def localizer_train(ibs, species_list=None, **kwargs):
from pydarknet import Darknet_YOLO_Detector
data_path = ibs.export_to_xml(species_list=species_list, **kwargs)
output_path = join(ibs.get_cachedir(), 'training', 'localizer')
ut.ensuredir(output_path)
dark = Darknet_YOLO_Detector()
model_path = dark.train(data_path, output_path)
del dark
return model_path
def train_part_detector():
"""
Problem:
healthy sharks usually have a mostly whole body shot
injured sharks usually have a close up shot.
This distribution of images is likely what the injur-shark net is picking up on.
The goal is to train a detector that looks for things that look
like the distribution of injured sharks.
We will run this on healthy sharks to find the parts of
"""
import wbia
ibs = wbia.opendb('WS_ALL')
imgset = ibs.imagesets(text='Injured Sharks')
injured_annots = imgset.annots[0] # NOQA
# config = {
# 'dim_size': (224, 224),
# 'resize_dim': 'wh'
# }
from pydarknet import Darknet_YOLO_Detector
data_path = ibs.export_to_xml()
output_path = join(ibs.get_cachedir(), 'training', 'localizer')
ut.ensuredir(output_path)
dark = Darknet_YOLO_Detector()
results = dark.train(data_path, output_path)
del dark
localizer_weight_path, localizer_config_path, localizer_class_path = results
classifier_model_path = ibs.classifier_train()
labeler_model_path = ibs.labeler_train()
output_path = join(ibs.get_cachedir(), 'training', 'detector')
ut.ensuredir(output_path)
ut.copy(localizer_weight_path, join(output_path, 'localizer.weights'))
ut.copy(localizer_config_path, join(output_path, 'localizer.config'))
ut.copy(localizer_class_path, join(output_path, 'localizer.classes'))
ut.copy(classifier_model_path, join(output_path, 'classifier.npy'))
ut.copy(labeler_model_path, join(output_path, 'labeler.npy'))
def run_tests():
# # Build module list and run tests
# import sys
# ut.change_term_title('RUN pydarknet TESTS')
# exclude_doctests_fnames = set([
# ])
# exclude_dirs = [
# '_broken', 'old', 'tests', 'timeits',
# '_scripts', '_timeits', '_doc', 'notebook',
# ]
# dpath_list = ['pydarknet']
# doctest_modname_list = ut.find_doctestable_modnames(
# dpath_list, exclude_doctests_fnames, exclude_dirs)
# for modname in doctest_modname_list:
# exec('import ' + modname, globals())
# module_list = [sys.modules[name] for name in doctest_modname_list]
# nPass, nTotal, failed_cmd_list = ut.doctest_module_list(module_list)
# if nPass != nTotal:
# return 1
# else:
# return 0
from pydarknet import Darknet_YOLO_Detector
# dark = Darknet_YOLO_Detector()
# voc_path = '/media/hdd/jason/yolo/LearningData'
# weight_path = '/media/hdd/jason/yolo/weights'
# ut.ensuredir(weight_path)
# dark.train(voc_path, weight_path)
config_filepath = '/media/hdd/jason/yolo/weights/detect.yolo.12.cfg'
weight_filepath = '/media/hdd/jason/yolo/weights/detect.yolo.12.weights'
# config_filepath = '/Users/bluemellophone/Desktop/detect.yolo.12.cfg'
# weight_filepath = '/Users/bluemellophone/Desktop/detect.yolo.12.weights'
dark = Darknet_YOLO_Detector(config_filepath=config_filepath, weight_filepath=weight_filepath)
import cv2
from os.path import abspath, join, basename
input_gpath_list = [
abspath(join('_test', 'test_%05d.jpg' % (i, )))
for i in range(1, 76)
]
# input_gpath_list = input_gpath_list[:2]
temp_path = abspath('temp')
ut.ensuredir(temp_path)
results_list1 = list(dark.detect(input_gpath_list, grid=False))
results_list2 = list(dark.detect(input_gpath_list, grid=True))
zipped = zip(results_list1, results_list2)
for (filename, result_list1), (filename2, result_list2) in zipped:
print(filename)
image = cv2.imread(filename)
for result in result_list1:
if result['confidence'] < 0.2:
continue
print(' Found 1: %r' % (result, ))
xtl = int(result['xtl'])
ytl = int(result['ytl'])
xbr = int(result['xtl'] + result['width'])
ybr = int(result['ytl'] + result['height'])
cv2.rectangle(image, (xtl, ytl), (xbr, ybr), (255, 140, 0), 5)
for result in result_list2:
if result['confidence'] < 0.2:
continue
print(' Found 2: %r' % (result, ))
xtl = int(result['xtl'])
ytl = int(result['ytl'])
xbr = int(result['xtl'] + result['width'])
ybr = int(result['ytl'] + result['height'])
cv2.rectangle(image, (xtl, ytl), (xbr, ybr), (0, 140, 255), 3)
for result in result_list1:
if result['confidence'] < 0.2:
continue
xtl = int(result['xtl'])
ytl = int(result['ytl'])
xbr = int(result['xtl'] + result['width'])
ybr = int(result['ytl'] + result['height'])
cv2.rectangle(image, (xtl, ytl), (xbr, ybr), (255, 140, 0), 1)
temp_filepath = join(temp_path, basename(filename))
cv2.imwrite(temp_filepath, image)