import numpy as np
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
from framework import utils
from framework import viz_utils
#################################################################################################
# CONFIG #
#################################################################################################
original_execution_dir = r'/home/omer/orchards_ws/results/global_ekf_updates'
#################################################################################################
if __name__ == '__main__':
execution_dir = utils.create_new_execution_folder('global_ekf_updates_post_processing')
ekf_errors = {}
ekf_with_aerial_update_errors = {}
fig = plt.figure(figsize=(8.5, 11))
for experiment_idx, experiment_name in enumerate(sorted(os.listdir(original_execution_dir))):
with open(os.path.join(original_execution_dir, experiment_name, 'experiment_summary.json')) as f:
experiment_summary = json.load(f)
with open(experiment_summary['data_sources']['ugv_poses_path']) as f:
ugv_poses_path = json.load(f)
relevant_update_index = experiment_summary['data_sources']['relevant_update_index']
ekf_df = pd.read_csv(experiment_summary['results']['1']['ekf_pose_path'], index_col=0)
ekf_with_aerial_update_df = pd.read_csv(experiment_summary['results']['1']['ekf_with_aerial_update_path'], index_col=0)
update_time = ekf_with_aerial_update_df.loc[(ekf_with_aerial_update_df['pose.pose.position.x'].diff().abs() > 0.5) |
(ekf_with_aerial_update_df['pose.pose.position.y'].diff().abs() > 0.5)].index[0]
ekf_before_update_df = ekf_with_aerial_update_df[ekf_with_aerial_update_df.index < update_time]
ekf_without_update_df = ekf_df[ekf_df.index > update_time]
markers_locations_json_paths = [
dji.snapshots_60_meters_markers_locations_json_path,
dji.snapshots_80_meters_markers_locations_json_path
]
fork_shaped = False
S_shaped = False
random_shaped = True
if __name__ == '__main__':
if image_path is not None:
raise NotImplemented
group_idx = 1
for snapshots_group, markers_locations_json_path in zip(
snapshots_groups, markers_locations_json_paths):
execution_dir = utils.create_new_execution_folder(
'trajectory_tagging_%d' % group_idx)
group_idx += 1
with open(markers_locations_json_path) as f:
markers_locations = json.load(f)
for key, data_descriptor in snapshots_group.items():
if relevant_keys is not None:
if key not in relevant_keys:
continue
points = markers_locations[key]
image = cv2.imread(data_descriptor.path)
map_image = maps_generation.generate_canopies_map(image)
(upper_left_x,
upper_left_y), (lower_right_x,
lower_right_y) = cv_utils.get_bounding_box(
map_image,
points,
# Stop recording output bag
# ros_utils.stop_recording_bags()
# Kill RVIZ
if launch_rviz:
ros_utils.kill_rviz()
if __name__ == '__main__':
from content.data_pointers.lavi_april_18.dji import trunks_detection_results_dir as td_results_dir
from content.data_pointers.lavi_april_18 import base_resources_path, base_raw_data_path
from content.data_pointers.lavi_april_18 import orchard_topology
execution_dir = utils.create_new_execution_folder('amcl_video')
td_exp_name_for_map = 'trunks_detection_on_apr_16-55-1'
with open(
os.path.join(td_results_dir, td_exp_name_for_map,
'experiment_summary.json')) as f:
td_summary_for_map = json.load(f)
image_path = td_summary_for_map['data_sources']
map_semantic_trunks = td_summary_for_map['results']['1']['semantic_trunks']
pixel_to_meter_ratio_for_map = calibration.calculate_pixel_to_meter(
td_summary_for_map['results']['1']['optimized_grid_dim_x'],
td_summary_for_map['results']['1']['optimized_grid_dim_y'],
orchard_topology.measured_row_widths,
orchard_topology.measured_intra_row_distances)
map_resolution = (1.0 / pixel_to_meter_ratio_for_map) * 1.5
experiment = AmclVideoExperiment(name='amcl_video',
from content.data_pointers.lavi_april_18.dji import trunks_detection_results_dir as td_results_dir
from content.data_pointers.lavi_april_18.dji import selected_trunks_detection_experiments as selected_td_experiments
from content.data_pointers.lavi_april_18.dji import snapshots_80_meters_markers_locations_json_path as markers_json_path
elif setup == 'nov1':
from content.data_pointers.lavi_november_18.dji import trunks_detection_results_dir as td_results_dir
from content.data_pointers.lavi_november_18.dji import plot1_selected_trunks_detection_experiments as selected_td_experiments
from content.data_pointers.lavi_november_18.dji import plot1_snapshots_80_meters_markers_locations_json_path as markers_json_path
elif setup == 'nov2':
