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utils.py
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utils.py
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import os
import cv2
import glob
import h5py
import math
import scipy
import random
import numpy as np
import tensorflow as tf
import keras.backend as K
from keras.losses import mean_squared_error
from scipy.ndimage.filters import gaussian_filter
from keras.layers import AveragePooling2D
from skimage.measure import compare_psnr, compare_ssim
def get_density_map_gaussian(im, points, adaptive_mode=False, fixed_value=15, fixed_values=None):
density_map = np.zeros(im.shape[:2], dtype=np.float32)
h, w = density_map.shape[:2]
num_gt = np.squeeze(points).shape[0]
if num_gt == 0:
return density_map
if adaptive_mode == True:
fixed_values = None
leafsize = 2048
tree = scipy.spatial.KDTree(points.copy(), leafsize=leafsize)
distances, locations = tree.query(points, k=4)
for idx, p in enumerate(points):
p = np.round(p).astype(int)
p[0], p[1] = min(h-1, p[1]), min(w-1, p[0])
if num_gt > 1:
if adaptive_mode == 1:
sigma = int(np.sum(distances[idx][1:4]) * 0.1)
elif adaptive_mode == 0:
sigma = fixed_value
else:
sigma = fixed_value
sigma = max(1, sigma)
gaussian_radius_no_detection = sigma * 3
gaussian_radius = gaussian_radius_no_detection
if fixed_values is not None:
grid_y, grid_x = int(p[0]//(h/3)), int(p[1]//(w/3))
grid_idx = grid_y * 3 + grid_x
gaussian_radius = fixed_values[grid_idx] if fixed_values[grid_idx] else gaussian_radius_no_detection
gaussian_map = np.multiply(
cv2.getGaussianKernel(gaussian_radius*2+1, sigma),
cv2.getGaussianKernel(gaussian_radius*2+1, sigma).T
)
gaussian_map[gaussian_map < 0.0003] = 0
if np.sum(gaussian_map):
gaussian_map = gaussian_map / np.sum(gaussian_map)
x_left, x_right, y_up, y_down = 0, gaussian_map.shape[1], 0, gaussian_map.shape[0]
# cut the gaussian kernel
if p[1] < gaussian_radius:
x_left = gaussian_radius - p[1]
if p[0] < gaussian_radius:
y_up = gaussian_radius - p[0]
if p[1] + gaussian_radius >= w:
x_right = gaussian_map.shape[1] - (gaussian_radius + p[1] - w) - 1
if p[0] + gaussian_radius >= h:
y_down = gaussian_map.shape[0] - (gaussian_radius + p[0] - h) - 1
density_map[
max(0, p[0]-gaussian_radius):min(density_map.shape[0], p[0]+gaussian_radius+1),
max(0, p[1]-gaussian_radius):min(density_map.shape[1], p[1]+gaussian_radius+1)
] += gaussian_map[y_up:y_down, x_left:x_right]
# density_map[density_map < 0.0003] = 0
density_map = density_map / (np.sum(density_map / num_gt))
return density_map
def get_density_map_gaussian_old(im, points, adaptive_mode=0, fixed_value=15, with_direction=False, templates=None, normal_distribution_mask=False):
density_map = np.zeros(im.shape[:2], dtype=np.float32)
h, w = density_map.shape[:2]
num_gt = np.squeeze(points).shape[0]
if num_gt == 0:
return density_map
if adaptive_mode == 1:
# referred from https://github.com/vlad3996/computing-density-maps/blob/master/make_ShanghaiTech.ipynb
leafsize = 2048
tree = scipy.spatial.KDTree(points.copy(), leafsize=leafsize)
distances, locations = tree.query(points, k=4)
angle_idx = [0, 45, 90, 135]
for idx, p in enumerate(points):
p = np.round(p).astype(int)
p[0], p[1] = min(h-1, p[1]), min(w-1, p[0])
if num_gt > 1:
if adaptive_mode == 1:
sigma = int(np.sum(distances[idx][1:4]) * 0.1)
elif adaptive_mode == 0:
sigma = fixed_value
else:
sigma = fixed_value # np.average([h, w]) / 2. / 2.
