class Runner:
def __init__(self):
self.loader = UADetracLoader()
self.preprocess = PreprocessingModule()
self.network = UNet()
self.cluster = ClusterModule()
self.index = IndexingModule()
def run(self):
"""
Steps:
1. Load the data
2. Preprocess the data
3. Train the network
4a. Cluster the data
4b. Postprocess the data
5a. Generate compressed form
5b. Generate indexes and preform CBIR
:return: ???
"""
import time
st = time.time()
# 1. Load the image
images = self.loader.load_cached_images()
labels = self.loader.load_cached_labels()
vehicle_labels = labels['vehicle']
video_start_indices = self.loader.get_video_start_indices()
print("Done loading images in", time.time() - st, "(sec)")
# 2. Begin preprocessing
st = time.time()
segmented_images = self.preprocess.run(images, video_start_indices)
print("Done with background subtraction in", time.time() - st, "(sec)")
self.preprocess.saveSegmentedImages()
st = time.time()
self.network.train(images, segmented_images)
final_compressed_images, final_segmented_images = self.network.execute(
)
print("Done training the main network in", time.time() - st, "(sec)")
st = time.time()
cluster_labels = self.cluster.run(final_compressed_images)
print("Done clustering in", time.time() - st, "(sec)")
st = time.time()
self.index.train(images, final_segmented_images, vehicle_labels)
def load_jnet_results(images,
model_directory,
segmented_images_directory=None):
"""
we need to load the segmented images instead of the original ones
:return:
"""
## need to check if segmented images exist
if segmented_images_directory is None:
## load the model
network = UNet()
## execute the model on the images of interest
_, level4_outputs = network.execute(images, load_dir=model_directory)
## we should overlap the images
level4_overlapped_outputs = util_custom.overlap(images, level4_outputs)
return level4_overlapped_outputs
else:
return np.load(segmented_images_directory)
def __init__(self):
self.loader = UADetracLoader()
self.preprocess = PreprocessingModule()
self.network = UNet()
self.cluster = ClusterModule()
self.index = IndexingModule()
segmented_images = self.preprocess.run(images, video_start_indices)
print("Done with background subtraction in", time.time() - st, "(sec)")
self.preprocess.saveSegmentedImages()
st = time.time()
self.network.train(images, segmented_images)
final_compressed_images, final_segmented_images = self.network.execute(
)
print("Done training the main network in", time.time() - st, "(sec)")
st = time.time()
cluster_labels = self.cluster.run(final_compressed_images)
print("Done clustering in", time.time() - st, "(sec)")
st = time.time()
self.index.train(images, final_segmented_images, vehicle_labels)
if __name__ == "__main__":
# 0. Initialize the modules
loader = UADetracLoader()
preprocess = PreprocessingModule()
network = UNet()
import time
st = time.time()
# 1. Load the images (cached images is fine)
images = loader.load_cached_images()
labels = loader.load_cached_labels()
video_start_indices = loader.get_video_start_indices()
def train_conservative(train_images, preprocess, name):
segmented_images = preprocess.run(train_images, None, load=True)
model = UNet()
model.train(train_images, segmented_images, save_name=name)
return
if __name__ == "__main__":
# %%
import os
import time
from loaders.uadetrac_loader import UADetracLoader
from eva_storage.preprocessingModule import PreprocessingModule
from eva_storage.UNet import UNet
from eva_storage.postprocessingModule import PostprocessingModule
from logger import Logger
loader = UADetracLoader()
preprocess = PreprocessingModule()
bare_model = UNet()
postprocess = PostprocessingModule()
logger = Logger()
st = time.time()
# 1. Load the images (cached images is fine)
images = loader.load_cached_images()
labels = loader.load_cached_labels()
video_start_indices = loader.get_video_start_indices()
print(f"Done loading images in {time.time() - st} (sec)")
directory_begin = '/nethome/jbang36/eva_jaeho/data/models/'
"""
model_names = ['history20_dist2thresh300',
'history20_dist2thresh300_bloat_lvl2',
'history20_dist2thresh300_bloat_lvl3',