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

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List of commits:

Subject	Hash	Author	Date (UTC)
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
add readme	580cf43d1edddd67b1f6a2c57fdd5cee3dba925c	Thai Thien	2019-10-02 17:44:49

Commit f3fe45c23dfeab3730624737efabb0b14d23c25b - visualize eval context aware network seem ok
Author: Thai Thien
Author date (UTC): 2020-02-02 04:50
Committer name: Thai Thien
Committer date (UTC): 2020-02-02 04:50
Parent(s): b96910e05dd6e363f8e9669fd4e5df75c43b88d9
Signing key:
Tree: fa7d80f0532443593d3b3d56f770d5491f6bf3cf

File	Lines added	Lines deleted
eval_context_aware_network.py	34	1

File eval_context_aware_network.py changed (mode: 100644) (index 760e6bd..6c8d057)
...	...	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	14
14	15	_description="""	_description="""
15	16	This file run predict	This file run predict

...	...	Data path = /home/tt/project/ShanghaiTechCAN/part_B/test_data/images
17	18	model path = /home/tt/project/MODEL/Context-aware/part_B_pre.pth.tar	model path = /home/tt/project/MODEL/Context-aware/part_B_pre.pth.tar
18	19	"""	"""
19	20
	21		# if true, render every density map and its image
	22		IS_VISUAL = True
	23		saved_folder = "visualize/eval_context_aware_network_part_b"
	24
20	25	transform=transforms.Compose([	transform=transforms.Compose([
21	26	transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406],	transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406],
22	27	std=[0.229, 0.224, 0.225]),	std=[0.229, 0.224, 0.225]),
23	28	])	])
24	29
	30		transform_no_normalize=transforms.Compose([
	31		transforms.ToTensor()
	32		])
	33
25	34	# the folder contains all the test images	# the folder contains all the test images
26	35	hard_code = HardCodeVariable()	hard_code = HardCodeVariable()
27	36	shanghaitech_data = ShanghaiTechDataPath(root=hard_code.SHANGHAITECH_PATH)	shanghaitech_data = ShanghaiTechDataPath(root=hard_code.SHANGHAITECH_PATH)

...	...	img_paths=[]
33	42
34	43	for img_path in glob.glob(os.path.join(img_folder, '*.jpg')):	for img_path in glob.glob(os.path.join(img_folder, '*.jpg')):
35	44	img_paths.append(img_path)	img_paths.append(img_path)
36		# img_paths = img_paths[:10]
	45		img_paths = img_paths[:10]
37	46
38	47
39	48	model = CANNet()	model = CANNet()

...	...	model.eval()
49	58	pred= []	pred= []
50	59	gt = []	gt = []
51	60
	61		if IS_VISUAL:
	62		os.makedirs(saved_folder, exist_ok=True)
	63
52	64	for i in range(len(img_paths)):	for i in range(len(img_paths)):
	65		img_original = transform_no_normalize(Image.open(img_paths[i]).convert('RGB')).unsqueeze(0)
53	66	img = transform(Image.open(img_paths[i]).convert('RGB')).cuda()	img = transform(Image.open(img_paths[i]).convert('RGB')).cuda()
54	67	img = img.unsqueeze(0)	img = img.unsqueeze(0)
55	68	h,w = img.shape[2:4]	h,w = img.shape[2:4]
56	69	h_d = int(h/2)	h_d = int(h/2)
57	70	w_d = int(w/2)	w_d = int(w/2)
58	71	img_1 = Variable(img[:,:,:h_d,:w_d].cuda())	img_1 = Variable(img[:,:,:h_d,:w_d].cuda())
	72		img_original_1 = img_original[:,:,:h_d,:w_d]
	73
59	74	img_2 = Variable(img[:,:,:h_d,w_d:].cuda())	img_2 = Variable(img[:,:,:h_d,w_d:].cuda())
	75		img_original_2 = img_original[:,:,:h_d,w_d:]
	76
60	77	img_3 = Variable(img[:,:,h_d:,:w_d].cuda())	img_3 = Variable(img[:,:,h_d:,:w_d].cuda())
	78		img_original_3 = img_original[:,:,h_d:,:w_d]
	79
61	80	img_4 = Variable(img[:,:,h_d:,w_d:].cuda())	img_4 = Variable(img[:,:,h_d:,w_d:].cuda())
	81		img_original_4 = img_original[:,:,h_d:,w_d:]
	82
62	83	density_1 = model(img_1).data.cpu().numpy()	density_1 = model(img_1).data.cpu().numpy()
63	84	density_2 = model(img_2).data.cpu().numpy()	density_2 = model(img_2).data.cpu().numpy()
64	85	density_3 = model(img_3).data.cpu().numpy()	density_3 = model(img_3).data.cpu().numpy()

...	...	for i in range(len(img_paths)):
71	92	pred.append(pred_sum)	pred.append(pred_sum)
72	93	gt.append(np.sum(groundtruth))	gt.append(np.sum(groundtruth))
73	94	print("done ", i, "pred ",pred_sum, " gt ", np.sum(groundtruth))	print("done ", i, "pred ",pred_sum, " gt ", np.sum(groundtruth))
	95		## 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")
	106		##
74	107
75	108	print(len(pred))	print(len(pred))
76	109	print(len(gt))	print(len(gt))

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