sigma = max(1, sigma)
gaussian_radius = sigma * 3
# filter_mask = np.zeros_like(density_map)
# gaussian_center = (p[0], p[1])
# filter_mask[gaussian_center] = 1
# density_map += gaussian_filter(filter_mask, sigma, mode='constant')
# If you feel that the scipy api is too slow (gaussian_filter) -- Substitute it with codes below
# could make it about 100+ times faster, taking around 1.5 minutes on the whole ShanghaiTech dataset A and B.
if with_direction:
dt = np.array(distances[idx][1:4]).tolist()
idx_3 = locations[idx][1:4]
locations_3 = points[idx_3]
idx_near = [d for d in range(len(dt)) if dt[d] < gaussian_radius*2]
distances_3 = distances[idx][1:4][idx_near]
locations_3 = locations_3[idx_near]
if len(distances_3) > 1:
weights_add = []
for idx_d in range(len(distances_3)):
if distances_3[idx_d] == 0:
if np.mean(distances_3) == 0:
weights_add.append(1/3)
else:
weights_add.append(1/np.mean(distances_3))
else:
weights_add.append(1/distances_3[idx_d])
weights_add = np.array(weights_add) / np.sum(weights_add)
# print(distances_3, '\n', weights_add)
angles_3 = []
for l in locations_3:
if l[0] == p[1]:
angle = 90
elif l[1] == p[0]:
angle = 0
else:
slope = (l[1] - p[0]) / (l[0] - p[1])
if np.sin(np.deg2rad(45/2)) < slope < np.sin(np.deg2rad(45/2)):
angle = 45
elif slope > np.sin(np.deg2rad(90-45/2)) or slope < - np.sin(np.deg2rad(90-45/2)):
angle = 90
elif - np.sin(np.deg2rad(45/2)) < slope < np.sin(np.deg2rad(45/2)):
angle = 0
else:
angle = 135
angles_3.append(angle)
gaussian_map = np.zeros((gaussian_radius*2+1, gaussian_radius*2+1))
for ag_idx in range(len(angles_3)):
# print(angle_idx.index(angles_3[ag_idx]), gaussian_map.shape, templates[angle_idx.index(angles_3[ag_idx])].shape)
temp = cv2.resize(
templates[angle_idx.index(angles_3[ag_idx])] * weights_add[ag_idx], (gaussian_radius*2+1, gaussian_radius*2+1),
interpolation=cv2.INTER_LANCZOS4
)
gaussian_map += (temp / np.sum(temp))
else:
gaussian_map = np.multiply(
cv2.getGaussianKernel(gaussian_radius*2+1, sigma),
cv2.getGaussianKernel(gaussian_radius*2+1, sigma).T
)
else:
gaussian_map = np.multiply(
cv2.getGaussianKernel(gaussian_radius*2+1, sigma),
cv2.getGaussianKernel(gaussian_radius*2+1, sigma).T
)
if normal_distribution_mask:
gaussian_map = np.zeros_like(gaussian_map)
cv2.circle(gaussian_map, (gaussian_radius, gaussian_radius), gaussian_radius//2, 255, -1)
gaussian_map = gaussian_map / np.sum(gaussian_map)
x_left, x_right, y_up, y_down = 0, gaussian_map.shape[1], 0, gaussian_map.shape[0]
# cut the gaussian kernel
if p[1] < gaussian_radius:
x_left = gaussian_radius - p[1]
if p[0] < gaussian_radius:
y_up = gaussian_radius - p[0]
if p[1] + gaussian_radius >= w:
x_right = gaussian_map.shape[1] - (gaussian_radius + p[1] - w) - 1
if p[0] + gaussian_radius >= h:
y_down = gaussian_map.shape[0] - (gaussian_radius + p[0] - h) - 1
density_map[
max(0, p[0]-gaussian_radius):min(density_map.shape[0], p[0]+gaussian_radius+1),
max(0, p[1]-gaussian_radius):min(density_map.shape[1], p[1]+gaussian_radius+1)
] += gaussian_map[y_up:y_down, x_left:x_right]
density_map = density_map / (np.sum(density_map / num_gt))
return density_map
def load_img(path):
img = cv2.cvtColor(cv2.imread(path), cv2.COLOR_BGR2RGB)
img = img / 255.0
img[:, :, 0]=(img[:, :, 0] - 0.485) / 0.229
img[:, :, 1]=(img[:, :, 1] - 0.456) / 0.224
img[:, :, 2]=(img[:, :, 2] - 0.406) / 0.225
# img[:, :, 0]=(img[:, :, 0] - 0.5) / 1
# img[:, :, 1]=(img[:, :, 1] - 0.5) / 1
# img[:, :, 2]=(img[:, :, 2] - 0.5) / 1
return img.astype(np.float32)
def img_from_h5(path):
gt_file = h5py.File(path, 'r')
density_map = np.asarray(gt_file['density'])
stride = 1
if stride > 1:
density_map_stride = np.zeros((np.asarray(density_map.shape).astype(int)//stride).tolist(), dtype=np.float32)
