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coco_data_loader.py
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coco_data_loader.py
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import os
import sys
import cv2
import math
import random
import numpy as np
from chainer.dataset import DatasetMixin
from pycocotools.coco import COCO
from entity import JointType, params
class CocoDataLoader(DatasetMixin):
def __init__(self, coco, insize, mode='train', n_samples=None):
self.coco = coco
assert mode in ['train', 'val', 'eval'], 'Data loading mode is invalid.'
self.mode = mode
self.catIds = coco.getCatIds(catNms=['person'])
self.imgIds = sorted(coco.getImgIds(catIds=self.catIds))
if self.mode in ['val', 'eval'] and n_samples is not None:
self.imgIds = random.sample(self.imgIds, n_samples)
print('{} images: {}'.format(mode, len(self)))
self.insize = insize
def __len__(self):
return len(self.imgIds)
def overlay_paf(self, img, paf):
hue = ((np.arctan2(paf[1], paf[0]) / np.pi) / -2 + 0.5)
saturation = np.sqrt(paf[0] ** 2 + paf[1] ** 2)
saturation[saturation > 1.0] = 1.0
value = saturation.copy()
hsv_paf = np.vstack((hue[np.newaxis], saturation[np.newaxis], value[np.newaxis])).transpose(1, 2, 0)
rgb_paf = cv2.cvtColor((hsv_paf * 255).astype(np.uint8), cv2.COLOR_HSV2BGR)
img = cv2.addWeighted(img, 0.6, rgb_paf, 0.4, 0)
return img
def overlay_pafs(self, img, pafs):
mix_paf = np.zeros((2,) + img.shape[:-1])
paf_flags = np.zeros(mix_paf.shape) # for constant paf
for paf in pafs.reshape((int(pafs.shape[0]/2), 2,) + pafs.shape[1:]):
paf_flags = paf != 0
paf_flags += np.broadcast_to(paf_flags[0] | paf_flags[1], paf.shape)
mix_paf += paf
mix_paf[paf_flags > 0] /= paf_flags[paf_flags > 0]
img = self.overlay_paf(img, mix_paf)
return img
def overlay_heatmap(self, img, heatmap):
rgb_heatmap = cv2.applyColorMap((heatmap * 255).astype(np.uint8), cv2.COLORMAP_JET)
img = cv2.addWeighted(img, 0.6, rgb_heatmap, 0.4, 0)
return img
def overlay_ignore_mask(self, img, ignore_mask):
img = img * np.repeat((ignore_mask == 0).astype(np.uint8)[:, :, None], 3, axis=2)
return img
def get_pose_bboxes(self, poses):
pose_bboxes = []
for pose in poses:
x1 = pose[pose[:, 2] > 0][:, 0].min()
y1 = pose[pose[:, 2] > 0][:, 1].min()
x2 = pose[pose[:, 2] > 0][:, 0].max()
y2 = pose[pose[:, 2] > 0][:, 1].max()
pose_bboxes.append([x1, y1, x2, y2])
pose_bboxes = np.array(pose_bboxes)
return pose_bboxes
def resize_data(self, img, ignore_mask, poses, shape):
"""resize img, mask and annotations"""
img_h, img_w, _ = img.shape
resized_img = cv2.resize(img, shape)
ignore_mask = cv2.resize(ignore_mask.astype(np.uint8), shape).astype('bool')
poses[:, :, :2] = (poses[:, :, :2] * np.array(shape) / np.array((img_w, img_h)))
return resized_img, ignore_mask, poses
def random_resize_img(self, img, ignore_mask, poses):
h, w, _ = img.shape
joint_bboxes = self.get_pose_bboxes(poses)
bbox_sizes = ((joint_bboxes[:, 2:] - joint_bboxes[:, :2] + 1)**2).sum(axis=1)**0.5
min_scale = params['min_box_size']/bbox_sizes.min()
max_scale = params['max_box_size']/bbox_sizes.max()