from content.data_pointers.lavi_november_18.dji import trunks_detection_results_dir as td_results_dir
from content.data_pointers.lavi_november_18.dji import plot2_selected_trunks_detection_experiments as selected_td_experiments
from content.data_pointers.lavi_november_18.dji import plot2_snapshots_80_meters_markers_locations_json_path as markers_json_path
else:
raise NotImplementedError
if __name__ == '__main__':
execution_dir = utils.create_new_execution_folder('image_alignment_%s' %
setup)
results_df = pd.DataFrame(
index=map(lambda c: '%s__%s' % (c[0], c[1]),
combinations(selected_td_experiments, 2)),
columns=[
'by_trunks/mse', 'by_orb/mse', 'by_markers/mse', 'by_trunks/ssim',
'by_orb/ssim', 'by_markers/ssim'
])
with open(markers_json_path) as f:
all_markers_locations = json.load(f)
for experiment1, experiment2 in combinations(selected_td_experiments, r=2):
with open(
os.path.join(td_results_dir, experiment1,
'experiment_summary.json')) as f:
td_summary1 = json.load(f)
with open(
from framework import utils
from content.experiments.jackal_gmapping import JackalGmapping
#################################################################################################
# CONFIG #
#################################################################################################
ugv_bag_name = '18-24-26'
periodic_map_saving = False
setup = 'apr' # apr / nov / lab
#################################################################################################
if setup == 'apr':
from content.data_pointers.lavi_april_18.jackal import jackal_18 as ugv_pointers
elif setup == 'nov':
raise NotImplementedError
elif setup == 'lab':
raise NotImplementedError
if __name__ == '__main__':
execution_dir = utils.create_new_execution_folder('jackal_gmapping')
bag_descriptor = ugv_pointers[ugv_bag_name]
experiment = JackalGmapping(name='gmapping_on_apr_%s' % ugv_bag_name,
data_sources=bag_descriptor.path,
working_dir=execution_dir)
experiment.run(repetitions=1, periodic_map_saving=periodic_map_saving)
import os
import pandas as pd
import matplotlib.pyplot as plt
from framework import utils
from framework import viz_utils
#################################################################################################
# CONFIG #
#################################################################################################
original_experiment_dir = r'/home/omer/temp/20190316-152308_apr_icp/20190316-152308_icp_snapshots_for_fork_trajectory_on_15-08-1'
repetition_ids = [1, 2, 3]
#################################################################################################
if __name__ == '__main__':
execution_dir = utils.create_new_execution_folder('icp_post_processing')
for repetition_id in repetition_ids:
plt.figure()
icp_df = pd.read_csv(os.path.join(original_experiment_dir,
str(repetition_id),
'canopies_icp_results.csv'),
index_col=0)
viz_utils.plot_2d_trajectory([
icp_df.loc[:, [
'icp_pose_x[%d]' % repetition_id,
'icp_pose_y[%d]' % repetition_id
]]
])
plt.savefig(os.path.join(execution_dir, 'icp_%d.jpg' % repetition_id))
return convergence_to_band_ratio, average_first_time_in_band, average_in_band_ratio, average_std, average_mean
def plot_canopies_vs_trunks(canopies_vector, trunks_vector, canopies_stds, trunks_stds, x_label=None, y_label=None):
viz_utils.plot_line_with_sleeve(canopies_vector, 2 * canopies_stds, 'green')
viz_utils.plot_line_with_sleeve(trunks_vector, 2 * trunks_stds, 'sienna')
plt.autoscale(enable=True, axis='x', tight=True)
plt.ylim(bottom=0)
if x_label is not None:
plt.xlabel(x_label)
if y_label is not None:
plt.ylabel(y_label)
if __name__ == '__main__':
execution_dir = utils.create_new_execution_folder('amcl_simulation_post_processing_%s' % os.path.basename(original_execution_dirname))
results_list = []
if plot:
fig = plt.figure(figsize=(8.5, 11))
ax_errors = None
ax_covariance_norms = None
full_experiment_names = filter(lambda name: name.find('png') == -1, os.listdir(os.path.join(base_results_path, 'amcl', original_execution_dirname)))
sorted_full_experiment_names = []
for trajectory_name in ['narrow_row', 'wide_row', 's_patrol', 'u_turns', 'tasks_and_interrupts']:
for full_experiment_name in full_experiment_names:
if full_experiment_name.find(trajectory_name) != -1:
sorted_full_experiment_names.append(full_experiment_name)
for experiment_idx, experiment_name in enumerate(sorted_full_experiment_names):
with open(os.path.join(base_results_path, 'amcl', original_execution_dirname, experiment_name, 'experiment_summary.json')) as f:
