# -*- python -*-
#
# Copyright INRIA - CIRAD - INRA
#
# Distributed under the Cecill-C License.
# See accompanying file LICENSE.txt or copy at
# http://www.cecill.info/licences/Licence_CeCILL-C_V1-en.html
#
# OpenAlea WebSite : http://openalea.gforge.inria.fr
#
# ==============================================================================
"""
Algorithms to threshold image
"""
# ==============================================================================
from __future__ import division, print_function
import cv2
import numpy
# ==============================================================================
[docs]
def threshold_meanshift(image, mean_image, threshold=0.3, reverse=False, mask=None):
"""
Threshold pixels in numpy array such as::
image / mean <= (1.0 - threshold)
If reverse is True (Inequality is reversed)::
image / mean <= (1.0 + threshold)
Parameters
----------
image : numpy.ndarray of integers
3-D array
mean_image : numpy.ndarray of the same shape as 'image'
3-D array 'mean_image'
threshold : float, optional
Threshold value. Must between 0.0 and 1.0
reverse : bool, optional
If True reverse inequality
mask : numpy.ndarray, optional
Array of same shape as `image`. Only points at which mask == True
will be thresholded.
Returns
-------
out : numpy.ndarray
Thresholded binary image
See Also
--------
get_mean_image, threshold_hsv
"""
# ==========================================================================
# Check Parameters
if not isinstance(image, numpy.ndarray):
raise TypeError("image should be a numpy.ndarray")
if not isinstance(mean_image, numpy.ndarray):
raise TypeError("mean should be a numpy.ndarray")
if not isinstance(reverse, bool):
raise TypeError("reverse should be a bool")
if image.ndim != 3:
raise ValueError("image should be 3D array")
if mean_image.ndim != 3:
raise ValueError("mean should be 3D array")
if image.shape != mean_image.shape:
raise ValueError("image and mean must have equal sizes")
if not (0.0 <= threshold <= 1.0):
raise ValueError("threshold must be between 0.0 and 1.0")
if mask is not None:
if not isinstance(mask, numpy.ndarray):
raise TypeError("mask should be a numpy.ndarray")
if mask.ndim != 2:
raise ValueError("mask should be 2D array")
if image.shape[0:2] != mask.shape:
raise ValueError("mask and image must have equal sizes")
# ==========================================================================
with numpy.errstate(divide="ignore", invalid="ignore"):
img = numpy.divide(numpy.float32(image), numpy.float32(mean_image))
img[~numpy.isfinite(img)] = 0
if reverse:
# Take max value of RGB tuple
img = img.max(2)
out = img >= (1.0 + threshold)
else:
# Take min value of RGB tuple
img = img.min(2)
out = img <= (1.0 - threshold)
out = numpy.uint8(out)
if mask is not None:
out = numpy.bitwise_and(out, mask)
del img
return out * 255
[docs]
def threshold_meanshift_enhance(image, mean_image, threshold=0.3, mask=None):
# ==========================================================================
image[image[:, :, 0] == 0] = 1
image[image[:, :, 1] == 0] = 1
image[image[:, :, 2] == 0] = 1
mean_image[mean_image[:, :, 0] == 0] = 1
mean_image[mean_image[:, :, 1] == 0] = 1
mean_image[mean_image[:, :, 2] == 0] = 1
img = numpy.divide(numpy.float32(image), numpy.float32(mean_image))
# Take min value of RGB tuple
out = img.min(2) <= (1.0 - threshold)
out = numpy.uint8(out)
if mask is not None:
out = numpy.bitwise_and(out, mask)
del img
return out * 255
[docs]
def threshold_hsv(image, hsv_min, hsv_max, mask=None):
"""
Binarize HSV image with hsv_min and hsv_max parameters.
=> cv2.inRange(hsv_image, hsv_min, hsv_max)
If mask is not None :
=> cv2.bitwise_and(binary_hsv_image, mask)
Parameters
----------
image : numpy.ndarray of integers
3-D array of image RGB
hsv_min : tuple of integers
HSV value of minimum range
hsv_max : tuple of integers
HSV value of maximum range
mask : numpy.ndarray, optional
Array of same shape as `image`. Only points at which mask == True
will be thresholded.
Returns
-------
out : numpy.ndarray
Thresholded binary image
See Also
--------
threshold_meanshift
"""
# ==========================================================================
# Check Parameters
if not isinstance(image, numpy.ndarray):
raise TypeError("image should be a numpy.ndarray")
if image.ndim != 3:
raise ValueError("image should be 3D array")
if not isinstance(hsv_min, tuple):
raise TypeError("hsv_min should be a Tuple")
if len(hsv_min) != 3:
raise ValueError("hsv_min should be of size 3")
for value in hsv_min:
if not isinstance(value, int):
raise ValueError("hsv_min value should be a integer")
if not isinstance(hsv_max, tuple):
raise TypeError("hsv_max should be a Tuple")
if len(hsv_max) != 3:
raise ValueError("hsv_max should be of size 3")
for value in hsv_max:
if not isinstance(value, int):
raise ValueError("hsv_max value should be a integer")
if mask is not None:
if not isinstance(mask, numpy.ndarray):
raise TypeError("mask should be a numpy.ndarray")
if mask.ndim != 2:
raise ValueError("mask should be 2D array")
if image.shape[0:2] != mask.shape:
raise ValueError("image and mask should have the same shape")
# ==========================================================================
out = cv2.inRange(image, hsv_min, hsv_max)
if mask is not None:
out = numpy.bitwise_and(out, mask)
return out
