# raw_file = os.path.join(get_trendytukan_drive_path(), "datasets/CREMI/official_test_samples/sample_{}+_padded_20160601.hdf".format(sample))
#
# import h5py
# GT_mask_file = os.path.join(get_trendytukan_drive_path(), "datasets/CREMI/alignment_experiments/sample_{}+_GT_mask.hdf".format(sample))
# mask_inner_path = "volumes/labels/mask"
# GT_box = np.zeros(padded_shape, dtype="uint8")
# GT_box[slice_original_pad] = 1
#
# with h5py.File(GT_mask_file, 'w') as f:
# f[mask_inner_path] = GT_box
#
from cremi_tools.alignment import backalign_segmentation
from cremi_tools.alignment.backalign import bounding_boxes
mask_GT_path = os.path.join(
get_trendytukan_drive_path(),
"datasets/CREMI/alignment_experiments/sample_{}_aligned.hdf".format(
sample))
# mask_GT_path = os.path.join(get_hci_home_path(), "sampleA+_gt.h5")
mask_GT = vigra.readHDF5(mask_GT_path,
"volumes/labels/mask")[bounding_boxes[sample]]
out_file = os.path.join(
get_trendytukan_drive_path(),
"datasets/CREMI/alignment_experiments/sample_{}_backaligned.hdf".format(
sample))
backalign_segmentation(sample,
mask_GT,
out_file,
key="volumes/labels/mask",
postprocess=False)
from segmfriends.utils.various import starmap_with_kwargs
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--exp_name', type=str, default="SSBMExperiment") #DebugExp
parser.add_argument('--project_directory', default="projects/spectral_compare", type=str)
# parser.add_argument('--project_directory', default="projects/agglo_cluster_compare/quadxeon5_results", type=str)
# parser.add_argument('--project_directory', default="../quadxeon5_scratch/projects/agglo_cluster_compare", type=str)
args = parser.parse_args()
exp_name = args.exp_name
project_dir = os.path.join(get_trendytukan_drive_path(), args.project_directory)
# project_dir = os.path.join(get_hci_home_path(), "../quadxeon5_scratch", args.project_directory)
fixed_kargs = {
"experiment_name": exp_name,
"project_directory": project_dir,
}
# Select experiment and plot results:
experiment = SSBM_experiments.get_experiment_by_name(exp_name)(fixed_kwargs=fixed_kargs)
experiment.make_plots(project_dir)
# Add missing package-paths
import long_range_compare
from long_range_compare.data_paths import get_hci_home_path, get_trendytukan_drive_path
import numpy as np
import json
import os
if __name__ == '__main__':
# Create dictionary:
results_collected = {}
results_dir = os.path.join(get_trendytukan_drive_path(), 'datasets/cityscape/data/gtFine_trainvaltest/evaluationResults/eval_out')
result_matrix = []
scores_collected = []
all_agglo_type = [' ']
def assign_color(value, good_thresh, bad_thresh, nb_flt, best="lowest"):
if best == "lowest":
if value < good_thresh:
return '{{\color{{ForestGreen}} {num:.{prec}f} }}'.format(prec=nb_flt, num=value)
if value > good_thresh and value < bad_thresh:
return '{{\color{{Orange}} {num:.{prec}f} }}'.format(prec=nb_flt, num=value)
if value > bad_thresh:
return '{{\color{{Red}} {num:.{prec}f} }}'.format(prec=nb_flt, num=value)
elif best == "highest":
if value > good_thresh:
# Original pad for GASP paper:
# slice_GT_mask = (slice(36, -37), slice(890, -890), slice(890, -890))
# # With the embedding we need at least a padding of (2, 240, 240) in the original res (big_pad_version)
# slice_GT_mask = (slice(32, -33), slice(580, -580), slice(580, -580))
# This is almost just a box around the raw data (raw_mask)
slice_GT_mask = (slice(5, -5), slice(180, -180), slice(180, -180))
padded_shape = (200, 3072, 3072)
for sample in ["A", "B", "C"]:
# test_sample_path = os.path.join(get_trendytukan_drive_path(), "datasets/CREMI/constantin_affs/test_samples/sample{}.h5".format(sample))
raw_file = os.path.join(get_trendytukan_drive_path(), "datasets/CREMI/padded_data/sample_{}_padded_20160501.hdf".format(sample))
out_file = os.path.join(get_trendytukan_drive_path(), "datasets/CREMI/padded_data/full_aligned_samples/sample_{}_aligned_plus_raw_mask.hdf".format(sample))
import h5py
GT_file = os.path.join(get_trendytukan_drive_path(), "datasets/CREMI/padded_data/sample_{}_padded_20160501.hdf".format(sample))
GT_inner_path = "volumes/labels/neuron_ids"
from cremi_tools.alignment import realign
realign(raw_file,
sample,
out_file,
labels_file=GT_file,
labels_key=GT_inner_path)
from segmfriends.utils.various import starmap_with_kwargs
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--exp_name', type=str,
default="FullTestSamples") #DebugExp
parser.add_argument('--project_directory',
default="projects/agglo_cluster_compare",
type=str)