experiment_summary = json.load(f)
trajectory_name = experiment_summary['metadata']['trajectory_name']
#################################################################################################
ugv_bag_name = '18-24-26'
icp_experiment_path = os.path.join(config.base_results_path, 'jackal_icp')
setup = 'apr' # apr / nov / lab
#################################################################################################
if setup == 'apr':
from content.data_pointers.lavi_april_18.jackal import jackal_18 as ugv_pointers
elif setup == 'nov':
raise NotImplementedError
elif setup == 'lab':
raise NotImplementedError
if __name__ == '__main__':
execution_dir = utils.create_new_execution_folder('jackal_baseline')
bag_descriptor = ugv_pointers[ugv_bag_name]
odom = ros_utils.bag_to_dataframe(
bag_descriptor.path,
topic=r'/jackal_velocity_controller/odom',
fields=['pose.pose.position.x', 'pose.pose.position.y'])
ekf = ros_utils.bag_to_dataframe(
bag_descriptor.path,
topic=r'/odometry/filtered',
fields=['pose.pose.position.x', 'pose.pose.position.y'])
icp_bag_path = os.path.join(icp_experiment_path, '1',
'icp_on_apr_%s_output.bag' % ugv_bag_name)
icp = ros_utils.bag_to_dataframe(icp_bag_path,
topic=r'/scanmatcher_pose',
fields=['x', 'y'])
from framework import config
from content.data_pointers.lavi_april_18 import dji
image_path = r'/home/omer/orchards_ws/data/lavi_apr_18/panorama/DJI_0178_afternoon_good_stitch_full_movie_cropped.jpg' # ignored if None
map_name = 'panorama_cropped' # ignored if None
relevant_keys = None # if None, take all
snapshots_groups = [dji.snapshots_60_meters, dji.snapshots_80_meters]
markers_locations_json_paths = [
dji.snapshots_60_meters_markers_locations_json_path,
dji.snapshots_80_meters_markers_locations_json_path
]
if __name__ == '__main__':
if image_path is not None:
execution_dir = utils.create_new_execution_folder('map_generation')
image = cv2.imread(image_path)
map_image = maps_generation.generate_canopies_map(image)
ros_utils.save_image_to_map(map_image,
resolution=config.top_view_resolution,
map_name='%s_map' % map_name,
dir_name=execution_dir)
else:
group_idx = 1
for snapshots_group, markers_locations_json_path in zip(
snapshots_groups, markers_locations_json_paths):
execution_dir = utils.create_new_execution_folder(
'map_generation_%d' % group_idx)
group_idx += 1
with open(markers_locations_json_path) as f:
markers_locations = json.load(f)
from content.data_pointers.lavi_november_18.dji import plot1_snapshots_80_meters as snapshots
from content.data_pointers.lavi_november_18.orchard_topology import plot1_pattern as plot_pattern
elif setup == 'nov2':
from content.data_pointers.lavi_november_18.dji import plot2_snapshots_80_meters as snapshots
from content.data_pointers.lavi_november_18.orchard_topology import plot2_pattern as plot_pattern
elif setup == 'nov3':
from content.data_pointers.lavi_november_18.dji import plot3_snapshots_80_meters as snapshots
from content.data_pointers.lavi_november_18.orchard_topology import plot3_pattern as plot_pattern
elif setup == 'nov4':
from content.data_pointers.lavi_november_18.dji import plot4_snapshots_80_meters as snapshots
from content.data_pointers.lavi_november_18.orchard_topology import plot4_pattern as plot_pattern
image_keys = ['15-17-1', '15-18-3', '15-19-1']
if __name__ == '__main__':
execution_dir = utils.create_new_execution_folder('trunks_tagging')
for image_key in image_keys:
summary = {}
summary['results'] = {}
summary['results'][0] = {}
summary['results'][0]['semantic_trunks'] = {}
summary['metadata'] = {}
summary['metadata']['image_key'] = image_key
summary['metadata']['altitude'] = altitude
data_descriptor = snapshots[image_key]
summary['data_sources'] = data_descriptor.path
image = cv2.imread(data_descriptor.path)
for i in range(plot_pattern.shape[0]):
for j in range(plot_pattern.shape[1]):
if plot_pattern[(i, j)] == -1:
continue
from framework import utils
from computer_vision import calibration
from content.experiments.global_ekf_updates import GlobalEkfUpdatesExperiment
#################################################################################################
# CONFIG #
#################################################################################################
#################################################################################################
from content.data_pointers.lavi_november_18.jackal import jackal_18
from content.data_pointers.lavi_november_18.dji import plot1_snapshots_80_meters_ugv_poses_path, trunks_detection_results_dir
