def process_image(args, path):
global bin_mask, intersects, rotation, img, img_src, center
img = cv2.imread(path)
if img == None or img.size == 0:
logging.info("Failed to read %s" % path)
return
logging.info("Processing %s..." % path)
# Scale the image down if its perimeter exceeds the maximum (if set).
img = common.scale_max_perimeter(img, args.max_size)
img_src = img.copy()
# Perform segmentation
logging.info("- Segmenting...")
mask = common.grabcut(img, args.iters, None, args.margin)
bin_mask = np.where((mask == cv2.GC_FGD) + (mask == cv2.GC_PR_FGD), 255,
0).astype('uint8')
# Obtain contours (all points) from the mask.
contour = ft.get_largest_contour(bin_mask, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE)
# Fit an ellipse on the contour to get the rotation angle.
box = cv2.fitEllipse(contour)
rotation = int(box[2])
# Get the shape360 feature.
logging.info("- Obtaining shape...")
intersects, center = ft.shape_360(contour, rotation)
logging.info("- Done")
draw_axis()
def process_image(args, path):
global intersects, rotation, img, img_src, center
img = cv2.imread(path)
if img == None or img.size == 0:
logging.info("Failed to read %s" % path)
return
logging.info("Processing %s..." % path)
# Scale the image down if its perimeter exceeds the maximum (if set).
img = common.scale_max_perimeter(img, args.max_size)
img_src = img.copy()
# Perform segmentation
logging.info("- Segmenting...")
mask = common.grabcut(img, args.iters, None, args.margin)
bin_mask = np.where((mask == cv2.GC_FGD) + (mask == cv2.GC_PR_FGD), 255, 0).astype("uint8")
# Obtain contours (all points) from the mask.
contour = ft.get_largest_contour(bin_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
# Fit an ellipse on the contour to get the rotation angle.
box = cv2.fitEllipse(contour)
rotation = int(box[2])
# Get the shape360 feature.
logging.info("- Obtaining shape...")
intersects, center = ft.shape_360(contour, rotation)
logging.info("- Done")
draw_axis()
def __get_shape_360(self, args, bin_mask):
"""Executes :meth:`features.shape_360`."""
if self.bin_mask == None:
raise ValueError("Binary mask cannot be None")
rotation = getattr(args, 'rotation', 0)
step = getattr(args, 'step', 1)
t = getattr(args, 't', 8)
output_functions = getattr(args, 'output_functions', {'mean_sd': True})
# Get the largest contour from the binary mask.
contour = ft.get_largest_contour(bin_mask, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE)
if contour == None:
raise ValueError("No contour found for binary image")
# Set the rotation.
if rotation == 'FIT_ELLIPSE':
box = cv2.fitEllipse(contour)
rotation = int(box[2])
if not 0 <= rotation <= 179:
raise ValueError("Rotation must be in the range 0 to 179, "\
"found %s" % rotation)
# Extract shape feature.
intersects, center = ft.shape_360(contour, rotation, step, t)
# Create a masked image.
if 'color_histograms' in output_functions:
img_masked = cv2.bitwise_and(self.img, self.img, mask=bin_mask)
# Run the output function for each angle.
means = []
sds = []
histograms = []
for angle in range(0, 360, step):
for f_name, f_args in vars(output_functions).iteritems():
# Mean distance + standard deviation.
if f_name == 'mean_sd':
distances = []
for p in intersects[angle]:
d = ft.point_dist(center, p)
distances.append(d)
if len(distances) == 0:
mean = 0
sd = 0
else:
mean = np.mean(distances, dtype=np.float32)
if len(distances) > 1:
sd = np.std(distances, ddof=1, dtype=np.float32)
else:
sd = 0
means.append(mean)
sds.append(sd)
# Color histograms.
if f_name == 'color_histograms':
# Get a line from the center to the outer intersection point.
line = None
if intersects[angle]:
line = ft.extreme_points([center] + intersects[angle])
# Create a mask for the line, where the line is foreground.
line_mask = np.zeros(self.img.shape[:2], dtype=np.uint8)
if line is not None:
cv2.line(line_mask, tuple(line[0]), tuple(line[1]),
255, 1)
# Create histogram from masked + line masked image.
hists = self.__get_color_histograms(img_masked, f_args,
line_mask)
histograms.append(hists)
means = means.astype(float)
sds = sds.astype(float)
# Normalize the features if a scaler is set.
if self.scaler and 'mean_sd' in output_functions:
means = self.scaler.fit_transform(means)
sds = self.scaler.fit_transform(sds)
# Group the means+sds together.
means_sds = np.array(zip(means, sds)).flatten()
return np.append(means_sds, histograms)
def __get_shape_360(self, args, bin_mask):
"""Executes :meth:`features.shape_360`."""
if self.bin_mask == None:
raise ValueError("Binary mask cannot be None")
rotation = getattr(args, 'rotation', 0)
step = getattr(args, 'step', 1)
t = getattr(args, 't', 8)
output_functions = getattr(args, 'output_functions', {'mean_sd': True})
# Get the largest contour from the binary mask.
contour = ft.get_largest_contour(bin_mask, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE)
if contour == None:
raise ValueError("No contour found for binary image")
# Set the rotation.
if rotation == 'FIT_ELLIPSE':
box = cv2.fitEllipse(contour)
rotation = int(box[2])
if not 0 <= rotation <= 179:
raise ValueError("Rotation must be in the range 0 to 179, "\
"found %s" % rotation)
# Extract shape feature.
intersects, center = ft.shape_360(contour, rotation, step, t)
# Create a masked image.
if 'color_histograms' in output_functions:
img_masked = cv2.bitwise_and(self.img, self.img, mask=bin_mask)
# Run the output function for each angle.
means = []
sds = []
histograms = []
for angle in range(0, 360, step):
for f_name, f_args in vars(output_functions).iteritems():
# Mean distance + standard deviation.
if f_name == 'mean_sd':
distances = []
for p in intersects[angle]:
d = ft.point_dist(center, p)
distances.append(d)
if len(distances) == 0:
mean = 0
sd = 0
else:
mean = np.mean(distances, dtype=np.float32)
if len(distances) > 1:
sd = np.std(distances, ddof=1, dtype=np.float32)
else:
sd = 0
means.append(mean)
sds.append(sd)
# Color histograms.
if f_name == 'color_histograms':
# Get a line from the center to the outer intersection point.
line = None
if intersects[angle]:
line = ft.extreme_points([center] + intersects[angle])
# Create a mask for the line, where the line is foreground.
line_mask = np.zeros(self.img.shape[:2], dtype=np.uint8)
if line is not None:
cv2.line(line_mask, tuple(line[0]), tuple(line[1]),
255, 1)
# Create histogram from masked + line masked image.
hists = self.__get_color_histograms(
img_masked, f_args, line_mask)
histograms.append(hists)
means = means.astype(float)
sds = sds.astype(float)
# Normalize the features if a scaler is set.
if self.scaler and 'mean_sd' in output_functions:
means = self.scaler.fit_transform(means)
sds = self.scaler.fit_transform(sds)
# Group the means+sds together.
means_sds = np.array(zip(means, sds)).flatten()
return np.append(means_sds, histograms)