for r in range(density_map_stride.shape[0]):
for c in range(density_map_stride.shape[1]):
density_map_stride[r, c] = np.sum(density_map[r*stride:(r+1)*stride, c*stride:(c+1)*stride])
else:
density_map_stride = density_map
return density_map_stride
def gen_x_y(img_paths, train_val_test='train', augmentation_methods=['ori']):
x, y = [], []
for i in img_paths:
x_ = load_img(i)
y_ = img_from_h5(i.replace('.jpg', '.h5').replace('images', 'ground'))
x_, y_ = fix_singular_shape(x_), fix_singular_shape(y_)
if 'ori' in augmentation_methods:
x.append(np.expand_dims(x_, axis=0))
y.append(np.expand_dims(np.expand_dims(y_, axis=0), axis=-1))
if 'flip' in augmentation_methods and train_val_test == 'train':
x.append(np.expand_dims(cv2.flip(x_, 1), axis=0))
y.append(np.expand_dims(np.expand_dims(cv2.flip(y_, 1), axis=0), axis=-1))
if train_val_test == 'train':
random_num = random.randint(7, 77)
random.seed(random_num)
random.shuffle(x)
random.seed(random_num)
random.shuffle(y)
random.seed(random_num)
random.shuffle(img_paths)
return x, y, img_paths
def eval_loss(model, x, y, quality=False):
preds = []
for i in x:
preds.append(np.squeeze(model.predict(i)))
DM = []
labels = []
for i in y:
DM.append(np.squeeze(i))
labels.append(round(np.sum(i)))
losses_DMD = []
for i in range(len(preds)):
losses_DMD.append(np.mean(np.square(preds[i] - DM[i]))*5e7) # mean of Frobenius norm
loss_DMD = np.mean(losses_DMD)
losses_MAE = []
for i in range(len(preds)):
losses_MAE.append(np.abs(np.sum(preds[i]) - labels[i]))
losses_MAPE = []
for i in range(len(preds)):
losses_MAPE.append(np.abs(np.sum(preds[i]) - labels[i]) / labels[i])
losses_MSE = []
for i in range(len(preds)):
losses_MSE.append(np.square(np.sum(preds[i]) - labels[i]))
loss_DMD = np.mean(losses_DMD)
loss_MAE = np.mean(losses_MAE)
loss_MAPE = np.mean(losses_MAPE)
loss_MSE = np.sqrt(np.mean(losses_MSE))
if quality:
PSNR = []
SSIM = []
for i in range(len(preds)):
data_range = np.max([np.max(preds[i]), np.max(DM[i])])-np.min([np.min(preds[i]), np.min(DM[i])])
psnr = compare_psnr(preds[i], DM[i], data_range=data_range)
ssim = compare_ssim(preds[i], DM[i], data_range=data_range)
PSNR.append(psnr)
SSIM.append(ssim)
return loss_DMD, loss_MAE, loss_MAPE, loss_MSE, np.mean(PSNR), np.mean(SSIM)
return loss_DMD, loss_MAE, loss_MAPE, loss_MSE
def gen_paths(path_file_root='data/paths_train_val_test', dataset='A'):
path_file_root_curr = os.path.join(path_file_root, 'paths_'+dataset)
img_paths = []
paths = ['paths_test.txt', 'paths_train.txt']
print(os.listdir(path_file_root_curr)[:2])
for i in sorted([os.path.join(path_file_root_curr, p) for p in paths]):
with open(i, 'r') as fin:
img_paths.append([l.rstrip() for l in fin.readlines()])
return img_paths # img_paths_test, img_paths_train, img_paths_val
def eval_path_files(dataset="A", validation_split=0.05):
root = 'data/ShanghaiTech/'
paths_train = os.path.join(root, 'part_' + dataset, 'train_data', 'images')
paths_test = os.path.join(root, 'part_' + dataset, 'test_data', 'images')
img_paths_train = []
for img_path in glob.glob(os.path.join(paths_train, '*.jpg')):
img_paths_train.append(str(img_path))
print("len(img_paths_train) =", len(img_paths_train))
img_paths_test = []
for img_path in glob.glob(os.path.join(paths_test, '*.jpg')):
img_paths_test.append(str(img_path))
print("len(img_paths_test) =", len(img_paths_test))
random.shuffle(img_paths_train)
lst_to_write = [img_paths_train, img_paths_train[:int(len(img_paths_train)*validation_split)], img_paths_test]
for idx, i in enumerate(['train', 'val', 'test']):
with open('data/paths_train_val_test/paths_'+dataset+'/paths_'+i+'.txt', 'w') as fout:
fout.write(str(lst_to_write[idx]))
print('Writing to data/paths_train_val_test/paths_'+dataset+'/paths_'+i+'.txt')
