def test_voxelgrid_generation(self):
dataset_path = get_dataset_path()
print("Using dataset path:", dataset_path)
dataset_parameters_voxelgrids = {}
dataset_parameters_voxelgrids["input_type"] = "voxelgrid"
dataset_parameters_voxelgrids["random_seed"] = 666
dataset_parameters_voxelgrids["voxelgrid_target_shape"] = (32, 32, 32)
dataset_parameters_voxelgrids["voxel_size_meters"] = 0.1
data_generator = create_datagenerator_from_parameters(dataset_path, dataset_parameters_voxelgrids)
data_generator.analyze_files()
dataset = next(data_generator.generate(size=100, verbose=True))
assert dataset[0].shape == (100, 3000, 4), str(dataset[0].shape)
assert dataset[1].shape == (100, 1), str(dataset[1].shape)
def test_pointcloud_generation(self):
dataset_path = get_dataset_path()
print("Using dataset path:", dataset_path)
dataset_parameters_pointclouds = {}
dataset_parameters_pointclouds["input_type"] = "pointcloud"
dataset_parameters_pointclouds["random_seed"] = 666
dataset_parameters_pointclouds["pointcloud_target_size"] = 3000
dataset_parameters_pointclouds["pointcloud_random_rotation"] = True
data_generator = create_datagenerator_from_parameters(dataset_path, dataset_parameters_pointclouds)
data_generator.analyze_files()
dataset = next(data_generator.generate(size=100, verbose=True))
assert dataset[0].shape == (100, 3000, 4), str(dataset[0].shape)
assert dataset[1].shape == (100, 1), str(dataset[1].shape)
def test_sequence_pointcloud(self):
dataset_path = get_dataset_path("../../data/preprocessed")
print("Using dataset path:", dataset_path)
dataset_parameters_pointclouds = {}
dataset_parameters_pointclouds["input_type"] = "pointcloud"
dataset_parameters_pointclouds["random_seed"] = 666
dataset_parameters_pointclouds["pointcloud_target_size"] = 3000
dataset_parameters_pointclouds["pointcloud_random_rotation"] = True
dataset_parameters_pointclouds["filter"] = "360"
dataset_parameters_pointclouds["sequence_length"] = 8
data_generator = create_datagenerator_from_parameters(dataset_path, dataset_parameters_pointclouds)
data_generator.analyze_files()
dataset = next(data_generator.generate(size=10, verbose=True))
assert dataset[0].shape == (10, 8, 3000, 3), str(dataset[0].shape)
assert dataset[1].shape == (10, 1), str(dataset[1].shape)
def test_sequence_rgb_map_stress(self):
dataset_path = get_dataset_path("../../data/preprocessed")
print("Using dataset path:", dataset_path)
dataset_parameters_rgbmaps = {}
dataset_parameters_rgbmaps["input_type"] = "rgbmap"
dataset_parameters_rgbmaps["random_seed"] = 666
dataset_parameters_rgbmaps["filter"] = "360"
dataset_parameters_rgbmaps["sequence_length"] = 8
dataset_parameters_rgbmaps["rgbmap_target_width"] = 64
dataset_parameters_rgbmaps["rgbmap_target_height"] = 64
dataset_parameters_rgbmaps["rgbmap_scale_factor"] = 1.0
dataset_parameters_rgbmaps["rgbmap_axis"] = "vertical"
data_generator = create_datagenerator_from_parameters(dataset_path, dataset_parameters_rgbmaps)
data_generator.analyze_files()
dataset = next(data_generator.generate(size=10000, verbose=True))
assert dataset[0].shape == (10000, 64, 64, 3), str(dataset[0].shape)
assert dataset[1].shape == (10000, 1), str(dataset[1].shape)
def test_pointcloud_measuring_time(self):
dataset_path = get_dataset_path()
print("Using dataset path:", dataset_path)
dataset_parameters_pointclouds = {}
dataset_parameters_pointclouds["input_type"] = "pointcloud"
dataset_parameters_pointclouds["random_seed"] = 666
dataset_parameters_pointclouds["pointcloud_target_size"] = 30000
dataset_parameters_pointclouds["pointcloud_random_rotation"] = True
data_generator = create_datagenerator_from_parameters(dataset_path, dataset_parameters_pointclouds)
start_time = time.time()
pointclouds_count = 0
for qrcode, paths in data_generator.qrcodes_dictionary.items():
for path in paths:
with open(path, "rb") as file:
(pointcloud, targets) = pickle.load(file)
pointclouds_count += 1
del pointcloud
del targets
elapsed_time = time.time() - start_time
print("Loaded {} pointclouds in {} seconds".format(pointclouds_count, elapsed_time))
def test_sequence_rgb_map(self):
dataset_path = get_dataset_path("../../data/preprocessed")
print("Using dataset path:", dataset_path)
dataset_parameters_rgbmaps = {}
dataset_parameters_rgbmaps["input_type"] = "rgbmap"
dataset_parameters_rgbmaps["output_targets"] = ["weight"]
dataset_parameters_rgbmaps["random_seed"] = 666
dataset_parameters_rgbmaps["filter"] = "360"
dataset_parameters_rgbmaps["sequence_length"] = 4
dataset_parameters_rgbmaps["rgbmap_target_width"] = 64
dataset_parameters_rgbmaps["rgbmap_target_height"] = 64
dataset_parameters_rgbmaps["rgbmap_scale_factor"] = 1.0
dataset_parameters_rgbmaps["rgbmap_axis"] = "horizontal"
data_generator = create_datagenerator_from_parameters(dataset_path, dataset_parameters_rgbmaps)
data_generator.analyze_files()
generator = data_generator.generate(size=10, verbose=False)
for _ in range(10):
dataset = next(generator)
assert dataset[0].shape == (10, 4, 64, 64, 3), str(dataset[0].shape)
assert dataset[1].shape == (10, 1), str(dataset[1].shape)
print(dataset[1])
'''
This script trains on RGB-Maps.
'''
from cgmcore import modelutils
from cgmcore import utils
import numpy as np
from keras import models, layers, callbacks, optimizers
import pprint
import os
from cgmcore.preprocesseddatagenerator import get_dataset_path, create_datagenerator_from_parameters
import random
import qrcodes
# Get the dataset path.
dataset_path = get_dataset_path()
print("Using dataset path", dataset_path)
# Hyperparameters.
steps_per_epoch = 100
validation_steps = 10
epochs = 50
batch_size = 32
random_seed = 667
image_size = 128
# For creating pointclouds.
dataset_parameters = {}
dataset_parameters["input_type"] = "rgbmap"
dataset_parameters["output_targets"] = ["weight"]
dataset_parameters["random_seed"] = 666