def test_inf_prediction():
'''Test for function which classifies images as with and
without inflection'''
file_name = (
'../musical-robot/musicalrobot/data/10_17_19_PPA_Shallow_plate.tiff')
frames = edge_detection.input_file(file_name)
crop_frame = []
for frame in frames:
crop_frame.append(frame[35:85, 40:120])
n_samples = 9
n_rows = 3
n_columns = 3
labeled_samples = edge_detection.edge_detection(crop_frame, n_samples)
regprops = edge_detection.regprop(labeled_samples, crop_frame, n_rows,
n_columns)
sorted_regprops = edge_detection.sort_regprops(regprops, n_columns, n_rows)
temp, plate_temp = edge_detection.sample_temp(sorted_regprops, crop_frame)
data_encoding.noise_image(temp, plate_temp, 'musicalrobot/data/')
file_path = 'musicalrobot/data/noise_images/'
result_df, nonoise_index = data_encoding.noise_prediction(file_path)
data_encoding.inf_images(temp, plate_temp, 2, nonoise_index,
'musicalrobot/data/')
file_path = 'musicalrobot/data/inf_images/'
inf_pred, inf_index = data_encoding.inf_prediction(file_path)
assert isinstance(inf_pred, dict), 'Output is not a dictionary'
assert isinstance(inf_index, list), 'Output is not a list'
return
def test_plot_to_array():
'''Test for function which generates a gray images
of the temperature profile'''
file_name = (
'../musical-robot/musicalrobot/data/10_17_19_PPA_Shallow_plate.tiff')
frames = edge_detection.input_file(file_name)
crop_frame = []
for frame in frames:
crop_frame.append(frame[35:85, 40:120])
n_samples = 9
n_rows = 3
n_columns = 3
labeled_samples = edge_detection.edge_detection(crop_frame, n_samples)
regprops = edge_detection.regprop(labeled_samples, crop_frame, n_rows,
n_columns)
sorted_regprops = edge_detection.sort_regprops(regprops, n_columns, n_rows)
temp, plate_temp = edge_detection.sample_temp(sorted_regprops, crop_frame)
for i in range(len(temp)):
x = plate_temp[i]
y = temp[i]
length = 2
gray_image = data_encoding.plot_to_array(x, y, length)
assert isinstance(gray_image, np.ndarray), 'Output is not an array'
assert len(gray_image) == 200, 'Incorrectly sized output array'
return
def test_sample_peaks():
''' Test for function which obtains the peaks in the sample temperature profile'''
file_name = (
'../musical-robot/musicalrobot/data/10_17_19_PPA_Shallow_plate.tiff')
frames = edge_detection.input_file(file_name)
crop_frame = []
for frame in frames:
crop_frame.append(frame[35:85, 40:120])
# flip_frames = edge_detection.flip_frame(crop_frame)
n_samples = 9
n_rows = 3
n_columns = 3
labeled_samples = edge_detection.edge_detection(crop_frame, n_samples)
regprops = edge_detection.regprop(labeled_samples, crop_frame, n_rows,
n_columns)
sorted_regprops = edge_detection.sort_regprops(regprops, n_columns, n_rows)
temp, plate_temp = edge_detection.sample_temp(sorted_regprops, crop_frame)
s_peaks, s_infl = edge_detection.peak_detection(temp, plate_temp, 'Sample')
assert isinstance(s_peaks, list), 'Output is not a list'
assert isinstance(s_infl, list), 'Output is not a list'
assert len(s_peaks) == n_samples, 'Wrong number of peaks detected'
assert len(
s_infl
) == n_samples, 'Wrong number of inflection temperatures detected'
return
def test_sample_temp():
'''Test for function which obtaines temperature of samples and plate temperature'''
file_name = (
'../musical-robot/musicalrobot/data/10_17_19_PPA_Shallow_plate.tiff')
frames = edge_detection.input_file(file_name)
crop_frame = []
for frame in frames:
crop_frame.append(frame[35:85, 40:120])
# flip_frames = edge_detection.flip_frame(crop_frame)
n_samples = 9
n_rows = 3
n_columns = 3
labeled_samples = edge_detection.edge_detection(crop_frame, n_samples)
regprops = edge_detection.regprop(labeled_samples, crop_frame, n_rows,
n_columns)
sorted_regprops = edge_detection.sort_regprops(regprops, n_columns, n_rows)
temp, plate_temp = edge_detection.sample_temp(sorted_regprops, crop_frame)
assert isinstance(temp, list), 'Sample temperature output is not a list'
assert isinstance(plate_temp,
