RocketGit

hahattpro / crowd_counting_framework (public) (License: Unspecified) (since 2020-12-23) (hash sha1)

backupfrom https://github.com/ttpro1995/crowd_counting_framework

Clone URLs: https://rocketgit.com/user/hahattpro/crowd_counting_framework ssh://rocketgit@ssh.rocketgit.com/user/hahattpro/crowd_counting_framework git://git.rocketgit.com/user/hahattpro/crowd_counting_framework

List of commits:

Subject	Hash	Author	Date (UTC)
some visualize to debug data loader	e4f52007616acf307bddbde79c0fb4f8c649c785	Thai Thien	2019-09-13 17:35:45
wip	d7d44cad6774355bdfa45414258763f6c6a0c299	Thai Thien	2019-08-31 16:58:16
commit all	6dad7a58f7dbf9fc288ce9dd3e92be538851c2a7	Thai Thien	2019-08-29 19:10:44
input d1,d2,d3 match	fc2a809241f8b6356d964c63d40cbebd55ca5f6c	Thai Thien	2019-08-28 17:57:05
WIP	39eab26d061e61dfffbf164dbd5fd878299b7250	thient	2019-08-28 11:09:12
output of de is ok	dd770386674df3e0fbebafdfc48a9352bc28967d	thient	2019-08-28 10:54:09
code pacnn	c49537b5cc91e96e4e35c9338d2c95b9bb41c672	Thai Thien	2019-08-27 16:35:27
crowd counting stuff	da9f27a39cba9bdd021b6b5c562f5f7c2be50190	Thai Thien	2019-08-24 18:27:44
seem ok	53fa176c31669a0e89b04adf290cb398f0316c45	Thai Thien	2019-08-24 18:26:31
flow ok	ad849681000818dfbcd0c1715c2858aed7236041	Thai Thien	2019-08-24 17:00:02
wip	23c3ec48497782bbc91d829e1c8a682502360ab9	Thai Thien	2019-08-24 14:19:22
work in progress, try to use https://pytorch.org/ignite/quickstart.html	39c824fe8fc2501628ee42c236a844df45521007	Thai Thien	2019-08-24 07:41:46
Work in progress	984be31d85e5cbdb2af296ccdb128381fe9bf09e	Thai Thien	2019-08-24 05:30:51
README	045706df1fa3452d150a190675c60e80ebd18e08	Thai Thien	2019-08-24 04:11:49
init	18f7c296fb05c4340c1c20ca84c60fef4f93bb1f	Thai Thien	2019-08-24 04:08:16

Commit e4f52007616acf307bddbde79c0fb4f8c649c785 - some visualize to debug data loader
Author: Thai Thien
Author date (UTC): 2019-09-13 17:35
Committer name: Thai Thien
Committer date (UTC): 2019-09-13 17:35
Parent(s): d7d44cad6774355bdfa45414258763f6c6a0c299
Signing key:
Tree: 7db66f68e76455912abfef5045d7671bccb60283

File	Lines added	Lines deleted
hard_code_variable.py	3	0
visualize_data_loader.py	46	0
visualize_util.py	24	0

File hard_code_variable.py added (mode: 100644) (index 0000000..836144a)
	1	class HardCodeVariable():
	2	def __init__(self):
	3	self.UCF_CC_50_PATH = "/data/cv_data/UCFCrowdCountingDataset_CVPR13_with_people_density_map/UCF_CC_50"

File visualize_data_loader.py added (mode: 100644) (index 0000000..ad2acdc)
	1	from args_util import real_args_parse
	2	from data_flow import get_train_val_list, get_dataloader, create_training_image_list
	3	from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator
	4	from ignite.metrics import Loss, MeanAbsoluteError, MeanSquaredError
	5	from crowd_counting_error_metrics import CrowdCountingMeanAbsoluteError, CrowdCountingMeanSquaredError
	6	import torch
	7	from torch import nn
	8	import torch.nn.functional as F
	9	from models import CSRNet,PACNN
	10	import os
	11	import cv2
	12	from torchvision import datasets, transforms
	13	from data_flow import ListDataset
	14	import pytorch_ssim
	15
	16	from hard_code_variable import HardCodeVariable
	17	from visualize_util import save_img, save_density_map
	18
	19
	20	if __name__ == "__main__":
	21	HARD_CODE = HardCodeVariable()
	22	DATA_PATH = HARD_CODE.UCF_CC_50_PATH
	23	train_list, val_list = get_train_val_list(DATA_PATH, test_size=0.2)
	24	test_list = None
	25
	26	# create data loader
	27	train_loader, val_loader, test_loader = get_dataloader(train_list, val_list, test_list, dataset_name="ucf_cc_50")
	28	train_loader_pacnn = torch.utils.data.DataLoader(
	29	ListDataset(train_list,
	30	shuffle=True,
	31	transform=transforms.Compose([
	32	transforms.ToTensor()
	33	]),
	34	train=True,
	35	batch_size=1,
	36	num_workers=4, dataset_name="ucf_cc_50_pacnn"),
	37	batch_size=1, num_workers=4)
	38
	39	img, label = next(iter(train_loader_pacnn))
	40
	41	print(img.shape)
	42	save_img(img, "pacnn_loader_img.png")
	43	save_density_map(label[0].numpy()[0], "pacnn_loader_density1.png")
	44	save_density_map(label[1].numpy()[0], "pacnn_loader_density2.png")
	45	save_density_map(label[2].numpy()[0], "pacnn_loader_density3.png")
	46

File visualize_util.py added (mode: 100644) (index 0000000..4064de3)
	1	import glob
	2	import PIL.Image as Image
	3	from matplotlib import pyplot as plt
	4	from matplotlib import cm as CM
	5	import os
	6	import numpy as np
	7
	8	from PIL import Image
	9
	10
	11	def save_density_map(density_map, name):
	12	plt.figure(dpi=600)
	13	plt.axis('off')
	14	plt.margins(0, 0)
	15	plt.imshow(density_map, cmap=CM.jet)
	16	plt.savefig(name, dpi=600, bbox_inches='tight', pad_inches=0)
	17
	18
	19	def save_img(imgnp, name):
	20	# plt.imshow(imgnp[0].permute(1, 2, 0).numpy())
	21	plt.imsave(name, imgnp[0].permute(1, 2, 0).numpy())
	22	# plt.show()
	23	# im = Image.fromarray(imgnp[0].permute(1, 2, 0).numpy())
	24	# im.save(name)

Hints:
Before first commit, do not forget to setup your git environment:

git config --global user.name "your_name_here"
git config --global user.email "your@email_here"

Clone this repository using HTTP(S):

git clone https://rocketgit.com/user/hahattpro/crowd_counting_framework

Clone this repository using ssh (do not forget to upload a key first):

git clone ssh://rocketgit@ssh.rocketgit.com/user/hahattpro/crowd_counting_framework

Clone this repository using git:

git clone git://git.rocketgit.com/user/hahattpro/crowd_counting_framework

You are allowed to anonymously push to this repository.
This means that your pushed commits will automatically be transformed into a merge request:

... clone the repository ...
... make some changes and some commits ...
git push origin main