgset == 'coco':
for i in range(17):
if part[i][0] > 0 and part[i][0] > upLeft[0] and part[i][1] > upLeft[1] \
and part[i][0] < bottomRight[0] and part[i][1] < bottomRight[1]:
jointNum += 1
# Doing Random Crop
if opt.addDPG:
if jointNum > 13 and train:
switch = random.uniform(0, 1)
if switch > 0.96:
bottomRight[0] = (upLeft[0] + bottomRight[0]) / 2
bottomRight[1] = (upLeft[1] + bottomRight[1]) / 2
elif switch > 0.92:
upLeft[0] = (upLeft[0] + bottomRight[0]) / 2
bottomRight[1] = (upLeft[1] + bottomRight[1]) / 2
elif switch > 0.88:
upLeft[1] = (upLeft[1] + bottomRight[1]) / 2
bottomRight[0] = (upLeft[0] + bottomRight[0]) / 2
elif switch > 0.84:
upLeft[0] = (upLeft[0] + bottomRight[0]) / 2
upLeft[1] = (upLeft[1] + bottomRight[1]) / 2
elif switch > 0.80:
bottomRight[0] = (upLeft[0] + bottomRight[0]) / 2
elif switch > 0.76:
upLeft[0] = (upLeft[0] + bottomRight[0]) / 2
elif switch > 0.72:
bottomRight[1] = (upLeft[1] + bottomRight[1]) / 2
elif switch > 0.68:
upLeft[1] = (upLeft[1] + bottomRight[1]) / 2
inputResH, inputResW = opt.inputResH, opt.inputResW
outputResH, outputResW = opt.outputResH, opt.outputResW
inp = cropBox(img, upLeft, bottomRight, inputResH, inputResW)
if jointNum == 0:
inp = torch.zeros(3, inputResH, inputResW)
out = torch.zeros(nJoints, outputResH, outputResW)
setMask = torch.zeros(nJoints, outputResH, outputResW)
# Draw Label
if imgset == 'coco':
for i in range(nJoints_coco):
if part[i][0] > 0 and part[i][0] > upLeft[0] and part[i][1] > upLeft[1] \
and part[i][0] < bottomRight[0] and part[i][1] < bottomRight[1]:
hm_part = transformBox(
part[i], upLeft, bottomRight, inputResH, inputResW, outputResH, outputResW)
out[i] = drawGaussian(out[i], hm_part, opt.hmGauss)
setMask[i].add_(1)
if train:
# Flip
if random.uniform(0, 1) < 0.5:
inp = flip(inp)
out = shuffleLR(flip(out), dataset)
# Rotate
r = rnd(opt.rotate)
if random.uniform(0, 1) < 0.6:
r = 0
if r != 0:
inp = cv_rotate(inp, r, opt.inputResW, opt.inputResH)
out = cv_rotate(out, r, opt.outputResW, opt.outputResH)
return inp, out, setMask
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