from content.data_pointers.lavi_november_18 import orchard_topology
if __name__ == '__main__':
execution_dir = utils.create_new_execution_folder('global_ekf_updates')
with open(plot1_snapshots_80_meters_ugv_poses_path) as f:
ugv_poses = json.load(f, object_pairs_hook=OrderedDict)
for relevant_update_index, update_image_key in enumerate(
ugv_poses.keys()[1:]):
with open(
os.path.join(trunks_detection_results_dir,
'trunks_detection_on_nov1_%s' % update_image_key,
'experiment_summary.json')) as f:
td_experiment_summary = json.load(f)
pixel_to_meter = calibration.calculate_pixel_to_meter(
td_experiment_summary['results']['1']['optimized_grid_dim_x'],
td_experiment_summary['results']['1']['optimized_grid_dim_y'],
orchard_topology.plot1_measured_row_widths,
orchard_topology.plot1_measured_intra_row_distances)
resolution = 1.0 / pixel_to_meter
#################################################################################################
# CONFIG #
#################################################################################################
apr_selected_td_experiments = {
'noon': 'trunks_detection_on_apr_15-08-1',
'late_noon': 'trunks_detection_on_apr_15-53-1',
'afternoon': 'trunks_detection_on_apr_16-55-1',
'late_afternoon': 'trunks_detection_on_apr_19-04-1'
}
nov_selected_td_experiment = 'trunks_detection_on_nov1_10-12-1'
#################################################################################################
if __name__ == '__main__':
execution_dir = utils.create_new_execution_folder('contours_comparison')
with open(
os.path.join(apr_td_results_dir,
apr_selected_td_experiments['noon'],
'experiment_summary.json')) as f:
apr_noon_td_summary = json.load(f)
with open(
os.path.join(apr_td_results_dir,
apr_selected_td_experiments['late_noon'],
'experiment_summary.json')) as f:
apr_late_noon_td_summary = json.load(f)
with open(
os.path.join(apr_td_results_dir,
apr_selected_td_experiments['afternoon'],
'experiment_summary.json')) as f:
import os
import cv2
import json
from framework import cv_utils
from framework import utils
from content.data_pointers.lavi_november_18.dji import plot1_snapshots_80_meters as snapshots
from framework import logger
from collections import OrderedDict
_logger = logger.get_logger()
relevant_image_keys = [
'10-26-3', '10-27-1', '10-27-2', '10-28-1', '10-29-1', '10-29-2',
'10-30-1', '10-30-2', '10-31-1', '10-32-1'
]
if __name__ == '__main__':
execution_dir = utils.create_new_execution_folder('ugv_tagging')
ugv_poses = OrderedDict()
for image_key in relevant_image_keys:
image = cv2.imread(snapshots[image_key].path)
height = image.shape[0]
ugv_pose = list(cv_utils.sample_pixel_coordinates(image))
ugv_pose[1] = height - ugv_pose[1]
ugv_poses[image_key] = tuple(ugv_pose)
with open(os.path.join(execution_dir, 'ugv_poses.json'), 'w') as f:
json.dump(ugv_poses, f, indent=4)
source_image_index = 0
setup = 'apr' # apr / nov1
min_area = None
#################################################################################################
if setup == 'apr':
from content.data_pointers.lavi_april_18.dji import trunks_detection_results_dir as td_results_dir
from content.data_pointers.lavi_april_18.dji import selected_trunks_detection_experiments as selected_td_experiments
elif setup == 'nov1':
from content.data_pointers.lavi_november_18.dji import trunks_detection_results_dir as td_results_dir
from content.data_pointers.lavi_november_18.dji import plot1_selected_trunks_detection_experiments as selected_td_experiments
else:
raise NotImplementedError
if __name__ == '__main__':
execution_dir = utils.create_new_execution_folder('canopy_contours_drawer')
with open(os.path.join(td_results_dir, selected_td_experiments[source_image_index], 'experiment_summary.json')) as f:
td_summary = json.load(f)
image = cv2.imread(td_summary['data_sources'])
contours, canopies_mask = segmentation.extract_canopy_contours(image, min_area=min_area)
image_with_contours = image.copy()
cv2.drawContours(image_with_contours, contours, contourIdx=-1, color=(0, 255, 0), thickness=5)
canopies_mask_with_contours = cv2.cvtColor(canopies_mask.copy(), cv2.COLOR_GRAY2BGR)
cv2.drawContours(canopies_mask_with_contours, contours, contourIdx=-1, color=(0, 255, 0), thickness=5)
canopies_mask_with_trunks = cv2.cvtColor(canopies_mask.copy(), cv2.COLOR_GRAY2BGR)
canopies_mask_with_trunks = cv_utils.draw_points_on_image(canopies_mask_with_trunks, td_summary['results']['1']['semantic_trunks'].values(), color=(0, 220, 0))