# print(len(bbox_sizes))
# print('min: {}, max: {}'.format(min_scale, max_scale))
min_scale = min(max(min_scale, params['min_scale']), 1)
max_scale = min(max(max_scale, 1), params['max_scale'])
# print('min: {}, max: {}'.format(min_scale, max_scale))
scale = float((max_scale - min_scale) * random.random() + min_scale)
shape = (round(w * scale), round(h * scale))
# print(scale)
resized_img, resized_mask, resized_poses = self.resize_data(img, ignore_mask, poses, shape)
return resized_img, resized_mask, poses
def random_rotate_img(self, img, mask, poses):
h, w, _ = img.shape
# degree = (random.random() - 0.5) * 2 * params['max_rotate_degree']
degree = np.random.randn() / 3 * params['max_rotate_degree']
rad = degree * math.pi / 180
center = (w / 2, h / 2)
R = cv2.getRotationMatrix2D(center, degree, 1)
bbox = (w*abs(math.cos(rad)) + h*abs(math.sin(rad)), w*abs(math.sin(rad)) + h*abs(math.cos(rad)))
R[0, 2] += bbox[0] / 2 - center[0]
R[1, 2] += bbox[1] / 2 - center[1]
rotate_img = cv2.warpAffine(img, R, (int(bbox[0]+0.5), int(bbox[1]+0.5)), flags=cv2.INTER_CUBIC,
borderMode=cv2.BORDER_CONSTANT, borderValue=[127.5, 127.5, 127.5])
rotate_mask = cv2.warpAffine(mask.astype('uint8')*255, R, (int(bbox[0]+0.5), int(bbox[1]+0.5))) > 0
tmp_poses = np.ones_like(poses)
tmp_poses[:, :, :2] = poses[:, :, :2].copy()
tmp_rotate_poses = np.dot(tmp_poses, R.T) # apply rotation matrix to the poses
rotate_poses = poses.copy() # to keep visibility flag
rotate_poses[:, :, :2] = tmp_rotate_poses
return rotate_img, rotate_mask, rotate_poses
def random_crop_img(self, img, ignore_mask, poses):
h, w, _ = img.shape
insize = self.insize
joint_bboxes = self.get_pose_bboxes(poses)
bbox = random.choice(joint_bboxes) # select a bbox randomly
bbox_center = bbox[:2] + (bbox[2:] - bbox[:2])/2
r_xy = np.random.rand(2)
perturb = ((r_xy - 0.5) * 2 * params['center_perterb_max'])
center = (bbox_center + perturb + 0.5).astype('i')
crop_img = np.zeros((insize, insize, 3), 'uint8') + 127.5
crop_mask = np.zeros((insize, insize), 'bool')
offset = (center - (insize-1)/2 + 0.5).astype('i')
offset_ = (center + (insize-1)/2 - (w-1, h-1) + 0.5).astype('i')
x1, y1 = (center - (insize-1)/2 + 0.5).astype('i')
x2, y2 = (center + (insize-1)/2 + 0.5).astype('i')
x1 = max(x1, 0)
y1 = max(y1, 0)
x2 = min(x2, w-1)
y2 = min(y2, h-1)
x_from = -offset[0] if offset[0] < 0 else 0
y_from = -offset[1] if offset[1] < 0 else 0
x_to = insize - offset_[0] - 1 if offset_[0] >= 0 else insize - 1
y_to = insize - offset_[1] - 1 if offset_[1] >= 0 else insize - 1
crop_img[y_from:y_to+1, x_from:x_to+1] = img[y1:y2+1, x1:x2+1].copy()
crop_mask[y_from:y_to+1, x_from:x_to+1] = ignore_mask[y1:y2+1, x1:x2+1].copy()
poses[:, :, :2] -= offset
return crop_img.astype('uint8'), crop_mask, poses
def distort_color(self, img):
img_max = np.broadcast_to(np.array(255, dtype=np.uint8), img.shape[:-1])
img_min = np.zeros(img.shape[:-1], dtype=np.uint8)
hsv_img = cv2.cvtColor(img.copy(), cv2.COLOR_BGR2HSV).astype(np.int32)
hsv_img[:, :, 0] = np.maximum(np.minimum(hsv_img[:, :, 0] - 10 + np.random.randint(20 + 1), img_max), img_min) # hue
