def visualize_shanghaitech_keepfull():
HARD_CODE = HardCodeVariable()
shanghaitech_data = ShanghaiTechDataPath(root=HARD_CODE.SHANGHAITECH_PATH)
shanghaitech_data_part_a_train = shanghaitech_data.get_a().get_train().get(
)
saved_folder = "visualize/test_dataloader_shanghaitech"
os.makedirs(saved_folder, exist_ok=True)
train_list, val_list = get_train_val_list(shanghaitech_data_part_a_train,
test_size=0.2)
test_list = None
train_loader, val_loader, test_loader = get_dataloader(
train_list,
val_list,
test_list,
dataset_name="shanghaitech_keepfull",
visualize_mode=True)
# do with train loader
train_loader_iter = iter(train_loader)
for i in range(10):
img, label = next(train_loader_iter)
save_img(img, os.path.join(saved_folder,
"train_img" + str(i) + ".png"))
save_density_map(
label.numpy()[0][0],
os.path.join(saved_folder, "train_label" + str(i) + ".png"))
def visualize_shanghaitech_nonoverlap_downsample():
HARD_CODE = HardCodeVariable()
shanghaitech_data = ShanghaiTechDataPath(root=HARD_CODE.SHANGHAITECH_PATH)
shanghaitech_data_part_a_train = shanghaitech_data.get_a().get_train().get(
)
saved_folder = "visualize/test_dataloader"
os.makedirs(saved_folder, exist_ok=True)
DATA_PATH = HARD_CODE.SHANGHAITECH_PATH_PART_B
train_list, val_list = get_train_val_list(shanghaitech_data_part_a_train,
test_size=0.2)
test_list = None
# create data loader
train_loader, val_loader, test_loader = get_dataloader(
train_list,
val_list,
test_list,
dataset_name="shanghaitech_non_overlap_downsample",
visualize_mode=True)
img, label = next(iter(train_loader))
print(img.shape)
save_img(img, os.path.join(saved_folder,
"overlap_downsample_loader_1.png"))
save_density_map(
label[0].numpy()[0],
os.path.join(saved_folder,
"overlap_downsample_loader_with_p_density1.png"))
print("count1 ", label.numpy()[0].sum())
print("count2 ", label.numpy()[0].sum())
print("count3 ", label.numpy()[0].sum())
print("s1 ", label.shape)
def visualize_shanghaitech_pacnn_with_perspective():
HARD_CODE = HardCodeVariable()
shanghaitech_data = ShanghaiTechDataPath(root=HARD_CODE.SHANGHAITECH_PATH)
shanghaitech_data_part_a_train = shanghaitech_data.get_a().get_train().get(
)
saved_folder = "visualize/test_dataloader"
os.makedirs(saved_folder, exist_ok=True)
DATA_PATH = HARD_CODE.SHANGHAITECH_PATH_PART_A
train_list, val_list = get_train_val_list(shanghaitech_data_part_a_train,
test_size=0.2)
test_list = None
# create data loader
train_loader, val_loader, test_loader = get_dataloader(
train_list, val_list, test_list, dataset_name="ucf_cc_50")
train_loader_pacnn = torch.utils.data.DataLoader(ListDataset(
train_list,
shuffle=True,
transform=transforms.Compose([transforms.ToTensor()]),
train=True,
batch_size=1,
num_workers=4,
dataset_name="shanghaitech_pacnn_with_perspective",
debug=True),
batch_size=1,
num_workers=4)
img, label = next(iter(train_loader_pacnn))
print(img.shape)
save_img(img, os.path.join(saved_folder, "pacnn_loader_img.png"))
save_density_map(
label[0].numpy()[0],
os.path.join(saved_folder, "pacnn_loader_with_p_density1.png"))
save_density_map(
label[1].numpy()[0],
os.path.join(saved_folder, "pacnn_loader_with_p_density2.png"))
save_density_map(
label[2].numpy()[0],
os.path.join(saved_folder, "pacnn_loader_with_p_density3.png"))
save_density_map(label[3].numpy()[0],
os.path.join(saved_folder, "pacnn_loader_p_s_4.png"))
save_density_map(label[4].numpy()[0],
os.path.join(saved_folder, "pacnn_loader_p_5.png"))
print("count1 ", label[0].numpy()[0].sum())
print("count2 ", label[1].numpy()[0].sum())
print("count3 ", label[2].numpy()[0].sum())
print("count4 ", label[3].numpy()[0].sum())
print("count5 ", label[4].numpy()[0].sum())
print("s1 ", label[0].shape)
print("s2 ", label[1].shape)
print("s3 ", label[2].shape)
print("s4 ", label[3].shape)
