Skip to content

cellects.image_analysis.one_image_analysis_threads

cellects.image_analysis.one_image_analysis_threads

Module implementing an image processing pipeline for segmentation and analysis.

This module defines functionality to process images using various color space combinations, filters, and segmentation techniques like K-means clustering or Otsu thresholding. It supports logical operations between images and iterative refinement of color space combinations by adding/removing channels based on validation criteria. The pipeline evaluates resulting binary masks for blob statistics and spatial relationships.

Classes ProcessImage : Processes an image according to parameterized instructions, performing color space combination, filtering, segmentation, and shape validation.

Notes Relies on external functions from cellects.image_analysis module for core operations like color space combination, filtering, PCA extraction, and segmentation.

ProcessImage

A class for processing an image according to a list of parameters.

Source code in src/cellects/image_analysis/one_image_analysis_threads.py 
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
class ProcessImage:
    """
    A class for processing an image according to a list of parameters.
    """
    def __init__(self, l):
        """
        Arguments:
            list : list
        """
        self.stats = None
        self.greyscale = None
        self.csc_dict = None
        self.start_processing(l)

    def start_processing(self, l: list):
        """
        Begin processing tasks based on the given process type.

        Parameters
        ----------
        l : list
            A list containing parameters and instructions for processing.
            Expected structure:
                - First element: self.parent (object)
                - Second element: params (dict)
                - Third element: process type (str) ('one', 'PCA', 'add', 'subtract', or 'logical')
                - Fourth element (if applicable): additional parameters based on process type.

        Notes
        -----
        This function modifies instance attributes directly and performs various operations based on the `process` type.
        """
        self.parent = l[0]
        self.fact = pd.DataFrame(np.zeros((1, len(self.parent.factors)), dtype=np.uint32), columns=self.parent.factors)
        self.params = l[1]
        process = l[2]
        self.all_c_spaces = self.parent.all_c_spaces
        if process == 'one':
            self.csc_dict = l[3]
            self.combine_and_segment()
            self.evaluate_segmentation()
        if process == 'filter':
            self.params['filter_spec'] = l[3]
            self.image = self.parent.image
            self.apply_filter_and_segment()
            self.evaluate_segmentation()
        elif process == 'PCA':
            self.pca_and_segment()
            self.evaluate_segmentation()
        elif process == 'add':
            self.try_adding_channels(l[3])
        elif process == 'subtract':
            self.try_subtracting_channels()
        elif process == 'logical':
            self.operation = l[3]
            self.apply_logical_operation()

    def combine_and_segment(self, csc_dict: Dict=None):
        """
        Combines color spaces and segments images into binary masks using either K-means clustering or Otsu's thresholding based on specified parameters.

        Parameters
        ----------
        csc_dict : dict or None
            Optional dictionary mapping color space abbreviations (e.g., 'bgr', 'hsv') to their relative contribution coefficients for combination. If not provided, uses the instance's `self.csc_dict` attribute instead.

        """
        if csc_dict is None:
            csc_dict = self.csc_dict
        self.image = combine_color_spaces(translate_dict(csc_dict), self.all_c_spaces)
        self.apply_filter_and_segment()

    def pca_and_segment(self):
        """
        Extract the first principal component and perform segmentation.

        This method extracts the first principal component from the 'bgr' color space
        and performs k-means clustering or Otsu thresholding to segment the image based on the
        parameters provided.
        """
        self.image, _, first_pc_vector = extract_first_pc(self.all_c_spaces['bgr'])
        self.csc_dict = Dict()
        self.csc_dict['bgr'] = List(first_pc_vector)
        self.apply_filter_and_segment()

    def apply_filter_and_segment(self):
        self.greyscale = self.image
        if self.params['filter_spec'] is not None and self.params['filter_spec']["filter1_type"] != "":
            self.greyscale = apply_filter(self.greyscale, self.params['filter_spec']["filter1_type"], self.params['filter_spec']["filter1_param"])


        if self.params['kmeans_clust_nb'] is not None and (self.params['bio_mask'] is not None or self.params['back_mask'] is not None):
            self.binary_image, _, self.bio_label, _ = kmeans(self.greyscale, None, self.params['kmeans_clust_nb'],
                                                             self.params['bio_mask'], self.params['back_mask'])
        else:
            self.binary_image = otsu_thresholding(self.greyscale)
        self.validated_shapes = self.binary_image

    def evaluate_segmentation(self):
        """
        Use the filtering algorithm based on the kind of image to analyse
        """
        if self.params['is_first_image']:
            self.eval_first_image()
        else:
            self.eval_any_image()

    def eval_first_image(self):
        """
        First image filtering process for binary images.

