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dataset.py
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dataset.py
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#!/usr/bin/env python
# coding=utf-8
"""
All image shape is H x W type
"""
from __future__ import print_function
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
import pickle
import cv2
import os
class Config(object):
def __init__(self):
self.image_shape = None
self.output_shape = None
self.num_joints = None
config = Config()
config.image_shape = (256, 256) # H x W
config.output_shape = (64, 64) # H x W
config.output_depth = 64
config.num_joints = 14
def get_img(dataset_dir, index):
img_dir = dataset_dir + 'images/'
img_path = img_dir + str(index).zfill(5) + '.jpg'
img = plt.imread(img_path)
return img
def load_index_label(dataset_dir, index):
""" load index-th label from saved pickle file
@Returns:
joints: (3, N_nodes)
ordinal: (N_nodes, N_nodes)
"""
pkl_list = pickle.load(open(dataset_dir + 'label_mpii_lsp.pkl', 'rb'))
pkl = pkl_list[index]
ind = pkl['index']
assert ind == str(index).zfill(5)
joints = pkl['joints']
ordinal = pkl['ordinal']
return joints, ordinal
def get_label(pkl_list, index):
""" load index-th label from list of dict
@Returns:
joints: (3, N_nodes)
ordinal: (N_nodes, N_nodes)
"""
pkl = pkl_list[index]
ind = pkl['index']
assert ind == str(index).zfill(5)
joints = pkl['joints']
ordinal = pkl['ordinal']
return joints, ordinal
def warpAffine_sample(img, joints, image_shape=config.image_shape, dr=80):
""" crop and warp image
@Args:
img: 3-channel image
joints: (3, N_nodes)
config.image_shape: parameters from config, expected training image shape
"""
min_jx = np.min(joints[0, :])
max_jx = np.max(joints[0, :])
min_jy = np.min(joints[1, :])
max_jy = np.max(joints[1, :])
c_x = (min_jx + max_jx) / 2.0
c_y = (min_jy + max_jy) / 2.0
r = np.maximum(max_jx - min_jx, max_jy - min_jy) / 2.0
r = int(r) + dr
min_x = int(c_x) - r
max_x = int(c_x) + r
min_y = int(c_y) - r
max_y = int(c_y) + r
pts1 = np.float32([[min_x, min_y], [max_x, min_y], [min_x, max_y]])
pts2 = np.float32([[0, 0], [image_shape[1], 0], [0, image_shape[0]]]) # ! shape[0]:h
mat = cv2.getAffineTransform(pts1, pts2)
img_warped = cv2.warpAffine(img, mat, (image_shape[1], image_shape[0]))
# !!! cv2.warpAffine : 3rd para: (row, col), but img_warped.shape = (col, row, 3)
joints_warped = joints.copy()
pts = joints.copy()
pts[-1, :] = 1
pts = np.dot(mat, pts).astype(np.int32)
joints_warped[:2, :] = pts
return img_warped, joints_warped
def gen_joints_heatmap(joints, ori_size=config.image_shape, tar_size=config.output_shape, num_joints=config.num_joints):
""" generate GT heatmap label for joints
@Args:
joints: (3, num_joints), x/y/visible, x/y is in ori_size coordinate
ori_size: (H, W)
tar_size: (H, W)
num_joints: int
@Returns:
ret: gaussian blured heatmap (num_joints, tar_size[0], tar_size[1])
"""
ret = np.zeros( (num_joints, tar_size[0], tar_size[1]), dtype='float32' ) # D x H x W
# scale joints x/y to [0, 1]
label_x = joints[0] / ori_size[1]
label_y = joints[1] / ori_size[0]
for j in range(num_joints) :
if label_x[j] < 0 or label_y[j] < 0 or label_x[j] > 0.999 or label_y[j] > 0.999:
continue
ret[j][ int(label_y[j] * tar_size[0]) ][ int(label_x[j] * tar_size[1]) ] = 1
ret = np.transpose( ret, (1, 2, 0) )
ret = cv2.GaussianBlur( ret, (7, 7), 0 ) # the image can have any number of channels
ret = np.transpose( ret, (2, 0, 1) )
for j in range(num_joints) :
am = np.amax( ret[j] )
if am == 0 :
continue
ret[j] /= am
return ret
def visuaize_skeleton(joints):
"""
@Args:
joints: 2 x 14
"""
r_skeleton = [[0, 1], [1, 2], [6, 7], [7, 8]]
l_skeleton = [[3, 4], [4, 5], [9, 10], [10, 11]]
for [i, j] in r_skeleton:
plt.plot([joints[0, i], joints[0, j]], [joints[1, i], joints[1, j]], 'g')
for [i, j] in l_skeleton:
plt.plot([joints[0, i], joints[0, j]], [joints[1, i], joints[1, j]], 'b')
plt.plot([joints[0, 12], joints[0, 13]], [joints[1, 12], joints[1, 13]], 'y')
pelvis = (joints[:, 2] + joints[:, 3]) / 2.0
plt.plot([joints[0, 12], pelvis[0]], [joints[1, 12], pelvis[1]], 'y')
def visualize_index(dataset_dir, index):
""" visualize index-th image
@Args:
dataset_dir: directory to save images
index: int
"""
joints, ordinal = load_index_label(dataset_dir, index)
img_dir = dataset_dir + 'images/'
img_path = img_dir + str(index).zfill(5) + '.jpg'
img = plt.imread(img_path)
plt.figure()
plt.imshow(img)
joints_inst = joints[:2, :]