print("s5 ", label[4].shape)
def visualize_evaluation_shanghaitech_keepfull(
path=None,
dataset="shanghaitech_keepfull_r50",
output="visualize/verify_dataloader_shanghaitech",
meta_data="data_info.txt"):
HARD_CODE = HardCodeVariable()
if path == None:
shanghaitech_data = ShanghaiTechDataPath(
root=HARD_CODE.SHANGHAITECH_PATH)
shanghaitech_data_part_a_train = shanghaitech_data.get_a().get_train(
).get()
path = shanghaitech_data_part_a_train
saved_folder = output
os.makedirs(saved_folder, exist_ok=True)
train_list, val_list = get_train_val_list(path, test_size=0.2)
test_list = None
train_loader, val_loader, test_loader = get_dataloader(
train_list,
val_list,
test_list,
dataset_name=dataset,
visualize_mode=True,
debug=True)
# do with train loader
train_loader_iter = iter(train_loader)
f = open(meta_data, "w")
total = len(train_loader)
for i in range(len(train_loader)):
img, label, debug_data = next(train_loader_iter)
p_count = debug_data["p_count"]
name = debug_data["name"][0]
item_number = img_name_to_int(name)
density_map_count = label.sum()
log_str = str(item_number) + " " + str(
density_map_count.item()) + " " + str(p_count.item())
print(log_str)
f.write(log_str + "\n")
save_img(
img,
os.path.join(saved_folder,
"train_img_" + str(item_number) + ".png"))
save_path = os.path.join(saved_folder,
"train_label_" + str(item_number) + ".png")
save_density_map(label.numpy()[0][0], save_path)
print(str(i) + "/" + str(total))
f.close()
def visualize_ucf_cc_50_pacnn():
HARD_CODE = HardCodeVariable()
saved_folder = "visualize/test_dataloader"
os.makedirs(saved_folder, exist_ok=True)
DATA_PATH = HARD_CODE.UCF_CC_50_PATH
train_list, val_list = get_train_val_list(DATA_PATH, test_size=0.2)
test_list = None
# create data loader
train_loader, val_loader, test_loader = get_dataloader(
train_list, val_list, test_list, dataset_name="ucf_cc_50")
train_loader_pacnn = torch.utils.data.DataLoader(ListDataset(
train_list,
shuffle=True,
transform=transforms.Compose([transforms.ToTensor()]),
train=True,
batch_size=1,
num_workers=4,
dataset_name="shanghaitech_pacnn",
debug=True),
batch_size=1,
num_workers=4)
img, label = next(iter(train_loader_pacnn))
print(img.shape)
save_img(img, os.path.join(saved_folder, "pacnn_loader_img.png"))
save_density_map(label[0].numpy()[0],
os.path.join(saved_folder, "pacnn_loader_density1.png"))
save_density_map(label[1].numpy()[0],
os.path.join(saved_folder, "pacnn_loader_density2.png"))
save_density_map(label[2].numpy()[0],
os.path.join(saved_folder, "pacnn_loader_density3.png"))
print("count1 ", label[0].numpy()[0].sum())
print("count2 ", label[1].numpy()[0].sum())
print("count3 ", label[2].numpy()[0].sum())
print("done ", i, "pred ", pred_sum, " gt ", np.sum(groundtruth))
max_people_per_pix = 0
if density_1.max() > max_people_per_pix:
max_people_per_pix = density_1.max()
if density_2.max() > max_people_per_pix:
max_people_per_pix = density_2.max()
if density_3.max() > max_people_per_pix:
max_people_per_pix = density_3.max()
if density_4.max() > max_people_per_pix:
max_people_per_pix = density_4.max()
## print out visual
if IS_VISUAL:
name_prefix = os.path.join(saved_folder, "sample_" + str(i))
save_img(img_original_1, name_prefix + "_img1.png")
save_img(img_original_2, name_prefix + "_img2.png")
save_img(img_original_3, name_prefix + "_img3.png")
save_img(img_original_4, name_prefix + "_img4.png")
save_density_map_with_colorrange(density_1.squeeze(),
name_prefix + "_pred1.png", 0, 0.18)
save_density_map_with_colorrange(density_2.squeeze(),
name_prefix + "_pred2.png", 0, 0.18)
save_density_map_with_colorrange(density_3.squeeze(),
name_prefix + "_pred3.png", 0, 0.18)
save_density_map_with_colorrange(density_4.squeeze(),
name_prefix + "_pred4.png", 0, 0.18)
##
print(len(pred))