        This method processes the first binary image by identifying connected components, computing
        the total area of the image and its potential size limits. If the number of blobs and their
        total area fall within acceptable thresholds, it proceeds with additional processing and saving.
        """
        self.fact['unaltered_blob_nb'], shapes = cv2.connectedComponents(self.binary_image)
        self.fact['unaltered_blob_nb'] -= 1
        if 1 <= self.fact['unaltered_blob_nb'].values[0] < 10000:
            self.fact['total_area'] = np.sum(self.binary_image)
            inf_lim = np.min((20, np.ceil(self.binary_image.size / 1000)))
            if inf_lim < self.fact['total_area'].values[0] < self.binary_image.size * 0.9:
                self.process_first_binary_image()
                self.save_combination()

    def eval_any_image(self):
        """

        Summarizes the binary image analysis and determines blob characteristics.

        Evaluates a binary image to determine various attributes like surface area,
        blob number, and their relative positions within specified masks. It also
        checks for common areas with a reference image and saves the combination if
        certain conditions are met.
        """
        surf = self.binary_image.sum()
        if surf < self.params['total_surface_area']:
            self.fact['unaltered_blob_nb'], shapes = cv2.connectedComponents(self.binary_image)
            self.fact['unaltered_blob_nb'] -= 1
            test: bool = True
            if self.params['arenas_mask'] is not None:
                self.fact['out_of_arenas'] = np.sum(self.binary_image * self.params['out_of_arenas_mask'])
                self.fact['in_arenas'] = np.sum(self.binary_image * self.params['arenas_mask'])
                test = self.fact['out_of_arenas'].values[0] < self.fact['in_arenas'].values[0]
            if test:
                if self.params['con_comp_extent'][0] <= self.fact['unaltered_blob_nb'].values[0] <= self.params['con_comp_extent'][1]:
                    if self.params['ref_image'] is not None:
                        self.fact['common_with_ref'] = np.sum(self.params['ref_image'] * self.binary_image)
                        test = self.fact['common_with_ref'].values[0] > 0
                    if test:
                        self.fact['blob_nb'], shapes, self.stats, centroids = cv2.connectedComponentsWithStats(self.binary_image)
                        self.fact['blob_nb'] -= 1
                        if np.all(np.sort(self.stats[:, 4])[:-1] < self.params['max_blob_size']):
                            self.save_combination()

    def apply_logical_operation(self):
        """
        Apply a logical operation between two saved images.

        This method applies a specified logical operation ('And' or 'Or')
        between two images stored in the parent's saved_images_list. The result
        is stored as a binary image and validated, with color space information
        updated accordingly.

        Notes
        -----
        This method modifies the following instance attributes:
        - `binary_image`
        - `validated_shapes`
        - `image`
        - `csc_dict`
        """
        im1 = self.parent.saved_images_list[self.operation[0]]
        im2 = self.parent.saved_images_list[self.operation[1]]
        if self.operation['logical'] == 'And':
            self.binary_image = np.logical_and(im1, im2).astype(np.uint8)
        elif self.operation['logical'] == 'Or':
            self.binary_image = np.logical_or(im1, im2).astype(np.uint8)
        self.validated_shapes = self.binary_image
        self.greyscale = self.parent.converted_images_list[self.operation[0]]
        csc1 = self.parent.saved_color_space_list[self.operation[0]]
        csc2 = self.parent.saved_color_space_list[self.operation[1]]
        self.csc_dict = {}
        for k, v in csc1.items():
            self.csc_dict[k] = v
        for k, v in csc2.items():
            self.csc_dict[k] = v
        self.csc_dict['logical'] = self.operation['logical']
        self.evaluate_segmentation()

    def try_adding_channels(self, i: int):
        """
        Try adding channels to the current color space combination.