hsv_img[:, :, 1] = np.maximum(np.minimum(hsv_img[:, :, 1] - 40 + np.random.randint(80 + 1), img_max), img_min) # saturation
hsv_img[:, :, 2] = np.maximum(np.minimum(hsv_img[:, :, 2] - 30 + np.random.randint(60 + 1), img_max), img_min) # value
hsv_img = hsv_img.astype(np.uint8)
distorted_img = cv2.cvtColor(hsv_img, cv2.COLOR_HSV2BGR)
return distorted_img
def flip_img(self, img, mask, poses):
flipped_img = cv2.flip(img, 1)
flipped_mask = cv2.flip(mask.astype(np.uint8), 1).astype('bool')
poses[:, :, 0] = img.shape[1] - 1 - poses[:, :, 0]
def swap_joints(poses, joint_type_1, joint_type_2):
tmp = poses[:, joint_type_1].copy()
poses[:, joint_type_1] = poses[:, joint_type_2]
poses[:, joint_type_2] = tmp
swap_joints(poses, JointType.LeftEye, JointType.RightEye)
swap_joints(poses, JointType.LeftEar, JointType.RightEar)
swap_joints(poses, JointType.LeftShoulder, JointType.RightShoulder)
swap_joints(poses, JointType.LeftElbow, JointType.RightElbow)
swap_joints(poses, JointType.LeftHand, JointType.RightHand)
swap_joints(poses, JointType.LeftWaist, JointType.RightWaist)
swap_joints(poses, JointType.LeftKnee, JointType.RightKnee)
swap_joints(poses, JointType.LeftFoot, JointType.RightFoot)
return flipped_img, flipped_mask, poses
def augment_data(self, img, ignore_mask, poses):
aug_img = img.copy()
aug_img, ignore_mask, poses = self.random_resize_img(aug_img, ignore_mask, poses)
aug_img, ignore_mask, poses = self.random_rotate_img(aug_img, ignore_mask, poses)
aug_img, ignore_mask, poses = self.random_crop_img(aug_img, ignore_mask, poses)
if np.random.randint(2):
aug_img = self.distort_color(aug_img)
if np.random.randint(2):
aug_img, ignore_mask, poses = self.flip_img(aug_img, ignore_mask, poses)
return aug_img, ignore_mask, poses
# return shape: (height, width)
def generate_gaussian_heatmap(self, shape, joint, sigma):
x, y = joint
grid_x = np.tile(np.arange(shape[1]), (shape[0], 1))
grid_y = np.tile(np.arange(shape[0]), (shape[1], 1)).transpose()
grid_distance = (grid_x - x) ** 2 + (grid_y - y) ** 2
gaussian_heatmap = np.exp(-0.5 * grid_distance / sigma**2)
return gaussian_heatmap
def generate_heatmaps(self, img, poses, heatmap_sigma):
heatmaps = np.zeros((0,) + img.shape[:-1])
sum_heatmap = np.zeros(img.shape[:-1])
for joint_index in range(len(JointType)):
heatmap = np.zeros(img.shape[:-1])
for pose in poses:
if pose[joint_index, 2] > 0:
jointmap = self.generate_gaussian_heatmap(img.shape[:-1], pose[joint_index][:2], heatmap_sigma)
heatmap[jointmap > heatmap] = jointmap[jointmap > heatmap]
sum_heatmap[jointmap > sum_heatmap] = jointmap[jointmap > sum_heatmap]
heatmaps = np.vstack((heatmaps, heatmap.reshape((1,) + heatmap.shape)))
bg_heatmap = 1 - sum_heatmap # background channel
heatmaps = np.vstack((heatmaps, bg_heatmap[None]))
return heatmaps.astype('f')
# return shape: (2, height, width)
def generate_constant_paf(self, shape, joint_from, joint_to, paf_width):
if np.array_equal(joint_from, joint_to): # same joint
return np.zeros((2,) + shape[:-1])
joint_distance = np.linalg.norm(joint_to - joint_from)