return None
def ssim_loss(y_true, y_pred, c1=0.01**2, c2=0.03**2):
# Generate a 11x11 Gaussian kernel with standard deviation of 1.5
weights_initial = np.multiply(
cv2.getGaussianKernel(11, 1.5),
cv2.getGaussianKernel(11, 1.5).T
)
weights_initial = weights_initial.reshape(*weights_initial.shape, 1, 1)
weights_initial = K.cast(weights_initial, tf.float32)
mu_F = tf.nn.conv2d(y_pred, weights_initial, [1, 1, 1, 1], padding='SAME')
mu_Y = tf.nn.conv2d(y_true, weights_initial, [1, 1, 1, 1], padding='SAME')
mu_F_mu_Y = tf.multiply(mu_F, mu_Y)
mu_F_squared = tf.multiply(mu_F, mu_F)
mu_Y_squared = tf.multiply(mu_Y, mu_Y)
sigma_F_squared = tf.nn.conv2d(tf.multiply(y_pred, y_pred), weights_initial, [1, 1, 1, 1], padding='SAME') - mu_F_squared
sigma_Y_squared = tf.nn.conv2d(tf.multiply(y_true, y_true), weights_initial, [1, 1, 1, 1], padding='SAME') - mu_Y_squared
sigma_F_Y = tf.nn.conv2d(tf.multiply(y_pred, y_true), weights_initial, [1, 1, 1, 1], padding='SAME') - mu_F_mu_Y
ssim = ((2 * mu_F_mu_Y + c1) * (2 * sigma_F_Y + c2)) / ((mu_F_squared + mu_Y_squared + c1) * (sigma_F_squared + sigma_Y_squared + c2))
return 1 - tf.reduce_mean(ssim, reduction_indices=[1, 2, 3])
def ssim_eucli_loss(y_true, y_pred, alpha=0.001):
ssim = ssim_loss(y_true, y_pred)
eucli = mean_squared_error(y_true, y_pred)
loss = eucli + alpha * ssim
return loss
def local_sum_loss(y_true, y_pred, alpha=0.5, grid_pooling=3):
y_true_localized = AveragePooling2D((grid_pooling, grid_pooling))(y_true) * (grid_pooling ** 2)
y_pred_localized = AveragePooling2D((grid_pooling, grid_pooling))(y_pred) * (grid_pooling ** 2)
y_true, y_pred = y_true_localized, y_pred_localized
l1 = K.square(K.mean(K.abs(y_true - y_pred)))
l2 = K.mean(K.square(y_true - y_pred)) * 1000
loss = (1-alpha) * l1 + alpha * l2
loss = loss * 1
return loss
def random_cropping(x_train, y_train, grid=(2, 2)):
# Random cropping on training set
x_train_cropped, y_train_cropped = [], []
num_crop = grid[0] * grid[1]
for idx_x in range(len(x_train)):
wid_patch, hei_patch = int(x_train[idx_x].shape[1] / grid[0]), int(x_train[idx_x].shape[0] / grid[1])
up_range_x, left_range_x = hei_patch * (grid[0] - 1), wid_patch * (grid[1] - 1)
# up_range_y, left_range_y = (np.array(y_train[idx_x].shape[0:-1]) * (1 - grid[1])).astype(np.int)
x_ = x_train[idx_x]
y_ = y_train[idx_x]
for _ in range(num_crop):
up_x = random.randint(0, up_range_x-1)
left_x = random.randint(0, left_range_x-1)
x_train_cropped.append(fix_singular_shape(x_[up_x:up_x+hei_patch, left_x:left_x+wid_patch, :]))
up_y = up_x
left_y = left_x
y_train_cropped.append(fix_singular_shape(y_[up_y:up_y+hei_patch, left_y:left_y+wid_patch, :]))
return np.asarray(x_train_cropped), np.asarray(y_train_cropped)
def fix_singular_shape(tensor):
# Append 0 lines or colums to fix the shapes as integers times of 8, since there are 3 pooling layers.
for idx_sp in [0, 1]:
remainder = tensor.shape[idx_sp] % 8
if remainder != 0:
fix_len = 8 - remainder
pad_list = []
for idx_pdlst in range(len(tensor.shape)):
if idx_pdlst != idx_sp:
pad_list.append([0, 0])
else:
pad_list.append([int(fix_len/2), fix_len - int(fix_len/2)])
tensor = np.pad(tensor, pad_list, 'constant')
return tensor
# def compare_psnr(img1, img2):
# mse = np.mean(np.square(img1 - img2))
# if mse:
# psnr = 10 * np.log10((255**2)/mse)
# else:
# psnr = 1e6
# return psnr
# def flip_horizontally(x_train, y_train):
# # Flip horizontally
# x_train_flipped, y_train_flipped = [], []
# for x in x_train:
# x_train_flipped.append(x[:, :, ::-1, :])
# for y in y_train:
# y_train_flipped.append(y[:, :, ::-1, :])
# x_train += x_train_flipped
# y_train += y_train_flipped
# return x_train, y_train
# def data_augmentation(x_train, y_train, augmentation_methods):
# return x_train, y_train