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)
context aware visualize seem ok	1bdb6ffe77ca4e40ef8f299b2506df2266243db4	Thai Thien	2020-02-02 05:07:10
visualize eval context aware network seem ok	f3fe45c23dfeab3730624737efabb0b14d23c25b	Thai Thien	2020-02-02 04:50:34
visualize_shanghaitech_pacnn_with_perspective run without error	12366a2de2bd60ff4bd36e6132d44e37dedf7462	Thai Thien	2020-02-02 04:21:16
eval context aware network on ShanghaiTechB can run	e8c454d2b6d287c830c1286c9a37884b3cfc615f	Thai Thien	2020-02-02 04:09:14
import ShanghaiTechDataPath in data_util	e81eb56315d44375ff5c0e747d61456601492f8f	Thai Thien	2020-02-02 04:04:36
add model_context_aware_network.py	2a36025c001d85afc064c090f4d22987b328977b	Thai Thien	2020-02-02 03:46:38
PACNN (TODO: test this)	44d5ae7ec57c760fb4f105dd3e3492148a0cc075	Thai Thien	2020-02-02 03:40:26
add data path	80134de767d0137a663f343e4606bafc57a1bc1f	Thai Thien	2020-02-02 03:38:21
test if ShanghaiTech datapath is correct	97ee84944a4393ec3732879b24f614826f8e7798	Thai Thien	2020-02-01 03:57:31
refactor and test ShanghaiTech datapath	9542ebc00f257edc38690180b7a4353794be4019	Thai Thien	2020-02-01 03:53:49
fix the unzip flow	b53c5989935335377eb6a88c942713d3eccc5df7	Thai Thien	2020-02-01 03:53:13
data_script run seem ok	67420c08fc1c10a66404d3698994865726a106cd	Thai Thien	2020-02-01 03:33:18
add perspective	642d6fff8c9f31e510fda85a7fb631fb855d8a6d	Thai Thien	2019-10-06 16:54:44
fix padding with p	86c2fa07822d956a34b3b37e14da485a4249f01b	Thai Thien	2019-10-06 02:52:58
pacnn perspective loss	fb673e38a5f24ae9004fe2b7b93c88991e0c2304	Thai Thien	2019-10-06 01:38:28
data_flow shanghaitech_pacnn_with_perspective seem working	91d350a06f358e03223966297d124daee94123d0	Thai Thien	2019-10-06 01:31:11
multiscale loss and final loss only mode	c65dd0e74ad28503821e5c8651a3b47b4a0c7c64	Thai Thien	2019-10-05 15:58:19
wip : perspective map	eac63f2671dc5b064753acc4f40bf0f9f216ad2a	Thai Thien	2019-10-04 16:26:56
shell script	f2106e700b6f6174d4dd276f25ec6f3d9ff239bb	thient	2019-10-04 07:42:51
WIP	42c7c8e1d772fbbda61a4bdf9e329f74e1efb600	tthien	2019-10-03 17:52:47

Commit 1bdb6ffe77ca4e40ef8f299b2506df2266243db4 - context aware visualize seem ok
Author: Thai Thien
Author date (UTC): 2020-02-02 05:07
Committer name: Thai Thien
Committer date (UTC): 2020-02-02 05:07
Parent(s): f3fe45c23dfeab3730624737efabb0b14d23c25b
Signer:
Signing key:
Signing status: N
Tree: eae29b15ba8602c65c1d71618cb0c04d813e33f0

File	Lines added	Lines deleted
eval_context_aware_network.py	24	11
visualize_util.py	9	0

File eval_context_aware_network.py changed (mode: 100644) (index 6c8d057..9b254c3)
...	...	from torchvision import transforms
10	10	from models.context_aware_network import CANNet	from models.context_aware_network import CANNet
11	11	from data_util import ShanghaiTechDataPath	from data_util import ShanghaiTechDataPath
12	12	from hard_code_variable import HardCodeVariable	from hard_code_variable import HardCodeVariable
13		from visualize_util import save_img, save_density_map
	13		from visualize_util import save_img, save_density_map, save_density_map_with_colorrange
14	14
15	15	_description="""	_description="""
16	16	This file run predict	This file run predict