canopies_mask_with_labeled_trunks = canopies_mask_with_trunks.copy()
for trunk_label, trunk_pose in td_summary['results']['1']['semantic_trunks'].items():
canopies_mask_with_labeled_trunks = cv_utils.put_shaded_text_on_image(canopies_mask_with_labeled_trunks,
label=trunk_label,
location=trunk_pose,
import pandas as pd
import matplotlib.pyplot as plt
from framework import utils
from framework.config import base_results_path
#################################################################################################
# CONFIG #
#################################################################################################
template_matching_experiments_path = os.path.join(base_results_path,
'template_matching',
'template_matching_colored')
#################################################################################################
if __name__ == '__main__':
execution_dir = utils.create_new_execution_folder(
'template_matching_post_processing')
for template_matching_experiment in os.listdir(
template_matching_experiments_path):
errors_df = pd.read_csv(os.path.join(
template_matching_experiments_path, template_matching_experiment,
'errors.csv'),
index_col=0)
errors_df = errors_df.dropna(how='all')
plt.figure()
for method in errors_df.columns:
plt.plot(range(errors_df[method].shape[0]), errors_df[method])
plt.ylim([0, 20])
plt.savefig(
os.path.join(execution_dir,
'%s.jpg' % template_matching_experiment))
import os
from framework import utils
from content.experiments.jackal_icp import JackalIcp
#################################################################################################
# CONFIG #
#################################################################################################
ugv_bag_name = '18-24-26'
setup = 'apr' # apr / nov / lab
#################################################################################################
if setup == 'apr':
from content.data_pointers.lavi_april_18.jackal import jackal_18 as ugv_pointers
elif setup == 'nov':
raise NotImplementedError
elif setup == 'lab':
raise NotImplementedError
if __name__ == '__main__':
execution_dir = utils.create_new_execution_folder('jackal_icp')
bag_descriptor = ugv_pointers[ugv_bag_name]
experiment = JackalIcp(name='icp_on_apr_%s' % ugv_bag_name,
data_sources=bag_descriptor.path,
working_dir=execution_dir)
experiment.run(repetitions=1)
color=(0, 255, 255),
radius=30)
label_idx = start_waypoint_idx
for coordinates in waypoints_coordinates:
trajectory_image = cv_utils.put_shaded_text_on_image(
trajectory_image,
label=chr(label_idx + 65),
location=tuple(np.array(coordinates) - np.array(upper_left)),
color=(0, 255, 255),
offset=(-40, -70))
label_idx += 1
cv2.imwrite(os.path.join(output_file_path), trajectory_image)
if __name__ == '__main__':
execution_dir = utils.create_new_execution_folder('trajectory_update')
with open(
os.path.join(td_results_dir, td_baseline_experiment_name,
'experiment_summary.json')) as f:
td_baseline_summary = json.load(f)
with open(
os.path.join(td_results_dir, td_obstacle_1_experiment_name,
'experiment_summary.json')) as f:
td_obstacle_1_summary = json.load(f)
with open(
os.path.join(td_results_dir, td_obstacle_2_experiment_name,
'experiment_summary.json')) as f:
td_obstacle_2_summary = json.load(f)
baseline_image_path = td_baseline_summary['data_sources']
from framework import config
#################################################################################################
# CONFIG #
#################################################################################################
from content.data_pointers.lavi_april_18.dji import snapshots_80_meters as snapshots
image_key = '15-08-1'
roi_expansion = 2.8
noise_sigma = 0.5
draw_range_circle = False
dummy_resolution = 0.0259837881206
#################################################################################################
if __name__ == '__main__':
execution_dir = utils.create_new_execution_folder('synthetic_scan_drawer')
image = cv2.imread(snapshots[image_key].path)
map_image = maps_generation.generate_canopies_map(image)
center_x, center_y = cv_utils.sample_pixel_coordinates(map_image)
scan_ranges = contours_scan.generate(
map_image,
center_x=center_x,
center_y=center_y,
min_angle=config.synthetic_scan_min_angle,
max_angle=config.synthetic_scan_max_angle,
samples_num=config.synthetic_scan_samples_num,
min_distance=config.synthetic_scan_min_distance,
max_distance=config.synthetic_scan_max_distance,
resolution=dummy_resolution,
r_primary_search_samples=config.
