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test.py
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test.py
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import os
import torch
import torch.nn.functional as F
from torch.autograd import Variable
import torchvision.transforms as standard_transforms
import misc.transforms as own_transforms
import tqdm
from model.locator import Crowd_locator
from misc.utils import *
from PIL import Image, ImageOps
import cv2
from collections import OrderedDict
dataset = 'JHU'
dataRoot = '../ProcessedData/' + dataset
test_list = 'test.txt'
GPU_ID = '2,3'
os.environ["CUDA_VISIBLE_DEVICES"] = GPU_ID
torch.backends.cudnn.benchmark = True
netName = 'HR_Net' # options: HR_Net,VGG16_FPN
model_path = './exp/01-03_13-54_JHU_HR_Net/ep_305_F1_0.676_Pre_0.764_Rec_0.607_mae_74.5_mse_332.6.pth'
out_file_name= './saved_exp_results/' + dataset + '_' + netName + '_' + test_list
if dataset == 'NWPU':
mean_std = ([0.446139603853, 0.409515678883, 0.395083993673], [0.288205742836, 0.278144598007, 0.283502370119])
if dataset == 'SHHA':
mean_std = ([0.410824894905, 0.370634973049, 0.359682112932], [0.278580576181, 0.26925137639, 0.27156367898])
if dataset == 'SHHB':
mean_std = ([0.452016860247, 0.447249650955, 0.431981861591], [0.23242045939, 0.224925786257, 0.221840232611])
if dataset == 'QNRF':
mean_std = ([0.413525998592, 0.378520160913, 0.371616870165], [0.284849464893, 0.277046442032, 0.281509846449])
if dataset == 'FDST':
mean_std = ([0.452016860247, 0.447249650955, 0.431981861591], [0.23242045939, 0.224925786257, 0.221840232611])
if dataset == 'JHU':
mean_std = ([0.429683953524, 0.437104910612, 0.421978861094], [0.235549390316, 0.232568427920, 0.2355950474739])
img_transform = standard_transforms.Compose([
standard_transforms.ToTensor(),
standard_transforms.Normalize(*mean_std)
])
restore = standard_transforms.Compose([
own_transforms.DeNormalize(*mean_std),
standard_transforms.ToPILImage()
])
def main():
txtpath = os.path.join(dataRoot, test_list)
with open(txtpath) as f:
lines = f.readlines()
test(lines, model_path)
def get_boxInfo_from_Binar_map(Binar_numpy, min_area=3):
Binar_numpy = Binar_numpy.squeeze().astype(np.uint8)
assert Binar_numpy.ndim == 2
cnt, labels, stats, centroids = cv2.connectedComponentsWithStats(Binar_numpy, connectivity=4) # centriod (w,h)
boxes = stats[1:, :]
points = centroids[1:, :]
index = (boxes[:, 4] >= min_area)
boxes = boxes[index]
points = points[index]
pre_data = {'num': len(points), 'points': points}
return pre_data, boxes
def test(file_list, model_path):
net = Crowd_locator(netName,GPU_ID,pretrained=True)
net.cuda()
state_dict = torch.load(model_path)
if len(GPU_ID.split(','))>1:
net.load_state_dict(state_dict)
else:
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k.replace('module.', '')
new_state_dict[name] = v
net.load_state_dict(new_state_dict)
net.eval()
gts = []
preds = []
file_list = tqdm.tqdm(file_list)
for infos in file_list:
filename = infos.split()[0]
imgname = os.path.join(dataRoot, 'images', filename + '.jpg')
img = Image.open(imgname)
if img.mode == 'L':
img = img.convert('RGB')
img = img_transform(img)[None, :, :, :]
slice_h, slice_w = 512,1024
slice_h, slice_w = slice_h, slice_w
with torch.no_grad():
img = Variable(img).cuda()
b, c, h, w = img.shape
crop_imgs, crop_dots, crop_masks = [], [], []
if h * w < slice_h * 2 * slice_w * 2 and h % 16 == 0 and w % 16 == 0:
[pred_threshold, pred_map, __] = [i.cpu() for i in net(img, mask_gt=None, mode='val')]
else:
if h % 16 != 0:
pad_dims = (0, 0, 0, 16 - h % 16)
h = (h // 16 + 1) * 16
img = F.pad(img, pad_dims, "constant")
if w % 16 != 0:
pad_dims = (0, 16 - w % 16, 0, 0)
w = (w // 16 + 1) * 16
img = F.pad(img, pad_dims, "constant")
for i in range(0, h, slice_h):
h_start, h_end = max(min(h - slice_h, i), 0), min(h, i + slice_h)
for j in range(0, w, slice_w):
w_start, w_end = max(min(w - slice_w, j), 0), min(w, j + slice_w)
crop_imgs.append(img[:, :, h_start:h_end, w_start:w_end])
mask = torch.zeros(1,1,img.size(2), img.size(3)).cpu()
mask[:, :, h_start:h_end, w_start:w_end].fill_(1.0)
crop_masks.append(mask)
crop_imgs, crop_masks = torch.cat(crop_imgs, dim=0), torch.cat(crop_masks, dim=0)
# forward may need repeatng
crop_preds, crop_thresholds = [], []
nz, period = crop_imgs.size(0), 4
for i in range(0, nz, period):
[crop_threshold, crop_pred, __] = [i.cpu() for i in net(crop_imgs[i:min(nz, i+period)],mask_gt = None, mode='val')]
crop_preds.append(crop_pred)
crop_thresholds.append(crop_threshold)
crop_preds = torch.cat(crop_preds, dim=0)
crop_thresholds = torch.cat(crop_thresholds, dim=0)
# splice them to the original size
idx = 0
pred_map = torch.zeros(b, 1, h, w).cpu()
pred_threshold = torch.zeros(b, 1, h, w).cpu().float()
for i in range(0, h, slice_h):
h_start, h_end = max(min(h - slice_h, i), 0), min(h, i + slice_h)
for j in range(0, w, slice_w):
w_start, w_end = max(min(w - slice_w, j), 0), min(w, j + slice_w)
pred_map[:, :, h_start:h_end, w_start:w_end] += crop_preds[idx]
pred_threshold[:, :, h_start:h_end, w_start:w_end] += crop_thresholds[idx]
idx += 1
mask = crop_masks.sum(dim=0)
pred_map = (pred_map / mask)
pred_threshold = (pred_threshold / mask)
a = torch.ones_like(pred_map)
b = torch.zeros_like(pred_map)
binar_map = torch.where(pred_map >= pred_threshold, a, b)
pred_data, boxes = get_boxInfo_from_Binar_map(binar_map.cpu().numpy())
with open(out_file_name, 'a') as f:
f.write(filename + ' ')
f.write(str(pred_data['num']) + ' ')
for ind,point in enumerate(pred_data['points'],1):
if ind < pred_data['num']:
f.write(str(int(point[0])) + ' ' + str(int(point[1])) + ' ')
else:
f.write(str(int(point[0])) + ' ' + str(int(point[1])))
f.write('\n')
f.close()
# record.close()
if __name__ == '__main__':
main()