...	...	for i in range(len(img_paths)):
92	92	pred.append(pred_sum)	pred.append(pred_sum)
93	93	gt.append(np.sum(groundtruth))	gt.append(np.sum(groundtruth))
94	94	print("done ", i, "pred ",pred_sum, " gt ", np.sum(groundtruth))	print("done ", i, "pred ",pred_sum, " gt ", np.sum(groundtruth))
	95
	96		max_people_per_pix = 0
	97		if density_1.max() > max_people_per_pix:
	98		max_people_per_pix = density_1.max()
	99		if density_2.max() > max_people_per_pix:
	100		max_people_per_pix = density_2.max()
	101		if density_3.max() > max_people_per_pix:
	102		max_people_per_pix = density_3.max()
	103		if density_4.max() > max_people_per_pix:
	104		max_people_per_pix = density_4.max()
	105
95	106	## print out visual	## print out visual
96		name_prefix = os.path.join(saved_folder, "sample_"+str(i))
97		save_img(img_original_1, name_prefix+"_img1.png")
98		save_img(img_original_2, name_prefix + "_img2.png")
99		save_img(img_original_3, name_prefix + "_img3.png")
100		save_img(img_original_4, name_prefix + "_img4.png")
101
102		save_density_map(density_1.squeeze(), name_prefix + "_pred1.png")
103		save_density_map(density_2.squeeze(), name_prefix + "_pred2.png")
104		save_density_map(density_3.squeeze(), name_prefix + "_pred3.png")
105		save_density_map(density_4.squeeze(), name_prefix + "_pred4.png")
	107		if IS_VISUAL:
	108		name_prefix = os.path.join(saved_folder, "sample_"+str(i))
	109		save_img(img_original_1, name_prefix+"_img1.png")
	110		save_img(img_original_2, name_prefix + "_img2.png")
	111		save_img(img_original_3, name_prefix + "_img3.png")
	112		save_img(img_original_4, name_prefix + "_img4.png")
	113
	114		save_density_map_with_colorrange(density_1.squeeze(), name_prefix + "_pred1.png", 0, 0.18)
	115		save_density_map_with_colorrange(density_2.squeeze(), name_prefix + "_pred2.png", 0, 0.18)
	116		save_density_map_with_colorrange(density_3.squeeze(), name_prefix + "_pred3.png", 0, 0.18)
	117		save_density_map_with_colorrange(density_4.squeeze(), name_prefix + "_pred4.png", 0, 0.18)
106	118	##	##
107	119
108	120	print(len(pred))	print(len(pred))

...	...	rmse = np.sqrt(mean_squared_error(pred,gt))
112	124
113	125	print('MAE: ',mae)	print('MAE: ',mae)
114	126	print('RMSE: ',rmse)	print('RMSE: ',rmse)
	127		print("max people per pix ", max_people_per_pix)

File visualize_util.py changed (mode: 100644) (index 4064de3..0190315)
...	...	def save_density_map(density_map, name):
14	14	plt.margins(0, 0)	plt.margins(0, 0)
15	15	plt.imshow(density_map, cmap=CM.jet)	plt.imshow(density_map, cmap=CM.jet)
16	16	plt.savefig(name, dpi=600, bbox_inches='tight', pad_inches=0)	plt.savefig(name, dpi=600, bbox_inches='tight', pad_inches=0)
	17		plt.close()
17	18
	19		def save_density_map_with_colorrange(density_map, name, vmin, vmax):
	20		plt.figure(dpi=600)
	21		plt.axis('off')
	22		plt.margins(0, 0)
	23		plt.imshow(density_map, cmap=CM.jet)
	24		plt.clim(vmin, vmax)
	25		plt.savefig(name, dpi=600, bbox_inches='tight', pad_inches=0)
	26		plt.close()
18	27
19	28	def save_img(imgnp, name):	def save_img(imgnp, name):
20	29	# plt.imshow(imgnp[0].permute(1, 2, 0).numpy())	# plt.imshow(imgnp[0].permute(1, 2, 0).numpy())

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