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 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 split_image(path, args):
img = cv2.imread(path)
if img == None or img.size == 0:
sys.stderr.write("Failed to read %s. Skipping.\n" % path)
return -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)
logging.info("Segmenting...")
# Perform segmentation.
mask = common.grabcut(img, args.iters, None, args.margin)
# Create a binary mask. Foreground is made white, background black.
bin_mask = np.where((mask==cv2.GC_FGD) + (mask==cv2.GC_PR_FGD), 255, 0).astype('uint8')
# Split the image into segments.
segments = ft.split_by_mask(img, bin_mask)
logging.info("Exporting segments...")
for i, im in enumerate(segments):
if sum(im.shape[:2]) < args.min_size_out:
continue
name = os.path.basename(path)
name = os.path.splitext(name)
out_path = "%s_%d%s" % (name[0], i, name[1])
out_path = os.path.join(args.output, out_path)
logging.info("\t%s" % out_path)
cv2.imwrite(out_path, im)
return 0
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 process_image(args, path):
global img, img_src
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')
contours, hierarchy = cv2.findContours(bin_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# Calculate contour properties.
logging.info("- Computing contour properties...")
props = ft.contour_properties(contours, 'all')
# Print properties.
pprint.pprint(props)
# Draw some properties.
draw_props(props)
def process_image(args, path):
global img, img_src, outline, box, bin_mask
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.
props = ft.contour_properties([contour], 'BoundingRect')
box = props[0]['BoundingRect']
# And draw it.
logging.info("- Done")
draw_sections(0, args.bins)
def main():
print __doc__
parser = argparse.ArgumentParser(description='Test image segmentation')
parser.add_argument('image', metavar='FILE', help='Input image')
parser.add_argument('--max-size', metavar='N', type=float, help="Scale the input image down if its perimeter exceeds N. Default is no scaling.")
args = parser.parse_args()
img = cv2.imread(args.image)
if img == None or img.size == 0:
sys.stderr.write("Failed to read %s\n" % args.image)
return -1
sys.stderr.write("Processing %s...\n" % args.image)
# Scale the image down if its perimeter exceeds the maximum (if set).
img = common.scale_max_perimeter(img, args.max_size)
# Linear enhancement
enhanced1 = ft.naik_murthy_linear(img)
# Nonlinear enhancement using S-type function
enhanced2a = ft.naik_murthy_nonlinear(enhanced1, ft.s_type_enhancement, 0, 3, 0.5, 2)
enhanced2b = ft.naik_murthy_nonlinear(enhanced1, ft.s_type_enhancement, 0, 3, 1.5, 2)
# Enhancement using histogram equalization
sums = bgr_to_sums(img) # Range 0..(255*3-1)
sums = cv2.normalize(sums, None, 0, 255, cv2.NORM_MINMAX) # Range 0..255
sums = cv2.equalizeHist(np.uint8(sums))
sums = cv2.normalize(np.float32(sums), None, 0, 3, cv2.NORM_MINMAX) # Range 0..3
enhanced3 = ft.naik_murthy_nonlinear(img, sums, fmap=True)
# Display the image in a window.
cv2.namedWindow('image')
cv2.imshow('image', img)
while True:
k = cv2.waitKey(0) & 0xFF
if k == ord('0'):
cv2.imshow('image', img)
elif k == ord('1'):
cv2.imshow('image', enhanced1)
elif k == ord('2'):
cv2.imshow('image', enhanced2a)
elif k == ord('w'):
cv2.imshow('image', enhanced2b)
elif k == ord('3'):
cv2.imshow('image', enhanced3)
elif k == 27:
break
cv2.destroyAllWindows()
return 0
def main():
print __doc__
parser = argparse.ArgumentParser(description="Get image color statistics")
parser.add_argument("image", metavar="FILE", help="Input image")
args = parser.parse_args()
img = cv2.imread(args.image)
if img == None or img.size == 0:
sys.stderr.write("Failed to read %s\n" % args.image)
return 1
sys.stderr.write("Processing %s...\n" % args.image)
