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data_stats.py
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data_stats.py
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import os
import numpy as np
from tqdm import tqdm
from utils import get_bbox
cfg = {
'image_shape': [1280, 1918],
'scaled_h': 640,
'roi_h': 640,
'buffer': 50
}
#def extract_color_stats(img_dir, set_):
# img_mean, img_std = [], []
def extract_bbox(mask_dir, set_):
gt_bboxes = []
basedir = os.path.join(mask_dir, str(set_).zfill(2))
mask_files = [os.path.join(basedir, f) for f in os.listdir(basedir) if 'augment' not in f]
for mask_file in tqdm(mask_files):
gt_bboxes.append(get_bbox(mask_file))
gt_bboxes = np.array(gt_bboxes)
return gt_bboxes
def prepare_data_stats(args):
"""Prepare data statistics and save it in a config file"""
set_ = args.set
mask_dir = args.train_mask_dir
data_stats_file = os.path.join(args.train_data_dir,
str(set_).zfill(2), 'data_stats.npz')
print("Extracting ground truth bboxes for set {}...".format(set_))
gt_bboxes = extract_bbox(mask_dir, set_)
if set_ != 1 and set_ != 9:
flipped_set = 18 - set_
print("Extracting ground truth bboxes for set {}...".format(flipped_set))
gt_bboxes_flipped = extract_bbox(mask_dir, flipped_set)
w = cfg['image_shape'][1]
gt_bboxes_flipped[:,1] = w - gt_bboxes_flipped[:,1] - gt_bboxes_flipped[:,3]
gt_bboxes = np.concatenate((gt_bboxes, gt_bboxes_flipped), axis=0)
left_min, left_max = np.min(gt_bboxes[:, 1]), np.max(gt_bboxes[:, 1])
top_min, top_max = np.min(gt_bboxes[:, 0]), np.max(gt_bboxes[:, 0])
right_min = np.min(gt_bboxes[:, 1] + gt_bboxes[:, 3])
right_max = np.max(gt_bboxes[:, 1] + gt_bboxes[:, 3])
bottom_min = np.min(gt_bboxes[:, 0] + gt_bboxes[:, 2])
bottom_max = np.max(gt_bboxes[:, 0] + gt_bboxes[:, 2])
height_min, height_max = np.min(gt_bboxes[:, 2]), np.max(gt_bboxes[:, 2])
width_min, width_max = np.min(gt_bboxes[:, 3]), np.max(gt_bboxes[:, 3])
aspect = gt_bboxes[:, 3] / gt_bboxes[:, 2]
aspect_min, aspect_max = np.min(aspect), np.max(aspect)
aspect_median = np.median(aspect)
top_std, left_std = np.std(gt_bboxes[:, :2], axis=0)
bottom_std, right_std = np.std(gt_bboxes[:, :2] + gt_bboxes[:, 2:], axis=0)
height_mean, width_mean = np.mean(gt_bboxes[:, 2:], axis=0)
print("Saving data statistics to {}".format(data_stats_file))
basedir = os.path.dirname(data_stats_file)
if not os.path.exists(basedir):
os.makedirs(basedir)
np.savez(data_stats_file,
left_range=np.array([left_min, left_max]),
right_range=np.array([right_min, right_max]),
top_range=np.array([top_min, top_max]),
bottom_range=np.array([bottom_min, bottom_max]),
height_range=np.array([height_min, height_max]),
width_range=np.array([width_min, width_max]),
aspect_stats=np.array([aspect_min, aspect_max, aspect_median]),
mean=np.array([height_mean, width_mean]),
std=np.array([left_std, right_std, top_std, bottom_std]))
configure(args)
def configure(args):
"""This function determines the ideal image preprocessing such cropping and resizing
using data statistics."""
