Datasets:

FelixOtt
/

industry_datasets

import keras
from tqdm import tqdm
import numpy as np
import argparse
from keras import backend as K
import os
import glob
import math
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session

parser = argparse.ArgumentParser()
parser.add_argument('--folder', help='name of the folder', required=True)
parser.add_argument('--test', help='file of the test dataset', required=False)

args = parser.parse_args()
folder = args.folder
test_set = args.test

# Read in the parameter information from the current train folder
f = open(folder + "parameters.txt", "r")
lines = f.read()
testdataset = test_set
modelname = lines.split("\n")[0].split(" ")[-1]
BETA = int(lines.split("\n")[4].split(" ")[-1])
weights = glob.glob(folder + "weights/*")
startweight = weights[-1]
errthresh = 3

config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
set_session(sess)

# Load and create the model
import cnn_mTD_2048 as cnn
model = cnn.create_cnn_lstm(startweight)
import utilities_pos as utilities

# Create the folder to save the results
if not os.path.exists(folder + "/error/"):
    os.makedirs(folder + "/error/")

# Create and open the files to save the results
tests_error = open(folder + "/error/" + folder.split("/")[-2] + "_result_" + testdataset.split("/")[-1], "w")
tests_error_pred= open(folder + "/error/" + folder.split("/")[-2] + "_prediction_" + testdataset.split("/")[-1], "w")


def getError(posx, posq, actualx, actualq):
    """ Calculates the position and rotation error """
    q1 = actualq / np.linalg.norm(actualq)
    q2 = posq / np.linalg.norm(posq)
    d = abs(np.sum(np.multiply(q1, q2)))
    theta = 2 * np.arccos(d) * 180 / np.pi
    errx = np.linalg.norm(actualx - posx)
    return errx, theta


# Loss function (not relevant)
def pose_loss3(y_true, y_pred):
        return K.sum(K.square(y_pred - y_true))


def rotation_loss3(y_true, y_pred):
        return BETA * K.sum(K.square(y_true-y_pred))


# compile the model
sgd = keras.optimizers.Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
run_opts = tf.RunOptions(report_tensor_allocations_upon_oom = True)
model.compile(optimizer=sgd, loss=[pose_loss3, rotation_loss3], options=run_opts)

# Load the test data and create the data generator
testdatasource, timestamps = utilities.get_training_data(testdataset)
datagen = utilities.limited_gen_data(testdatasource)

results = []
results2d = []
i = 0
for ims, xs, qs in tqdm(datagen):  # predict over all test samples and calculate the error
    inputs = np.zeros([1, 3, 224, 224, 3])
    inputs[0, :] = ims
    out = model.predict(inputs)
    pred_x = out[0][0][1]
    pred_q = out[1][0][1]
    label_x = xs[1]
    label_q = qs[1]
    _, theta = getError(pred_x, pred_q, label_x, label_q)
    label_x = np.squeeze(label_x)
    label_q = np.squeeze(label_q)
    pred_x = np.squeeze(pred_x)
    pred_q = np.squeeze(pred_q)
    if not math.isnan(theta):
        # Save the results in the above defined files
        error_x = np.linalg.norm(label_x - pred_x)
        error_x_2d = np.linalg.norm(label_x - [pred_x[0], pred_x[1], label_x[2]])
        results.append([error_x, theta])
        results2d.append([error_x_2d])
        tests_error.write("\t Image %d: Error xyz: %fm, Error wpqr: %f degrees \n" % (int(i), error_x, theta))
        tests_error.flush()
        tests_error_pred.write("Timestamp: " + timestamps[i] + ", Label: x: " + str(label_x[0]) + ", y: " + str(label_x[1]) + ", z: " + str(label_x[2]) + ", w0: " + str(label_q[0]) + ", w1: " + str(label_q[1]) + ", w2: " + str(label_q[2]) + ", w3: " + str(label_q[3]) + " Prediction: x: " + str(pred_x[0]) + ", y: " + str(pred_x[1]) + ", z: " + str(pred_x[2]) + ", w0: " + str(pred_q[0]) + ", w1: " + str(pred_q[1]) + ", w2: " + str(pred_q[2]) + ", w3: " + str(pred_q[3]) + "\n")
        tests_error_pred.flush()
    i = i + 1

# Calculate different values for an overview of the results
mean_result = np.mean(results, axis=0)
median_result = np.median(results, axis=0)
percentile50_result_2d = np.percentile(results2d, axis=0, q=50)
percentile95_result_2d = np.percentile(results2d, axis=0, q=95)
percentile50_result = np.percentile(results, axis=0, q=50)
percentile95_result = np.percentile(results, axis=0, q=95)

# Print and save the overview
print("\n Results: \n \t MAE 2D: %f m \n\t Median 2D: %f m \n\t CEP: %f m \n\t CE95: %f m \n\t MAE 2D: %f deg \n\t Median 2D: %f deg \n\t CE95: %f deg \n" % (mean_result[0], median_result[0], percentile50_result_2d[0], percentile95_result_2d[0], mean_result[1], median_result[1], percentile95_result[1]))
tests_error.write("\n Results: \n \t MAE 2D: %f m \n\t Median 2D: %f m \n\t CEP: %f m \n\t CE95: %f m \n\t MAE 2D: %f deg \n\t Median 2D: %f deg \n\t CE95: %f deg \n" % (mean_result[0], median_result[0], percentile50_result_2d[0], percentile95_result_2d[0], mean_result[1], median_result[1], percentile95_result[1]))

	import keras
	from tqdm import tqdm
	import numpy as np
	import argparse
	from keras import backend as K
	import os
	import glob
	import math
	import tensorflow as tf
	from keras.backend.tensorflow_backend import set_session