# TODO: option to pass some other fixed kwargs and overwrite it...?
args = parser.parse_args()
exp_name = args.exp_name
proj_dir = os.path.join(get_trendytukan_drive_path(),
args.project_directory)
fixed_kargs = {
"experiment_name":
exp_name,
"project_directory":
proj_dir,
"configs_dir_path":
os.path.join(
get_hci_home_path(),
"pyCharm_projects/longRangeAgglo/experiments/cremi/configs")
}
# Select experiment and load data:
experiment = cremi_experiments.get_experiment_by_name(exp_name)(
def run_agglo(problem_name=None, update_rule='mean', CLC=False):
saving_path = os.path.join(get_trendytukan_drive_path(), "projects/agglo_cluster_compare/bigFruitFlyGraphs/scores")
check_dir_and_create(saving_path)
saving_path_2 = os.path.join(saving_path,
problem_name.replace(".txt", ''))
check_dir_and_create(saving_path_2)
result_file_path = os.path.join(saving_path_2,
"{}_{}.json".format(update_rule, CLC))
if os.path.exists(result_file_path):
print(result_file_path)
print("Skip agglo ", update_rule, CLC, problem_name)
return
uvIds = all_data[problem_name][:,:2].astype('uint64')
edge_weights = all_data[problem_name][:, 2].astype('float64')
print("Building graph")
graph = UndirectedGraph(uvIds.max())
graph.insertEdges(uvIds)
# Run agglo:
tick = time.time()
print("Start agglo ", update_rule, CLC, problem_name)
try:
nodeSeg, _ = runGreedyGraphEdgeContraction(graph, edge_weights, update_rule=update_rule, add_cannot_link_constraints=CLC)
except RuntimeError:
print("Nifty Exception on {} {} {}!".format(problem_name, update_rule, CLC))
tock = time.time()
edge_labels = graph.nodesLabelsToEdgeLabels(nodeSeg)
MC_energy = (edge_weights * edge_labels).sum()
print(tock-tick)
print(MC_energy)
new_results = {}
new_results[update_rule] = {}
new_results[update_rule][CLC] = {}
new_results[update_rule][CLC]['MC_energy'] = MC_energy
new_results[update_rule][CLC]['runtime'] = tock - tick
with global_lock:
# if os.path.exists(result_file_path):
# try:
# with open(result_file_path, 'r') as f:
# result_dict = json.load(f)
# except Exception:
# result_dict = {}
# print("Exception raised on {}, {}!!".format(problem_name, update_rule) )
# else:
# result_dict = {}
#
# result_dict = recursive_dict_update(new_results, result_dict)
with open(result_file_path, 'w') as f:
try:
json.dump(new_results, f, indent=4, sort_keys=True)
except Exception:
print("Exception again!")