# Scale the image down if its perimeter exceeds the maximum.
img = common.scale_max_perimeter(img, 1000)
cs_str = "BGR"
hists = ft.color_histograms(img)
for i, hist in enumerate(hists):
print "%s: %s" % (cs_str[i], hist.astype(int).ravel())
print "BGR ranges:", get_min_max(img)
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
cs_str = "HSV"
hists = ft.color_histograms(img_hsv)
for i, hist in enumerate(hists):
print "%s: %s" % (cs_str[i], hist.astype(int).ravel())
print "HSV ranges:", get_min_max(img_hsv)
img_luv = cv2.cvtColor(img, cv2.COLOR_BGR2LUV)
cs_str = "Luv"
hists = ft.color_histograms(img_luv)
for i, hist in enumerate(hists):
print "%s: %s" % (cs_str[i], hist.astype(int).ravel())
print "LUV ranges:", get_min_max(img_luv)
return 0
def main():
print __doc__
parser = argparse.ArgumentParser(description='Get image color statistics')
parser.add_argument('image', metavar='FILE', help='Input image')
args = parser.parse_args()
img = cv2.imread(args.image)
if img == None or img.size == 0:
sys.stderr.write("Failed to read %s\n" % args.image)
return 1
sys.stderr.write("Processing %s...\n" % args.image)
# Scale the image down if its perimeter exceeds the maximum.
img = common.scale_max_perimeter(img, 1000)
cs_str = "BGR"
hists = ft.color_histograms(img)
for i, hist in enumerate(hists):
print "%s: %s" % (cs_str[i], hist.astype(int).ravel())
print "BGR ranges:", get_min_max(img)
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
cs_str = "HSV"
hists = ft.color_histograms(img_hsv)
for i, hist in enumerate(hists):
print "%s: %s" % (cs_str[i], hist.astype(int).ravel())
print "HSV ranges:", get_min_max(img_hsv)
img_luv = cv2.cvtColor(img, cv2.COLOR_BGR2LUV)
cs_str = "Luv"
hists = ft.color_histograms(img_luv)
for i, hist in enumerate(hists):
print "%s: %s" % (cs_str[i], hist.astype(int).ravel())
print "LUV ranges:", get_min_max(img_luv)
return 0
def split_image(path, args):
img = cv2.imread(path)
if img == None or img.size == 0:
sys.stderr.write("Failed to read %s. Skipping.\n" % path)
return -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)
logging.info("Segmenting...")
# Perform segmentation.
mask = common.grabcut(img, args.iters, None, args.margin)
# Create a binary mask. Foreground is made white, background black.
bin_mask = np.where((mask == cv2.GC_FGD) + (mask == cv2.GC_PR_FGD), 255,
0).astype('uint8')
# Split the image into segments.
segments = ft.split_by_mask(img, bin_mask)
logging.info("Exporting segments...")
for i, im in enumerate(segments):
if sum(im.shape[:2]) < args.min_size_out:
continue
name = os.path.basename(path)
name = os.path.splitext(name)
out_path = "%s_%d%s" % (name[0], i, name[1])
out_path = os.path.join(args.output, out_path)
logging.info("\t%s" % out_path)
cv2.imwrite(out_path, im)
return 0
def main():
print __doc__
parser = argparse.ArgumentParser(description='Test image segmentation')
parser.add_argument('image', metavar='FILE', help='Input image')
parser.add_argument('--iters',
metavar='N',
type=int,
default=5,
help="The number of grabCut iterations. Default is 5.")
parser.add_argument(
'--margin',
metavar='N',
type=int,
default=1,
help=
"The margin of the foreground rectangle from the edges. Default is 1.")
parser.add_argument(
'--max-size',
metavar='N',
type=float,
help=
"Scale the input image down if its perimeter exceeds N. Default is no scaling."
)
parser.add_argument(
'--algo',
metavar='simple|grabcut',
type=str,
choices=['simple', 'grabcut'],
default='grabcut',
help="The segmentation algorithm to use, either 'simple' or 'grabcut'."
)
parser.add_argument(
'--roi',
metavar='x,y,w,h',
type=str,
help="Region Of Interest, expressed as X,Y,Width,Height in pixel units."