joints_visb = joints[2, :]
ordinal_inst = ordinal[:, :]
# attention: MPII & LSP: visible definition diff, here visble = 1
for j in range(len(joints_visb)):
if joints_visb[j] == 1:
plt.scatter(joints_inst[0, j], joints_inst[1, j], marker='.', c='r', s=100)
visuaize_skeleton(joints_inst)
plt.title(index)
plt.show()
def visualize_sample(img, joints, auto_close=False):
""" visualize image and joints pair
@Args:
img: (H, W, 3)
joints: (3, N_nodes)
"""
plt.figure()
plt.subplot(121)
plt.imshow(img)
joints_inst = joints[:2, :]
joints_visb = joints[2, :]
# attention: MPII & LSP: visible definition diff, here visble = 1
# for j in range(len(joints_visb)):
# if joints_visb[j] == 1:
# plt.scatter(joints_inst[0, j], joints_inst[1, j], marker='.', c='r', s=100)
plt.scatter(joints_inst[0, :], joints_inst[1, :], marker='.', c='r', s=100)
visuaize_skeleton(joints_inst)
plt.subplot(122) # plot heatmap
heatmap = gen_joints_heatmap(joints)
heatmap = np.sum(heatmap, axis=0)
plt.imshow(heatmap)
if auto_close:
plt.show(block=False)
plt.pause(0.5)
plt.close()
else:
plt.show()
class DataLoader(object):
def __init__(self, dataset_dir=None, status='train' ,batch_size=32):
self.dataset_dir = dataset_dir
self.batch_size = batch_size
self.status = status
self.img_names = None
self.img_num = None
self.label_list = None
self.dataset = None
self.iter = None
self._build(status)
def _data_process_pyfunc(self, img_name, index):
# !!! in py_func, don't use tf function!
# img = tf.read_file(img_name)
# img_jpg = tf.image.decode_jpeg(img, channels=3)
img = cv2.imread(img_name.decode('utf-8'))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
label = self.label_list[index]
index = label['index']
joints = label['joints']
ordinal = label['ordinal']
img, joints = warpAffine_sample(img, joints)
img = img / 255.0
heatmap = gen_joints_heatmap(joints)
return np.int16(index), img.astype(np.float32), joints.astype(np.float32),\
heatmap.astype(np.float32), ordinal.astype(np.float32)
# train : return mini-batch
# test : return single component
def _build(self, status=None):
# build dataset
img_names = os.listdir(self.dataset_dir + 'images/')
img_names.sort() # !!! sort
self.img_names = [self.dataset_dir + 'images/' + fil for fil in img_names]
self.img_num = len(self.img_names)
self.label_list = pickle.load(open(self.dataset_dir + 'label_mpii_lsp.pkl', 'rb'))
label_index_list = list(range(self.img_num))
dataset = tf.data.Dataset.from_tensor_slices((self.img_names, label_index_list))
dataset = dataset.shuffle(buffer_size=self.img_num) # shuffle before heavy transformation !
dataset = dataset.map(lambda img_name, index : tuple(tf.py_func(self._data_process_pyfunc, \
[img_name, index], [tf.int16, tf.float32, tf.float32, tf.float32, tf.float32])))
# judge status
if status == 'train':
self.dataset = dataset.repeat().batch(self.batch_size) # first repeat then batch !!
elif status == 'test':
self.dataset = dataset.repeat()
else:
raise AttributeError("status must be 'train' or 'test' !")
# iter
self.iter = self.dataset.make_one_shot_iterator()
# return next_batch_op
def load_batch(self):
"""
@Returns:
index.shape = (batch_size,)
img.shape = (batch_size, image_shape, image_shape, 3)
joints.shape = (batch_size, 3, num_joints)
heatmap.shape = (batch_size, num_joints, output_shape, output_shape)
ordinal.shape = (batch_size, num_joints, num_joints)
"""
return self.iter.get_next()
if __name__ == '__main__':
dataset_dir = './dataset/'
dataLoader = DataLoader(dataset_dir)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# train_next_batch_op = dataLoader.load_batch()
# index, img, joints, heatmap, ordinal = sess.run(train_next_batch_op)
index_op, img_op, joints_op, heatmap_op, ordinal_op = dataLoader.load_batch()
index, img, joints, heatmap, ordinal = sess.run([index_op, img_op, joints_op, heatmap_op, ordinal_op])
for i in range(img.shape[0]):
print(index[i])
visualize_sample(img[i], joints[i]) #, auto_close=True)
from IPython import embed; embed()
"""
# for i in list(range(10)) + list(range(13029, 13035)):
# visual_index(dataset_dir, i)
pkl_list = pickle.load(open(dataset_dir + 'label_mpii_lsp.pkl', 'rb'))
for i in list(range(10)) + list(range(13029, 13035)):
img = get_img(dataset_dir, i)
joints, _ = get_label(pkl_list, i)
# visualize_sample(img, joints)
img, joints = warpAffine_sample(img, joints)
heatmap = gen_joints_heatmap(joints)
visualize_sample(img, joints) #, auto_close=True)
# from IPython import embed; embed()
"""