def __init__(self,
model_file_name=model_file_name,
verbose=False,
i_mag_lim=30):
self.i_mag_lim = i_mag_lim
self.model_file_name = model_file_name
self.get_model(verbose=False)
self.scarlet_param = btk_utils.Scarlet_resid_params(detect_coadd=True)
self.iter_scarlet_param = btk_utils.Scarlet_resid_params(
detect_centers=False)
def __init__(self,
model_file_name=model_file_name,
model_name=model_name,
stretch=2731,
input_model_mapping=True,
input_pull=True,
verbose=False):
self.model_file_name = model_file_name
self.model_name = model_name
self.get_model(verbose=False)
self.scarlet_param = btk_utils.Scarlet_resid_params()
self.norm = [0., 1., 0, 1.]
self.stretch = stretch
self.input_model_mapping = input_model_mapping
def detection_coadd(Args):
"""Test performance for btk input blends"""
norm = [
1.9844158727667542, 413.83759806375525, 51.2789974336363,
1038.4760551905683
]
count = 15 #4000 # 40000
catalog_name = os.path.join(DATA_PATH, 'OneDegSq.fits')
# Define parameters for mrcnn model with btk here
resid_model = btk_utils.Resid_btk_model(Args.model_name,
Args.model_path,
MODEL_DIR,
training=False,
images_per_gpu=1)
# Load parametrs for dataset and load model
meas_params = btk_utils.Scarlet_resid_params(detect_coadd=True)
resid_model.make_resid_model(catalog_name,
count=count,
max_number=2,
norm_val=norm,
meas_params=meas_params)
results = []
# np.random.seed(0)
for im_id in range(count):
iter_detected, sep_detected, true = resid_model.get_detections(im_id)
for i in range(len(true)):
it_det, it_undet, it_spur = btk.compute_metrics.evaluate_detection(
iter_detected[i], true[i])
# print(it_det, it_undet, it_spur)
if len(sep_detected[i]) == 0:
sep_det, sep_undet, sep_spur = 0, len(true[i]), 0
else:
unique_sep_det_cent = np.unique(sep_detected[i], axis=0)
sep_det, sep_undet, sep_spur = btk.compute_metrics.evaluate_detection(
unique_sep_det_cent, true[i])
# print(sep_det, sep_undet, sep_spur)
results.append([
len(true[i]), it_det, it_undet, it_spur, sep_det, sep_undet,
sep_spur
])
arr_results = np.array(results).T
print("Results: ", np.sum(arr_results, axis=1))
save_file_name = f"sep_det_results_2gal_coadd_temp.txt"
np.savetxt(save_file_name, arr_results)