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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)
create success	99951e32fedfcdb2ebc272d1678c094998c499f8	Thai Thien	2020-12-18 17:17:34
bike_video_frame_404_dccnn_t3	bbbd4ad319ac87586afc6ce6675adcf9d5f41fef	Thai Thien	2020-12-18 15:53:26
fix load model stuff	8c5c9c985fb747dc6392c86aa5f49282f8d12297	Thai Thien	2020-12-18 15:44:21
import comet first	dd4426221a95d0916c8cc7c02c7587ada1d910b5	Thai Thien	2020-12-18 15:41:59
predict on frame from video	ec39a97372a15ad75811fdb3417d2e48078a30b2	Thai Thien	2020-12-18 15:40:36
fix normalize in data loader	56a64c14dc62c96aba532d233ff2782414738d05	Thai Thien	2020-12-17 15:36:22
change gpu 5	46d32b3fc05909b61f56389b01c2e50e52937d9b	Thai Thien	2020-12-16 16:40:47
eval_dccnn_bike_video_VID_20201204_133931_404_model_adamw1_bigtail13i_t4_bike20	95bb636a0b544b7a72fc4c65acb47472da468766	Thai Thien	2020-12-16 16:35:14
eval_dccnn_bike_video_VID_20201204_133931_404_model_adamw1_bigtail13i_t3_bike20	27740a0e21ca42490f5cfe516b2453f7be3a2483	Thai Thien	2020-12-16 16:17:12
batch 2	85433ffd51070572d7e743a2b4ef53f340b658d4	Thai Thien	2020-12-15 16:57:56
reduce batch size 3	d2f145009c06f5e6e6e1e0270878ae275b611c2f	Thai Thien	2020-12-15 16:55:46
t3 and t4	70e09414713ea22800d8e87a0dd984f18406c375	Thai Thien	2020-12-15 16:50:32
prepare bike 20s q100	556a4d8f695ca154a5db42b363a276d7ab233434	Thai Thien	2020-12-15 16:46:08
VID_20201204_133931_404	8264dcb4d5389ed8ef2920eb3419cfc505b6a09c	Thai Thien	2020-12-13 11:31:10
add VID_20201204_133931_404	a36ba35ca7c54dcf552d17a08fa8c87720ce1c69	Thai Thien	2020-12-13 11:30:24
print total length on console	6a956d93489926e99909d234bf4a7b13d39aad56	Thai Thien	2020-12-13 11:24:56
fix input of save_density_map	dc2f014fdd570909b811ac77629f0c8c0ca156da	Thai Thien	2020-12-13 11:21:37
detach cpu for save_density_map	f41362501a0bc3578de0a48e6cbe97966a66677c	Thai Thien	2020-12-13 11:19:45
predict_video_server pred.detach().cpu().numpy()	3c23cd51890f8b791be91ad8eb74bdba9ace905a	Thai Thien	2020-12-13 11:14:51
remove stuff	212a4a703ae301fb90a0f292d8ccf19a1026e071	Thai Thien	2020-12-13 11:10:28

Commit 99951e32fedfcdb2ebc272d1678c094998c499f8 - create success
Author: Thai Thien
Author date (UTC): 2020-12-18 17:17
Committer name: Thai Thien
Committer date (UTC): 2020-12-18 17:17
Parent(s): bbbd4ad319ac87586afc6ce6675adcf9d5f41fef
Signing key:
Tree: e43c40ecae0ed9d33865a0826c640e5279c61508

File	Lines added	Lines deleted
dataset_script/combine_img_raw_and_density_map.py	45	0

File dataset_script/combine_img_raw_and_density_map.py added (mode: 100644) (index 0000000..d708e75)
	1	import numpy as np
	2	from torchvision.io import write_jpeg, read_image
	3	import torch
	4	from torch import nn
	5	import os
	6
	7	def overlay_img_with_density(img_path, density_map_path, output_path):
	8	img_tensor = read_image(img_path)
	9	density_map_tensor = torch.load(density_map_path)
	10
	11	print(img_tensor.shape)
	12	print(density_map_tensor.shape)
	13	print(density_map_tensor.sum())
	14	density_map_tensor = torch.from_numpy(density_map_tensor).unsqueeze(dim=0).unsqueeze(dim=0)
	15	print("density_map_tensor.shape", density_map_tensor.shape) # torch.Size([1, 1, 46, 82])
	16	upsampling_density_map_tensor = nn.functional.interpolate(density_map_tensor, scale_factor=8) / 64
	17
	18	overlay_density_map = img_tensor.detach().clone()
	19	upsampling_density_map_tensor = (upsampling_density_map_tensor.squeeze(dim=0) / upsampling_density_map_tensor.max() * 255)
	20	overlay_density_map[0] = torch.clamp_max(img_tensor[0] + upsampling_density_map_tensor[0] * 2, max=255)
	21
	22	write_jpeg(overlay_density_map.type(torch.uint8), output_path, quality=100)
	23
	24	def single_image_case():
	25	density = "/data/my_crowd_image/tmp/PRED_IMG_697.jpg.torch"
	26	img = "/data/my_crowd_image/video_bike_q100/IMG_697.jpg"
	27	output_path = "/data/my_crowd_image/tmp/PRED_OVERLAY_IMG_697.jpg"
	28	overlay_img_with_density(img, density, output_path)
	29
	30	def convert_folder():
	31	density_folder = "/data/my_crowd_image/bike_video_frame_404_dccnn_t4/"
	32	img_folder = "/data/my_crowd_image/video_bike_q100/"
	33	output_folder = "/data/my_crowd_image/overlay_bike_video_frame_404_dccnn_t4/"
	34	count = 0
	35	for img_name in os.listdir(img_folder):
	36	img_dir = os.path.join(img_folder, img_name)
	37	density_dir = os.path.join(density_folder, "PRED_" + img_name +".torch")
	38	output_dir = os.path.join(output_folder, "PRED_OVERLAY_" + img_name)
	39	overlay_img_with_density(img_dir, density_dir, output_dir)
	40	print("done " + img_name)
	41	count+=1
	42	print("count ", count)
	43
	44	if __name__ == "__main__":
	45	convert_folder()

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