def make_histogram(args, what_roi, session, metadata):
"""
Make a histogram of every image and write the values
to a file.
Expects the result of an argument parser, a string of what kind
of roi needs to be used and a connection
to an existing metadata database via an SQLAlchemy Session
instance 'session' and an SQLAlchemy MetaData instance
'metadata' which describes the database tables.
A connection to the database table 'Photos' is made.
Every file that is an image is opened and the title of that
image is taken from the database. A mask is created to isolate
a part of the image (Region Of Interest (ROI)) and a histogram is
made of that ROI. The values in the histogram-list are
normalized and relevant data is written to the outputfile.
"""
Base = automap_base(metadata=metadata)
Base.prepare()
configure_mappers()
Photo = Base.classes.photos
# Open outputfile.
outputfile = open(args.outputfile, 'a')
# Walk through files.
for root, dirs, files in os.walk(args.imdir):
for filename in files:
sys.stderr.write("File %s is being processed...\n" % filename)
# Make path to file.
path = os.path.join(root, filename)
# Open file and check datatype.
img = cv2.imread(path, 1)
if not isinstance(img, np.ndarray):
sys.stderr.write("File is no image: will be skipped.\n")
continue
photo_id = filename.split(".")[0]
# Get title of image from database.
# Set default in case there is no database entry for it.
title = photo_id
for pic in session.query(Photo).filter(Photo.id == photo_id):
title = photo_id if pic.title is None else pic.title
img, contour = create_mask(img, args, what_roi)
hist = ft.color_bgr_means(img, contour, bins=args.bins)
means_norm = hist_means(hist)
write_to_output_file(photo_id, title, means_norm, args, outputfile)
# Close outputfile.
outputfile.close()
def make_histogram(args, what_roi, session, metadata):
"""
Make a histogram of every image and write the values
to a file.
Expects the result of an argument parser, a string of what kind
of roi needs to be used and a connection
to an existing metadata database via an SQLAlchemy Session
instance 'session' and an SQLAlchemy MetaData instance
'metadata' which describes the database tables.
A connection to the database table 'Photos' is made.
Every file that is an image is opened and the title of that
image is taken from the database. A mask is created to isolate
a part of the image (Region Of Interest (ROI)) and a histogram is
made of that ROI. The values in the histogram-list are
normalized and relevant data is written to the outputfile.
"""
Base = automap_base(metadata=metadata)
Base.prepare()
configure_mappers()
Photo = Base.classes.photos
# Open outputfile.
outputfile = open(args.outputfile, 'a')
# Walk through files.
for root, dirs, files in os.walk(args.imdir):
for filename in files:
sys.stderr.write("File %s is being processed...\n" % filename)
# Make path to file.
path = os.path.join(root, filename)
# Open file and check datatype.
img = cv2.imread(path, 1)
if not isinstance(img, np.ndarray):
sys.stderr.write("File is no image: will be skipped.\n")
continue
photo_id = filename.split(".")[0]
# Get title of image from database.
# Set default in case there is no database entry for it.
title = photo_id
for pic in session.query(Photo).filter(Photo.id == photo_id):
title = photo_id if pic.title is None else pic.title
img, contour = create_mask(img, args, what_roi)
hist = ft.color_bgr_means(img, contour, bins=args.bins)
means_norm = hist_means(hist)
write_to_output_file(photo_id, title, means_norm, args, outputfile)
# Close outputfile.
outputfile.close()
def get_color_bgr_means(self, src, args, bin_mask=None):
if self.bin_mask == None:
raise ValueError("Binary mask cannot be None")
# Get the contours from the mask.
contour = ft.get_largest_contour(bin_mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if contour == None:
raise ValueError("No contour found for binary image")
# Create a masked image.
img = cv2.bitwise_and(src, src, mask=bin_mask)
bins = getattr(args, 'bins', 20)
output = ft.color_bgr_means(img, contour, bins)
# Normalize data to range -1 .. 1
return output * 2.0 / 255 - 1
def __get_color_bgr_means(self, src, args, bin_mask=None):
"""Executes :meth:`features.color_bgr_means`."""
if self.bin_mask is None:
raise ValueError("Binary mask cannot be None")
# Get the contours from the mask.
contour = ft.get_largest_contour(bin_mask.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
if contour is None:
raise ValueError("No contour found for binary image")
# Create a masked image.
img = cv2.bitwise_and(src, src, mask=bin_mask)
bins = getattr(args, 'bins', 20)
hor_means, ver_means = ft.color_bgr_means(img, contour, bins)
output = np.append(hor_means, ver_means).astype(float)
# Normalize the features if a scaler is set.
if self.scaler:
self.scaler.fit([0.0, 255.0])
output = self.scaler.transform( output )
return output
def __get_color_bgr_means(self, src, args, bin_mask=None):
"""Executes :meth:`features.color_bgr_means`."""
if self.bin_mask is None:
raise ValueError("Binary mask cannot be None")
# Get the contours from the mask.
contour = ft.get_largest_contour(bin_mask.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
if contour is None:
raise ValueError("No contour found for binary image")
# Create a masked image.
img = cv2.bitwise_and(src, src, mask=bin_mask)
bins = getattr(args, 'bins', 20)
hor_means, ver_means = ft.color_bgr_means(img, contour, bins)
output = np.append(hor_means, ver_means).astype(float)
# Normalize the features if a scaler is set.
if self.scaler:
self.scaler.fit([0.0, 255.0])
output = self.scaler.transform(output)
return output