set_ = args.set
data_stats_file = os.path.join(args.train_data_dir,
str(set_).zfill(2), 'data_stats.npz')
config_file = os.path.join(args.train_data_dir,
str(set_).zfill(2), 'config.npy')
stats = np.load(data_stats_file)
img_height, img_width = cfg['image_shape']
left_crop = stats['left_range'][0] - 0.5 * stats['std'][0]
left_crop = max(0., left_crop)
right_crop = img_width - (stats['right_range'][1] + 0.5 * stats['std'][1])
right_crop = max(0., right_crop)
top_crop = stats['top_range'][0] - 0.7 * stats['std'][2]
top_crop = max(0., top_crop)
bottom_crop = img_height - (stats['bottom_range'][1] + 0.7 * stats['std'][3])
bottom_crop = max(0., bottom_crop)
if set_ == 1 or set == 9:
left_crop = min(left_crop, right_crop)
right_crop = left_crop
h_cropped = img_height - top_crop - bottom_crop
w_cropped = img_width - left_crop - right_crop
aspect = np.round(w_cropped / h_cropped, decimals=1)
print("Aspect ratio : ", aspect)
delta_h = (w_cropped / aspect - h_cropped)/2
top_crop = int(top_crop - delta_h)
bottom_crop = int(bottom_crop - delta_h)
left_crop = int(left_crop)
right_crop = int(right_crop)
crops = [left_crop, right_crop, top_crop, bottom_crop]
print("Crop margins (l,r,t.b) : ", crops)
cfg['crops'] = crops
img_h = cfg['scaled_h']
img_w = int(aspect * img_h)
scale = img_w / w_cropped
print("Scale factor : ", scale)
print("Final image shape : ", [img_h, img_w])
cfg['scale'] = scale
cfg['scaled_img_shape'] = [img_h, img_w]
roi_aspect = stats['aspect_stats'][2] # median
roi_aspect = np.round(roi_aspect, decimals=1)
roi_h = cfg['roi_h']
roi_w = int(roi_aspect * roi_h)
roi_shape = [roi_h, roi_w]
print("Input roi size for segmentation : ", roi_shape)
cfg['roi_shape'] = roi_shape
print("Saving config data to {}".format(config_file))
print("Extracting image mean...")
np.save(config_file, cfg)
def load_config(train_data_dir, set_):
cfg_file = os.path.join(train_data_dir,
str(set_).zfill(2), 'config.npy')
return np.load(cfg_file).item()
# img_h = 320
# img_w = int(aspect * img_h)
# if args.basic_model != 'vgg16':
# img_h += 1
# img_w += 1
# scale = img_w/w_cropped
# print("Scale factor : ", scale)
# print("Final image shape : ", [img_h, img_w])
# cfg['crops'] = crops
# cfg['scale'] = scale
# cfg['img_shape'] = [img_h, img_w]
#
# feat_stride = 16
# print("Using feature stride of ", feat_stride)
#
# r_median = np.round(stats['aspect_stats'][2], decimals=1)
# roi_h = 320
# roi_w = int(r_median * roi_h)
# roi_shape = [roi_h, roi_w]
# print("Input roi size for mask-RCN : ", roi_shape)
# cfg['roi_shape'] = roi_shape
#
# if args.basic_model == 'vgg16':
# feat_shape = [img_h//feat_stride, img_w//feat_stride, 512]
# roi_feat_shape = [roi_h // feat_stride, roi_w // feat_stride, 512]
# else:
# feat_shape = [(img_h-1)//feat_stride+1, (img_w-1)//feat_stride+1, 2048]
# roi_feat_shape = [(roi_h-1)//feat_stride+1, (roi_w-1)//feat_stride+1, 2048]
# print("Feature shape from RPN : ", feat_shape)
# print("Feature shape from mask-RCN : ", roi_feat_shape)
# cfg['feat_shape'] = feat_shape
# cfg['roi_feat_shape'] = roi_feat_shape
# class ImageLoader(object):
# def __init__(self):
# self.img_scale = cfg['scale']
# self.crop_margin = cfg['crops']
#
# def load_img(self, img_file):
# """ Load and preprocess an image. """
# img = cv2.imread(img_file)
#
# if self.bgr:
# cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
#
# left, right, top, bottom = self.crop_margin
# img = img[top:img_height - bottom, left:img_width - right, :]
#
# h = img_height - top - bottom
# w = img_width - left - right
# img = cv2.resize(img, None, fx=self.img_scale, fy=self.img_scale, interpolation=cv2.INTER_AREA)
# img = np.float32(img)
#
# img[:, :, 0] -= 123.68
# img[:, :, 1] -= 116.78
# img[:, :, 2] -= 103.94
#
# return img
#
# def load_imgs(self, img_files):
# """ Load and preprocess a list of images. """
# imgs = []
# for img_file in img_files:
# imgs.append(self.load_img(img_file))
# imgs = np.array(imgs, np.float32)
# return imgs