def pixel_temp(frames,n_frames,n_columns,n_rows):
''' Function to determine the temperature of the samples and plate locations by analysing
pixel values and finding peaks.
Args:
frames: The frames of a video to be analysed.
n_frames: Number of frames in the video
n_columns: Number of columns of samples in the image
n_rows: Number of rows of samples in the image.
Returns:
m_df(Dataframe): A dataframe containing row and column coordinates of each sample
and its respective inflection point obtained.
'''
flip_frames = edge_detection.flip_frame(frames)
#Function to obtained an equalized image using all the frames
#in the video.
img_eq = image_eq(n_frames,flip_frames)
#Funtion to determine sum of pixels over all the rows and columns
#to obtain plots with peaks at the sample position in the array.
column_sum,row_sum = pixel_sum(img_eq)
# Function to find peaks from the column_sum and row_sum arrays
# and return a dataframe with sample locations.
sample_location = peak_values(column_sum,row_sum,n_columns,n_rows,img_eq)
# Function to find pixel intensity at all sample locations
# and plate locations in each frame.
temp,plate_temp = pixel_intensity(sample_location, frames, x_name = 'Row', y_name = 'Column', plate_name = 'plate_location')
# Function to obtain the inflection point(melting point) from the temperature profile.
inf_temp, s_peaks, p_peaks = inflection_point(temp,plate_temp)
# Dataframe with sample location (row and column coordinates) and respective inflection point.
m_df = pd.DataFrame({'Row':sample_location.Row,'Column':sample_location.Column,'Melting point':inf_temp})
return m_df
def test_locations():
'''Test for functin which returns a dataframe containing row and column
locations of samples and their respective plate location at which temperature
profiles are monitored'''
frames = edge_detection.input_file(
'../musical-robot/musicalrobot/data/10_17_19_PPA_Shallow_plate.tiff')
crop_frame = []
for frame in frames:
crop_frame.append(frame[35:85, 40:120])
pixel_frames = edge_detection.flip_frame(crop_frame)
img_eq = pixel_analysis.image_eq(pixel_frames)
column_sum, row_sum = pixel_analysis.pixel_sum(img_eq)
n_columns = 3
n_rows = 3
r_peaks, c_peaks = pixel_analysis.peak_values(column_sum,
row_sum,
n_columns,
n_rows,
freeze_heat=False)
sample_location = pixel_analysis.locations(r_peaks, c_peaks, img_eq)
assert isinstance(sample_location,
pd.DataFrame), 'Output is not a dataframe'
assert len(
sample_location
) == n_columns * n_rows, 'Wrong number of sample locations are present'
return
def test_pixel_intensity():
'''Test for function which determines sample temperature and plate temperature'''
frames = edge_detection.input_file(
'../musical-robot/musicalrobot/data/10_17_19_PPA_Shallow_plate.tiff')
crop_frame = []
for frame in frames:
crop_frame.append(frame[35:85, 40:120])
pixel_frames = edge_detection.flip_frame(crop_frame)
img_eq = pixel_analysis.image_eq(pixel_frames)
column_sum, row_sum = pixel_analysis.pixel_sum(img_eq)
n_columns = 3
n_rows = 3
r_peaks, c_peaks = pixel_analysis.peak_values(column_sum,
row_sum,
n_columns,
n_rows,
freeze_heat=False)
sample_location = pixel_analysis.locations(r_peaks, c_peaks, img_eq)
x_name = 'Row'
y_name = 'Column'
plate_name = 'plate_location'
pixel_sample, pixel_plate = pixel_analysis.pixel_intensity(
sample_location, pixel_frames, x_name, y_name, plate_name)
assert isinstance(pixel_sample, list), 'Output is not a list'
assert isinstance(pixel_plate, list), 'Output is not a list'
assert len(
pixel_sample
) == n_columns * n_rows, 'Temperature obtained for wrong number of samples'
assert len(
pixel_plate
) == n_columns * n_rows, 'Temperature obtained for wrong number of plate locations'
return
def test_image_eq():
''' Test for fucntion which equalizes a low contrast image'''
frames = edge_detection.input_file('../musical-robot/musicalrobot/data/10_17_19_PPA_Shallow_plate.tiff')
crop_frame = []
for frame in frames:
crop_frame.append(frame[35:85,40:120])
pixel_frames = edge_detection.flip_frame(crop_frame)
n_frames = len(pixel_frames)
img_eq = pixel_analysis.image_eq(n_frames,pixel_frames)
assert isinstance(img_eq,np.ndarray),'Output is not an array'
assert pixel_frames[0].shape == img_eq.shape, 'Output array shape is not same as the input array shape.'
return
def test_flip_frame():
'''Test for function which flips the frames horizontally
and vertically to correct for the mirroring during recording.'''
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)
assert isinstance(flip_frames, list), 'Output is not a list'
return
def test_pixel_temp():
'''Test for the wrapping function'''
frames = edge_detection.input_file('../musical-robot/musicalrobot/data/10_17_19_PPA_Shallow_plate.tiff')
crop_frame = []
for frame in frames:
crop_frame.append(frame[35:85,40:120])
pixel_frames = edge_detection.flip_frame(crop_frame)
n_frames = len(pixel_frames)
n_columns = 3
n_rows = 3
m_df = pixel_analysis.pixel_temp(pixel_frames,n_frames,n_columns,n_rows)
assert isinstance(m_df,pd.DataFrame),'Output obtained is not a dataframe'
assert len(m_df)==n_columns*n_rows,'Temperature obtained for wrong number of samples'
return
def test_pixel_sum():
'''Test for function which obtains the sum of pixels over all rows and columns'''
frames = edge_detection.input_file('../musical-robot/musicalrobot/data/10_17_19_PPA_Shallow_plate.tiff')
crop_frame = []
for frame in frames:
crop_frame.append(frame[35:85,40:120])
pixel_frames = edge_detection.flip_frame(crop_frame)
n_frames = len(pixel_frames)
img_eq = pixel_analysis.image_eq(n_frames,pixel_frames)
column_sum, row_sum = pixel_analysis.pixel_sum(img_eq)
assert isinstance(column_sum,list),'Column sum is not a list'
assert isinstance(row_sum,list),'Row sum is not a list'
assert len(row_sum) == img_eq.shape[0], 'The length of row_sum is not equal to number of rows in the input image'
assert len(column_sum) == img_eq.shape[1], 'The length of column_sum is not equal to number of columns in the input image'
return
def test_peak_values():
'''Test for function which finds peaks from the column_sum and row_sum arrays
and return a dataframe with sample locations and plate locations.'''
frames = edge_detection.input_file('../musical-robot/musicalrobot/data/10_17_19_PPA_Shallow_plate.tiff')
crop_frame = []
for frame in frames:
crop_frame.append(frame[35:85,40:120])
pixel_frames = edge_detection.flip_frame(crop_frame)
n_frames = len(pixel_frames)
img_eq = pixel_analysis.image_eq(n_frames,pixel_frames)
column_sum, row_sum = pixel_analysis.pixel_sum(img_eq)
n_columns = 3
n_rows = 3
r_peaks, c_peaks = pixel_analysis.peak_values(column_sum,row_sum,n_columns,n_rows,img_eq)
assert isinstance(r_peaks, list), 'Output is not a list'
assert isinstance(c_peaks, list), 'Output is not a list'
assert len(r_peaks) == n_rows, 'Wrong number of sample rows detected'
assert len(c_peaks) == n_columns, 'Wrong number of sample columns detected'
return