synthetic_scan_r_primary_search_samples,
from content.data_pointers.lavi_november_18.dji import trunks_detection_results_dir as td_results_dir
from content.data_pointers.lavi_november_18.dji import plot1_selected_trunks_detection_experiments as selected_td_experiments
from content.data_pointers.lavi_november_18.orchard_topology import plot1_measured_trunks_perimeters as measured_trunks_perimeters
from content.data_pointers.lavi_november_18.orchard_topology import plot1_measured_row_widths as measured_row_widths
from content.data_pointers.lavi_november_18.orchard_topology import plot1_measured_intra_row_distances as measured_intra_row_distances
from content.data_pointers.lavi_november_18.orchard_topology import plot1_trajectories as trajectories
elif setup == 'nov2':
from content.data_pointers.lavi_november_18.dji import trunks_detection_results_dir as td_results_dir
from content.data_pointers.lavi_november_18.dji import plot2_selected_trunks_detection_experiments as selected_td_experiments
from content.data_pointers.lavi_november_18.orchard_topology import plot2_measured_trunks_perimeters as measured_trunks_perimeters
from content.data_pointers.lavi_november_18.orchard_topology import plot2_measured_row_widths as measured_row_widths
from content.data_pointers.lavi_november_18.orchard_topology import plot2_measured_intra_row_distances as measured_intra_row_distances
from content.data_pointers.lavi_november_18.orchard_topology import plot2_trajectories as trajectories
if __name__ == '__main__':
execution_dir = utils.create_new_execution_folder(description)
for td_experiment in combinations(
selected_td_experiments,
r=2 if different_localization_and_mapping_sources else 1):
td_experiment_name_for_map = td_experiment[0]
if len(td_experiment) == 2:
td_experiment_name_for_localization = td_experiment[1]
else:
td_experiment_name_for_localization = td_experiment_name_for_map
with open(
os.path.join(td_results_dir, td_experiment_name_for_map,
'experiment_summary.json')) as f:
td_summary_for_map = json.load(f)
with open(
os.path.join(td_results_dir,
altitude = 80
repetitions = 1
grid_size_values = [6]
from content.data_pointers.lavi_april_18.dji import snapshots_80_meters as snapshots
# from content.data_pointers.lavi_november_18.dji import plot1_snapshots_80_meters as snapshots
# from content.data_pointers.lavi_november_18.dji import plot2_snapshots_80_meters as snapshots
from content.data_pointers.lavi_april_18.orchard_topology import plot_pattern
# from content.data_pointers.lavi_november_18.orchard_topology import plot1_pattern as plot_pattern
# from content.data_pointers.lavi_november_18.orchard_topology import plot2_pattern as plot_pattern
############################################################################################################################
if __name__ == '__main__':
execution_dir = utils.create_new_execution_folder('trunks_detection')
image_key_to_best_score = {}
for image_key in snapshots.keys():
image_descriptor = snapshots[image_key]
for grid_size in grid_size_values:
# Run experiment
experiment = TrunksDetectionExperiment(name='trunks_detection_on_%s' % image_key, data_sources=image_descriptor.path, working_dir=execution_dir,
params={'crop_ratio': 0.8, 'initial_sigma_to_dim_y_ratio': 0.33, 'grid_size_for_optimization': grid_size,
'orchard_pattern': plot_pattern}, metadata={'image_key': image_key, 'altitude': altitude})
experiment.run(repetitions, viz_mode=False)
# Post processing (image level)
semantic_images_dir = os.path.join(experiment.experiment_dir, 'semantic_trunk_images')
os.makedirs(semantic_images_dir)