list), 'Plate temperature output is not a list'
assert len(
temp) == n_samples, 'Temperature obtained for wrong number of samples'
assert len(
plate_temp
) == n_samples, 'Temperature obtained for wrong number of plate locations'
return
def test_final_result():
'''Test for the wrappung function'''
file_name = (
'../musical-robot/musicalrobot/data/10_17_19_PPA_Shallow_plate.tiff')
frames = edge_detection.input_file(file_name)
crop_frame = []
for frame in frames:
crop_frame.append(frame[35:85, 40:120])
n_samples = 9
n_rows = 3
n_columns = 3
labeled_samples = edge_detection.edge_detection(crop_frame, n_samples)
regprops = edge_detection.regprop(labeled_samples, crop_frame, n_rows,
n_columns)
sorted_regprops = edge_detection.sort_regprops(regprops, n_columns, n_rows)
temp, plate_temp = edge_detection.sample_temp(sorted_regprops, crop_frame)
path = 'musicalrobot/data/'
result_df = data_encoding.final_result(temp, plate_temp, path)
assert isinstance(result_df, pd.DataFrame), 'Output is not a dataframe'
assert len(result_df) == len(temp), 'Incorrect number of samples classfied'
return
def test_derivative():
'''Test for function which calculates the derivative
of the temperature profile for neural network input'''
file_name = (
'../musical-robot/musicalrobot/data/10_17_19_PPA_Shallow_plate.tiff')
frames = edge_detection.input_file(file_name)
crop_frame = []
for frame in frames:
crop_frame.append(frame[35:85, 40:120])
n_samples = 9
n_rows = 3
n_columns = 3
labeled_samples = edge_detection.edge_detection(crop_frame, n_samples)
regprops = edge_detection.regprop(labeled_samples, crop_frame, n_rows,
n_columns)
sorted_regprops = edge_detection.sort_regprops(regprops, n_columns, n_rows)
temp, plate_temp = edge_detection.sample_temp(sorted_regprops, crop_frame)
derivative_list = data_encoding.derivative(temp, plate_temp)
assert isinstance(derivative_list, list), 'Output is not a list'
assert len(derivative_list) == len(temp), 'Incorrect number of derivatives'
return
def test_noise_prediction():
'''Test for function which classifies images as noisy or
not noisy'''
file_name = (
'../musical-robot/musicalrobot/data/10_17_19_PPA_Shallow_plate.tiff')
frames = edge_detection.input_file(file_name)
crop_frame = []
for frame in frames:
crop_frame.append(frame[35:85, 40:120])
n_samples = 9
n_rows = 3
n_columns = 3
labeled_samples = edge_detection.edge_detection(crop_frame, n_samples)
regprops = edge_detection.regprop(labeled_samples, crop_frame, n_rows,
n_columns)
sorted_regprops = edge_detection.sort_regprops(regprops, n_columns, n_rows)
temp, plate_temp = edge_detection.sample_temp(sorted_regprops, crop_frame)
data_encoding.noise_image(temp, plate_temp, 'musicalrobot/data/')
file_path = 'musicalrobot/data/noise_images/'
result_df, nonoise_index = data_encoding.noise_prediction(file_path)
assert isinstance(result_df, pd.DataFrame), 'Output is not a dataframe'
assert len(result_df) == len(
temp), 'Incorrect number of samples in the dataframe'
assert isinstance(nonoise_index, list), 'Output is not a list'
def test_sort_regprops():
'''Test for function which sorts the dataframes in the dictionary regprops'''
file_name = (
'../musical-robot/musicalrobot/data/10_17_19_PPA_Shallow_plate.tiff')
frames = edge_detection.input_file(file_name)
crop_frame = []
for frame in frames:
crop_frame.append(frame[35:85, 40:120])
# flip_frames = edge_detection.flip_frame(crop_frame)
n_samples = 9
n_rows = 3
n_columns = 3
labeled_samples = edge_detection.edge_detection(crop_frame, n_samples)
regprops = edge_detection.regprop(labeled_samples, crop_frame, n_rows,
n_columns)
sorted_regprops = edge_detection.sort_regprops(regprops, n_columns, n_rows)
assert isinstance(sorted_regprops, dict), 'Output is not a dictionary'
assert len(sorted_regprops) == len(
crop_frame
), 'The number of dataframes in the dictionary is not equal to number of frames input.'
for i in range(len(crop_frame)):
assert len(sorted_regprops[i]
) == n_samples, 'Wrong number of samples detected'
return