def get_human_data_from_img_path(self,
img_path: str,
export_path: str = None) -> List[Human]:
img = cv2.imread(img_path)
humans = self.get_humans_from_img(img)
if export_path is not None:
save_humans_img(img,
humans,
file_path=os.path.join(
get_create_path(export_path),
get_filename_from_path(img_path)))
return humans
def __init__(self, model_name: str, model_dir: str):
self.model_name: str = model_name
self.model_dir: str = get_create_path(model_dir)
self.num_epochs = 150
self.checkpoint_epoch: int = 50
# Optimizer
self.learning_rate: float = 0.01
self.momentum: float = 0.9
self.weight_decay: float = 5e-4
# LR Scheduler
self.learning_rate_scheduler: LearningRateSchedulerBase = LearningRateSchedulerDummy()
def __init__(self, func: Callable, cache_config: CacheConfig):
"""
Cache decorator which currently supports functions with base type, dictionary, list and lambda parameters
:param func:
"""
self.func = func
self.cache_config = cache_config
try:
self.func_cache_dir = self.get_function_hash()
except OSError as err:
print(
"OS Error, probably not possible to retrieve source code. Disable cache. Details: '{}'"
.format(err))
self.cache_config.cache_enabled = False
return
self.is_method = self.__is_method(self.func)
self.func_cache_dir_path = get_create_path(
os.path.join(self.cache_config.cache_dir, self.func_cache_dir))
self.arg_names = list(inspect.signature(self.func).parameters.keys())
self.replacement_args = None
if self.func.__name__ in self.cache_config.str_args_replacement_dict.keys(
):
self.replacement_args = self.cache_config.str_args_replacement_dict[
self.func.__name__]
"ehpi_journal_2019_03_pose_seed_0_cp0200",
"ehpi_journal_2019_03_pose_seed_104_cp0200",
"ehpi_journal_2019_03_pose_seed_123_cp0200",
"ehpi_journal_2019_03_pose_seed_142_cp0200",
"ehpi_journal_2019_03_pose_seed_200_cp0200",
#
"ehpi_journal_2019_03_both_seed_0_cp0200",
"ehpi_journal_2019_03_both_seed_104_cp0200",
"ehpi_journal_2019_03_both_seed_123_cp0200",
"ehpi_journal_2019_03_both_seed_142_cp0200",
"ehpi_journal_2019_03_both_seed_200_cp0200",
]
# Test set
test_set = get_test_set_lab(ehpi_dataset_path, ImageSize(1280, 720))
result_path = get_create_path(
os.path.join(data_dir, "results", "its_journal_experiment_results",
"lab"))
# test_set = get_test_set_office(ImageSize(1280, 720))
# result_path = get_create_path(os.path.join(data_dir, "results", "its_journal_experiment_results", "office"))
test_loader = DataLoader(test_set, batch_size=1, shuffle=False)
for model_name in model_names:
print("Model name: {}".format(model_name))
weights_path = os.path.join(models_dir, "{}.pth".format(model_name))
tester = TesterEhpi()
ehpi_results, seq_results = tester.test(test_loader,
weights_path,
model=EhpiLSTM(15, 5))
import logging
from nobos_commons.utils.file_helper import get_create_path
#TODO: Make this more useable ...
logger = logging.getLogger()
logFormatter = logging.Formatter("%(asctime)s [%(filename)s | %(funcName)s()] [%(levelname)s] %(message)s")
logPath = get_create_path("logs") #Todo configurable
fileHandler = logging.FileHandler("{0}/{1}.log".format(r'logs', 'info'))
fileHandler.setFormatter(logFormatter)
fileHandler.setLevel(logging.ERROR)
logger.addHandler(fileHandler)
consoleFormatter = logging.Formatter("[%(funcName)s] %(message)s")
consoleHandler = logging.StreamHandler()
consoleHandler.setFormatter(consoleFormatter)
consoleHandler.setLevel(logging.DEBUG)
logger.addHandler(consoleHandler)
logger.setLevel(logging.INFO)
for test_name in test_names:
all_labels_seq = []
all_preds_seq = []
all_labels_ehpi = []
all_preds_ehpi = []
for seed in seeds:
model_name = "ehpi_journal_2019_03_{}_seed_{}_cp0200".format(
test_name.lower(), seed)
print("Model name: {}".format(model_name))
ehpi_results = np.load(
os.path.join(result_path, "{}_ehpis.npy".format(model_name)))
seq_results = np.load(
os.path.join(result_path, "{}_seqs.npy".format(model_name)))
plot_path = get_create_path(os.path.join(result_path, model_name))
labels_seq, preds_seq, false_detected_seqs = eval_seq(
seq_results, plot_path,
"ActionSim ({}) [Seq]: confusion matrix (normalized)".format(
test_name))
labels_ehpi, preds_ehpi = eval_ehpi(
ehpi_results, plot_path,
"ActionSim ({}) [TW]: confusion matrix (normalized)".format(
test_name))
all_labels_seq.extend(labels_seq)
all_preds_seq.extend(preds_seq)
all_false_detected_seqs[seed][test_name] = false_detected_seqs
all_labels_ehpi.extend(labels_ehpi)
all_preds_ehpi.extend(preds_ehpi)
plot_confusion_matrix(
# --------- Draw objects
bbs_to_draw = [
bb for bb in image_content.objects if bb.label != "person"
]
list(map(lambda bb: draw_bb(img, bb), bbs_to_draw))
list(
map(
lambda human: draw_bb(
img, human.bounding_box, actions[human.uid][0].name
if human.uid in actions else "None"),
image_content.humans))
img = cv2.resize(img, (1280, 720))
# cv2.imshow('webcam', img)
cv2.imwrite(
os.path.join(
get_create_path(
"/content/drive/My Drive/be proj alternate code/Check3/input1/img_out"
), "{}.png".format(str(frame_nr).zfill(5))), img)
# cv2.imwrite(os.path.join(get_create_path(
# "/media/disks/beta/records/with_visualizations/2019_03_13_Freilichtmuseum_Dashcam_Test_FASTMODE"),
# "{}.jpg".format(str(frame_nr).zfill(5))), img)
key = cv2.waitKey(1)
if key & 0xFF == ord('q'):
break
fps_tracker.print_fps()