	parser = argparse.ArgumentParser()
	parser.add_argument('--folder', help='name of the folder', required=True)
	parser.add_argument('--test', help='file of the test dataset', required=False)

	args = parser.parse_args()
	folder = args.folder
	test_set = args.test

	# Read in the parameter information from the current train folder
	f = open(folder + "parameters.txt", "r")
	lines = f.read()
	testdataset = test_set
	modelname = lines.split("\n")[0].split(" ")[-1]
	BETA = int(lines.split("\n")[4].split(" ")[-1])
	weights = glob.glob(folder + "weights/*")
	startweight = weights[-1]
	errthresh = 3

	config = tf.ConfigProto()
	config.gpu_options.allow_growth = True
	sess = tf.Session(config=config)
	set_session(sess)

	# Load and create the model
	import cnn_mTD_2048 as cnn
	model = cnn.create_cnn_lstm(startweight)
	import utilities_pos as utilities

	# Create the folder to save the results
	if not os.path.exists(folder + "/error/"):
	os.makedirs(folder + "/error/")

	# Create and open the files to save the results
	tests_error = open(folder + "/error/" + folder.split("/")[-2] + "_result_" + testdataset.split("/")[-1], "w")
	tests_error_pred= open(folder + "/error/" + folder.split("/")[-2] + "_prediction_" + testdataset.split("/")[-1], "w")


	def getError(posx, posq, actualx, actualq):
	""" Calculates the position and rotation error """
	q1 = actualq / np.linalg.norm(actualq)
	q2 = posq / np.linalg.norm(posq)
	d = abs(np.sum(np.multiply(q1, q2)))
	theta = 2 * np.arccos(d) * 180 / np.pi
	errx = np.linalg.norm(actualx - posx)
	return errx, theta


	# Loss function (not relevant)
	def pose_loss3(y_true, y_pred):
	return K.sum(K.square(y_pred - y_true))


	def rotation_loss3(y_true, y_pred):
	return BETA * K.sum(K.square(y_true-y_pred))


	# compile the model
	sgd = keras.optimizers.Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
	run_opts = tf.RunOptions(report_tensor_allocations_upon_oom = True)
	model.compile(optimizer=sgd, loss=[pose_loss3, rotation_loss3], options=run_opts)

	# Load the test data and create the data generator
	testdatasource, timestamps = utilities.get_training_data(testdataset)
	datagen = utilities.limited_gen_data(testdatasource)

	results = []
	results2d = []
	i = 0
	for ims, xs, qs in tqdm(datagen): # predict over all test samples and calculate the error
	inputs = np.zeros([1, 3, 224, 224, 3])
	inputs[0, :] = ims
	out = model.predict(inputs)
	pred_x = out[0][0][1]
	pred_q = out[1][0][1]
	label_x = xs[1]
	label_q = qs[1]
	_, theta = getError(pred_x, pred_q, label_x, label_q)
	label_x = np.squeeze(label_x)
	label_q = np.squeeze(label_q)
	pred_x = np.squeeze(pred_x)
	pred_q = np.squeeze(pred_q)
	if not math.isnan(theta):
	# Save the results in the above defined files
	error_x = np.linalg.norm(label_x - pred_x)
	error_x_2d = np.linalg.norm(label_x - [pred_x[0], pred_x[1], label_x[2]])
	results.append([error_x, theta])
	results2d.append([error_x_2d])
	tests_error.write("\t Image %d: Error xyz: %fm, Error wpqr: %f degrees \n" % (int(i), error_x, theta))
	tests_error.flush()
	tests_error_pred.write("Timestamp: " + timestamps[i] + ", Label: x: " + str(label_x[0]) + ", y: " + str(label_x[1]) + ", z: " + str(label_x[2]) + ", w0: " + str(label_q[0]) + ", w1: " + str(label_q[1]) + ", w2: " + str(label_q[2]) + ", w3: " + str(label_q[3]) + " Prediction: x: " + str(pred_x[0]) + ", y: " + str(pred_x[1]) + ", z: " + str(pred_x[2]) + ", w0: " + str(pred_q[0]) + ", w1: " + str(pred_q[1]) + ", w2: " + str(pred_q[2]) + ", w3: " + str(pred_q[3]) + "\n")
	tests_error_pred.flush()
	i = i + 1

	# Calculate different values for an overview of the results
	mean_result = np.mean(results, axis=0)
	median_result = np.median(results, axis=0)
	percentile50_result_2d = np.percentile(results2d, axis=0, q=50)
	percentile95_result_2d = np.percentile(results2d, axis=0, q=95)
	percentile50_result = np.percentile(results, axis=0, q=50)
	percentile95_result = np.percentile(results, axis=0, q=95)

	# Print and save the overview
	print("\n Results: \n \t MAE 2D: %f m \n\t Median 2D: %f m \n\t CEP: %f m \n\t CE95: %f m \n\t MAE 2D: %f deg \n\t Median 2D: %f deg \n\t CE95: %f deg \n" % (mean_result[0], median_result[0], percentile50_result_2d[0], percentile95_result_2d[0], mean_result[1], median_result[1], percentile95_result[1]))
	tests_error.write("\n Results: \n \t MAE 2D: %f m \n\t Median 2D: %f m \n\t CEP: %f m \n\t CE95: %f m \n\t MAE 2D: %f deg \n\t Median 2D: %f deg \n\t CE95: %f deg \n" % (mean_result[0], median_result[0], percentile50_result_2d[0], percentile95_result_2d[0], mean_result[1], median_result[1], percentile95_result[1]))