print("saved")
# path_file = os.path.join(get_trendytukan_drive_path(), DATA_DIR, problem_name)
# print("Loading data")
# my_data = np.genfromtxt(path_file, delimiter=' ')
# print("Writing...")
# vigra.writeHDF5(my_data, path_file.replace(".txt", ".h5"), 'data')
all_data = {}
for problem_name in ["large_problem_L4.txt",
"large_problem_L3.txt",
"large_problem_L2.txt",
"large_problem_L1.txt",
# "large_problem_L0.txt"
]:
path_file = os.path.join(get_trendytukan_drive_path(), DATA_DIR, problem_name)
print("Loading data")
# my_data = np.genfromtxt(path_file, delimiter=' ')
# print("Writing...")
# vigra.writeHDF5(my_data, path_file.replace(".txt", ".h5"), 'data')
all_data[problem_name] = vigra.readHDF5(path_file.replace(".txt", ".h5"), 'data')
def run_agglo(problem_name=None, update_rule='mean', CLC=False):
saving_path = os.path.join(get_trendytukan_drive_path(), "projects/agglo_cluster_compare/bigFruitFlyGraphs/scores")
check_dir_and_create(saving_path)
saving_path_2 = os.path.join(saving_path,
problem_name.replace(".txt", ''))
# Add missing package-paths
import long_range_compare
import os
from long_range_compare.data_paths import get_hci_home_path, get_trendytukan_drive_path
from shutil import copyfile
result_root_dir = os.path.join(get_trendytukan_drive_path(), "datasets/cityscape/data/gtFine_trainvaltest/out")
collected_result_dir = os.path.join(get_trendytukan_drive_path(), "datasets/cityscape/data/gtFine_trainvaltest/out/COLLECTED")
original_images_dir = os.path.join(get_trendytukan_drive_path(), "datasets/cityscape/data/leftImg8bit_trainvaltest/leftImg8bit/val")
image_name = 'munster/munster_000167_000019_leftImg8bit_combine.inst.jpg'
image_name = 'frankfurt/frankfurt_000001_020693_leftImg8bit_combine.inst.jpg'
# image_name = "munster/munster_000167_000019_leftImg8bit_combine.inst.jpg"
# image_name = 'frankfurt/frankfurt_000001_015768_leftImg8bit_combine.inst.jpg'
ignored = ["COLLECTED", "MAX_bk_mask", "MEAN_bk_mask", "MEAN_constr_bk_mask"]
for subdir, dirs, files in os.walk(result_root_dir):
# Copy original image:
original_image_path = os.path.join(original_images_dir, image_name.replace("_combine.inst.jpg", ".png"))
copyfile(original_image_path, os.path.join(collected_result_dir, image_name.replace("_combine.inst.jpg", ".png")))
for agglo_type in dirs:
# if agglo_type in ignored or ("clean" not in agglo_type and "ORIG" not in agglo_type):
# FOUND CROP SLICES:
# A+ (slice(36, 163, None), slice(1154, 2753, None), slice(934, 2335, None))
# B+ (slice(36, 163, None), slice(1061, 2802, None), slice(1254, 4009, None))
# C+ (slice(36, 163, None), slice(980, 2443, None), slice(1138, 2569, None))
# for sample in ["A+"]:
for sample in ["A+", "B+", "C+"]:
# Load GT mask:
print("Loading")
mask_inner_path = "volumes/labels/mask"
# source_path_big_pad = os.path.join(get_trendytukan_drive_path(),
# "datasets/CREMI/official_test_samples/full_aligned_samples/sample_{}_aligned_plus_big_pad.hdf".format(sample))
source_path_raw_mask = os.path.join(get_trendytukan_drive_path(),
"datasets/CREMI/official_test_samples/full_aligned_samples/sample_{}_aligned_plus_raw_mask.hdf".format(
sample))
# source_path = os.path.join(get_trendytukan_drive_path(),
# "datasets/CREMI/official_test_samples/full_aligned_samples/sample_{}_aligned.hdf".format(sample))
from segmfriends.utils.various import readHDF5, writeHDF5
print("Reading...")
mask_big_pad = readHDF5(source_path_raw_mask, mask_inner_path)
print("Max big pad: ", mask_big_pad.max())
# mask_border = mask_big_pad > 10
mask_big_pad = (mask_big_pad == 1).astype('uint16')
# print(mask_GT.shape)
print("Find crop")
# crop_slice = get_gt_bounding_box(mask_big_pad)