unit_vector = (joint_to - joint_from) / joint_distance
rad = np.pi / 2
rot_matrix = np.array([[np.cos(rad), np.sin(rad)], [-np.sin(rad), np.cos(rad)]])
vertical_unit_vector = np.dot(rot_matrix, unit_vector)
grid_x = np.tile(np.arange(shape[1]), (shape[0], 1))
grid_y = np.tile(np.arange(shape[0]), (shape[1], 1)).transpose()
horizontal_inner_product = unit_vector[0] * (grid_x - joint_from[0]) + unit_vector[1] * (grid_y - joint_from[1])
horizontal_paf_flag = (0 <= horizontal_inner_product) & (horizontal_inner_product <= joint_distance)
vertical_inner_product = vertical_unit_vector[0] * (grid_x - joint_from[0]) + vertical_unit_vector[1] * (grid_y - joint_from[1])
vertical_paf_flag = np.abs(vertical_inner_product) <= paf_width
paf_flag = horizontal_paf_flag & vertical_paf_flag
constant_paf = np.stack((paf_flag, paf_flag)) * np.broadcast_to(unit_vector, shape[:-1] + (2,)).transpose(2, 0, 1)
return constant_paf
def generate_pafs(self, img, poses, paf_sigma):
pafs = np.zeros((0,) + img.shape[:-1])
for limb in params['limbs_point']:
paf = np.zeros((2,) + img.shape[:-1])
paf_flags = np.zeros(paf.shape) # for constant paf
for pose in poses:
joint_from, joint_to = pose[limb]
if joint_from[2] > 0 and joint_to[2] > 0:
limb_paf = self.generate_constant_paf(img.shape, joint_from[:2], joint_to[:2], paf_sigma)
limb_paf_flags = limb_paf != 0
paf_flags += np.broadcast_to(limb_paf_flags[0] | limb_paf_flags[1], limb_paf.shape)
paf += limb_paf
paf[paf_flags > 0] /= paf_flags[paf_flags > 0]
pafs = np.vstack((pafs, paf))
return pafs.astype('f')
def get_img_annotation(self, ind=None, img_id=None):
"""インデックスまたは img_id から coco annotation dataを抽出、条件に満たない場合はNoneを返す """
annotations = None
if ind is not None:
img_id = self.imgIds[ind]
anno_ids = self.coco.getAnnIds(imgIds=[img_id], iscrowd=None)
# annotation for that image
if len(anno_ids) > 0:
annotations_for_img = self.coco.loadAnns(anno_ids)
person_cnt = 0
valid_annotations_for_img = []
for annotation in annotations_for_img:
# if too few keypoints or too small
if annotation['num_keypoints'] >= params['min_keypoints'] and annotation['area'] > params['min_area']:
person_cnt += 1
valid_annotations_for_img.append(annotation)
# if person annotation
if person_cnt > 0:
annotations = valid_annotations_for_img
if self.mode == 'train':
img_path = os.path.join(params['coco_dir'], 'train2017', self.coco.loadImgs([img_id])[0]['file_name'])
mask_path = os.path.join(params['coco_dir'], 'ignore_mask_train2017', '{:012d}.png'.format(img_id))
else:
img_path = os.path.join(params['coco_dir'], 'val2017', self.coco.loadImgs([img_id])[0]['file_name'])
mask_path = os.path.join(params['coco_dir'], 'ignore_mask_val2017', '{:012d}.png'.format(img_id))
img = cv2.imread(img_path)
ignore_mask = cv2.imread(mask_path, 0)
if ignore_mask is None:
ignore_mask = np.zeros(img.shape[:2], 'bool')
else:
ignore_mask = ignore_mask == 255
if self.mode == 'eval':
return img, img_id, annotations_for_img, ignore_mask
return img, img_id, annotations, ignore_mask
def parse_coco_annotation(self, annotations):
"""coco annotation dataのアノテーションをposes配列に変換"""
