def process_image(args, path):
global img, img_src, outline, box
img = cv2.imread(path)
if img == None or img.size == 0:
logging.error("Failed to read %s" % path)
exit(1)
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.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE)
# Get bounding rectange of the largest contour.
box = cv2.boundingRect(contour)
# Get the outline.
logging.info("- Obtaining shape...")
outline = ft.shape_outline(contour, args.k)
# And draw it.
logging.info("- Done")
draw_outline(0, outline, args.k)
def __get_shape_outline(self, args, bin_mask):
"""Executes :meth:`features.shape_outline`."""
if self.bin_mask == None:
raise ValueError("Binary mask cannot be None")
k = getattr(args, 'k', 15)
# Obtain contours (all points) from the mask.
contour = ft.get_largest_contour(bin_mask.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE)
if contour == None:
raise ValueError("No contour found for binary image")
# Get the outline.
outline = ft.shape_outline(contour, k)
# Compute the delta's for the horizontal and vertical point pairs.
shape = []
for x, y in outline:
delta_x = x[0] - x[1]
delta_y = y[0] - y[1]
shape.append(delta_x)
shape.append(delta_y)
shape = np.array(shape, dtype=float)
# Normalize the features if a scaler is set.
if self.scaler:
shape = self.scaler.fit_transform(shape)
return shape
def get_shape_outline(self, args, bin_mask):
if self.bin_mask == None:
raise ValueError("Binary mask cannot be None")
k = getattr(args, 'k', 15)
# Obtain contours (all points) from the mask.
contour = ft.get_largest_contour(bin_mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
if contour == None:
raise ValueError("No contour found for binary image")
# Get the outline.
outline = ft.shape_outline(contour, k)
# Compute the delta's for the horizontal and vertical point pairs.
shape = []
for x, y in outline:
delta_x = x[0] - x[1]
delta_y = y[0] - y[1]
shape.append(delta_x)
shape.append(delta_y)
# Normalize results.
shape = np.array(shape, dtype=np.float32)
shape = cv2.normalize(shape, None, -1, 1, cv2.NORM_MINMAX)
return shape.ravel()
def __get_shape_outline(self, args, bin_mask):
"""Executes :meth:`features.shape_outline`."""
if self.bin_mask == None:
raise ValueError("Binary mask cannot be None")
k = getattr(args, 'k', 15)
# Obtain contours (all points) from the mask.
contour = ft.get_largest_contour(bin_mask.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE)
if contour == None:
raise ValueError("No contour found for binary image")
# Get the outline.
outline = ft.shape_outline(contour, k)
# Compute the delta's for the horizontal and vertical point pairs.
shape = []
for x, y in outline:
delta_x = x[0] - x[1]
delta_y = y[0] - y[1]
shape.append(delta_x)
shape.append(delta_y)
shape = np.array(shape, dtype=float)
# Normalize the features if a scaler is set.
if self.scaler:
shape = self.scaler.fit_transform(shape)
return shape
def process_image(args, path):
global img, img_src, outline, box
img = cv2.imread(path)
if img == None or img.size == 0:
logging.error("Failed to read %s" % path)
exit(1)
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.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
# Get bounding rectange of the largest contour.
box = cv2.boundingRect(contour)
# Get the outline.
logging.info("- Obtaining shape...")
outline = ft.shape_outline(contour, args.k)
# And draw it.
logging.info("- Done")
draw_outline(0, outline, args.k)