)
args = parser.parse_args()
img = cv2.imread(args.image)
if img == None or img.size == 0:
sys.stderr.write("Failed to read %s\n" % args.image)
return -1
sys.stderr.write("Processing %s...\n" % args.image)
# Scale the image down if its perimeter exceeds the maximum (if set).
img = common.scale_max_perimeter(img, args.max_size)
# Process region of interest argument
roi = None
if args.roi != None:
roi = args.roi.split(',')
roi[0] = int(roi[0])
roi[1] = int(roi[1])
roi[2] = int(roi[2])
roi[3] = int(roi[3])
# Perform segmentation.
if args.algo == 'grabcut':
mask = common.grabcut(img, args.iters, roi, args.margin)
else:
mask = common.simple(img, roi)
# Create a binary mask. Foreground is made white, background black.
bin_mask = np.where((mask == cv2.GC_FGD) + (mask == cv2.GC_PR_FGD), 255,
0).astype('uint8')
# Create a binary mask for the largest contour.
contour = ft.get_largest_contour(bin_mask, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
mask_contour = np.zeros(bin_mask.shape, dtype=np.uint8)
cv2.drawContours(mask_contour, [contour], 0, 255, -1)
cv2.drawContours(img, [contour], 0, common.COLOR['green'], 1)
# Merge the binary mask with the image.
img_masked = cv2.bitwise_and(img, img, mask=bin_mask)
img_masked_contour = cv2.bitwise_and(img, img, mask=mask_contour)
# Display the image in a window.
cv2.namedWindow('image')
cv2.imshow('image', img_masked)
while True:
k = cv2.waitKey(0) & 0xFF
if k == ord('o'):
cv2.imshow('image', img)
elif k == ord('s'):
cv2.imshow('image', img_masked)
elif k == ord('l'):
cv2.imshow('image', img_masked_contour)
elif k == ord('q'):
break
cv2.destroyAllWindows()
return 0
def main():
print __doc__
parser = argparse.ArgumentParser(description='Test image segmentation')
parser.add_argument('image', metavar='FILE', help='Input image')
parser.add_argument('--iters', metavar='N', type=int, default=5, help="The number of grabCut iterations. Default is 5.")
parser.add_argument('--margin', metavar='N', type=int, default=1, help="The margin of the foreground rectangle from the edges. Default is 1.")
parser.add_argument('--max-size', metavar='N', type=float, help="Scale the input image down if its perimeter exceeds N. Default is no scaling.")
parser.add_argument('--algo', metavar='simple|grabcut', type=str, choices=['simple', 'grabcut'], default='grabcut', help="The segmentation algorithm to use, either 'simple' or 'grabcut'.")
parser.add_argument('--roi', metavar='x,y,w,h', type=str, help="Region Of Interest, expressed as X,Y,Width,Height in pixel units.")
args = parser.parse_args()
img = cv2.imread(args.image)
if img == None or img.size == 0:
sys.stderr.write("Failed to read %s\n" % args.image)
return -1
sys.stderr.write("Processing %s...\n" % args.image)
# Scale the image down if its perimeter exceeds the maximum (if set).
img = common.scale_max_perimeter(img, args.max_size)
# Process region of interest argument
roi = None
if args.roi != None:
roi = args.roi.split(',')
roi[0] = int(roi[0])
roi[1] = int(roi[1])
roi[2] = int(roi[2])
roi[3] = int(roi[3])
# Perform segmentation.
if args.algo == 'grabcut':
mask = common.grabcut(img, args.iters, roi, args.margin)
else:
mask = common.simple(img, roi)
# Create a binary mask. Foreground is made white, background black.
bin_mask = np.where((mask==cv2.GC_FGD) + (mask==cv2.GC_PR_FGD), 255, 0).astype('uint8')
# Create a binary mask for the largest contour.
contour = ft.get_largest_contour(bin_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
mask_contour = np.zeros(bin_mask.shape, dtype=np.uint8)
cv2.drawContours(mask_contour, [contour], 0, 255, -1)
cv2.drawContours(img, [contour], 0, common.COLOR['green'], 1)
# Merge the binary mask with the image.
img_masked = cv2.bitwise_and(img, img, mask=bin_mask)
img_masked_contour = cv2.bitwise_and(img, img, mask=mask_contour)
# Display the image in a window.
cv2.namedWindow('image')
cv2.imshow('image', img_masked)
while True:
k = cv2.waitKey(0) & 0xFF
if k == ord('o'):
cv2.imshow('image', img)
elif k == ord('s'):
cv2.imshow('image', img_masked)
elif k == ord('l'):
cv2.imshow('image', img_masked_contour)
elif k == ord('q'):
break
cv2.destroyAllWindows()
return 0