poses = np.zeros((0, len(JointType), 3), dtype=np.int32)
for ann in annotations:
ann_pose = np.array(ann['keypoints']).reshape(-1, 3)
pose = np.zeros((1, len(JointType), 3), dtype=np.int32)
# convert poses position
for i, joint_index in enumerate(params['coco_joint_indices']):
pose[0][joint_index] = ann_pose[i]
# compute neck position
if pose[0][JointType.LeftShoulder][2] > 0 and pose[0][JointType.RightShoulder][2] > 0:
pose[0][JointType.Neck][0] = int((pose[0][JointType.LeftShoulder][0] + pose[0][JointType.RightShoulder][0]) / 2)
pose[0][JointType.Neck][1] = int((pose[0][JointType.LeftShoulder][1] + pose[0][JointType.RightShoulder][1]) / 2)
pose[0][JointType.Neck][2] = 2
poses = np.vstack((poses, pose))
gt_pose = np.array(ann['keypoints']).reshape(-1, 3)
return poses
def generate_labels(self, img, poses, ignore_mask):
img, ignore_mask, poses = self.augment_data(img, ignore_mask, poses)
resized_img, ignore_mask, resized_poses = self.resize_data(img, ignore_mask, poses, shape=(self.insize, self.insize))
heatmaps = self.generate_heatmaps(resized_img, resized_poses, params['heatmap_sigma'])
pafs = self.generate_pafs(resized_img, resized_poses, params['paf_sigma'])
ignore_mask = cv2.morphologyEx(ignore_mask.astype('uint8'), cv2.MORPH_DILATE, np.ones((16, 16))).astype('bool')
return resized_img, pafs, heatmaps, ignore_mask
def get_example(self, i):
img, img_id, annotations, ignore_mask = self.get_img_annotation(ind=i)
if self.mode == 'eval':
# don't need to make heatmaps/pafs
return img, annotations, img_id
# if no annotations are available
while annotations is None:
img_id = self.imgIds[np.random.randint(len(self))]
img, img_id, annotations, ignore_mask = self.get_img_annotation(img_id=img_id)
poses = self.parse_coco_annotation(annotations)
resized_img, pafs, heatmaps, ignore_mask = self.generate_labels(img, poses, ignore_mask)
return resized_img, pafs, heatmaps, ignore_mask
if __name__ == '__main__':
mode = 'train'
coco = COCO(os.path.join(params['coco_dir'], 'annotations/person_keypoints_{}2017.json'.format(mode)))
data_loader = CocoDataLoader(coco, params['insize'], mode=mode)
cv2.namedWindow('w', cv2.WINDOW_NORMAL)
for i in range(len(data_loader)):
img, img_id, annotations, ignore_mask = data_loader.get_img_annotation(ind=i)
if annotations is not None:
poses = data_loader.parse_coco_annotation(annotations)
resized_img, pafs, heatmaps, ignore_mask = data_loader.generate_labels(img, poses, ignore_mask)
# resize to view
shape = (params['insize'],) * 2
pafs = cv2.resize(pafs.transpose(1, 2, 0), shape).transpose(2, 0, 1)
heatmaps = cv2.resize(heatmaps.transpose(1, 2, 0), shape).transpose(2, 0, 1)
ignore_mask = cv2.resize(ignore_mask.astype(np.uint8)*255, shape) > 0
# overlay labels
img_to_show = resized_img.copy()
img_to_show = data_loader.overlay_pafs(img_to_show, pafs)
img_to_show = data_loader.overlay_heatmap(img_to_show, heatmaps[:-1].max(axis=0))
img_to_show = data_loader.overlay_ignore_mask(img_to_show, ignore_mask)
cv2.imshow('w', np.hstack((resized_img, img_to_show)))
k = cv2.waitKey(0)
if k == ord('q'):
sys.exit()