        Extend the functionality of a selected color space combination by attempting
        to add channels from other combinations, evaluating the results based on
        the number of shapes and total area.

        Parameters
        ----------
        i : int
            The index of the saved color space and combination features to start with.
        """
        saved_color_space_list = self.parent.saved_color_space_list
        combination_features = self.parent.combination_features
        self.csc_dict = saved_color_space_list[i]
        previous_shape_number = combination_features.loc[i, 'blob_nb']
        previous_sum = combination_features.loc[i, 'total_area']
        for j in self.params['possibilities'][::-1]:
            csc_dict2 = saved_color_space_list[j]
            csc_dict = self.csc_dict.copy()
            keys = list(csc_dict.keys())

            k2 = list(csc_dict2.keys())[0]
            v2 = csc_dict2[k2]
            if np.isin(k2, keys) and np.sum(v2 * csc_dict[k2]) != 0:
                break
            for factor in [2, 1]:
                if np.isin(k2, keys):
                    csc_dict[k2] += v2 * factor
                else:
                    csc_dict[k2] = v2 * factor
                self.combine_and_segment(csc_dict)
                if self.params['is_first_image']:
                    self.process_first_binary_image()
                else:
                    self.fact['blob_nb'], shapes, self.stats, centroids = cv2.connectedComponentsWithStats(self.validated_shapes)
                    self.fact['blob_nb'] -= 1
                self.fact['total_area'] = self.validated_shapes.sum()
                if self.fact['blob_nb'].values[0] < previous_shape_number  and self.fact['total_area'].values[0] > previous_sum * 0.9:
                    previous_shape_number = self.fact['blob_nb'].values[0]
                    previous_sum = self.fact['total_area'].values[0]
                    self.csc_dict = csc_dict.copy()
                    self.fact['unaltered_blob_nb'] = combination_features.loc[i, 'unaltered_blob_nb']
                    self.save_combination()

    def try_subtracting_channels(self):
        """
        Tries to subtract channels to find the optimal color space combination.

        This method attempts to remove color spaces one by one from the image
        to find a combination that maintains the majority of areas while reducing
        the number of color spaces. This process is repeated until no further
        improvements are possible.
        """
        potentials = self.parent.all_combined
        # Try to remove color space one by one
        i = 0
        original_length = len(potentials)
        # The while loop until one col space remains or the removal of one implies a strong enough area change
        while np.logical_and(len(potentials) > 1, i < original_length - 1):
            self.combine_and_segment(potentials)
            if self.params['is_first_image']:
                self.process_first_binary_image()
                previous_blob_nb = self.fact['blob_nb'].values[0]
            else:
                previous_blob_nb, shapes, self.stats, centroids = cv2.connectedComponentsWithStats(
                    self.validated_shapes)
                previous_blob_nb -= 1
            previous_sum = self.validated_shapes.sum()
            color_space_to_remove = List()
            for c_space in potentials.keys():
                try_potentials = potentials.copy()
                try_potentials.pop(c_space)
                self.combine_and_segment(try_potentials)
                if self.params['is_first_image']:
                    self.process_first_binary_image()
                else:
                    self.fact['blob_nb'], shapes, self.stats, centroids = cv2.connectedComponentsWithStats(
                        self.validated_shapes)
                    self.fact['blob_nb'] -= 1
                self.fact['total_area'] = self.validated_shapes.sum()
                if self.fact['blob_nb'].values[0] < previous_blob_nb and self.fact['total_area'].values[0] > previous_sum * 0.9:
                    previous_blob_nb = self.fact['blob_nb'].values[0]
                    previous_sum = self.fact['total_area'].values[0]
                    self.csc_dict = try_potentials.copy()
                    self.fact['unaltered_blob_nb'] = previous_blob_nb
                    self.save_combination()
                    # If removing that color space helps, we remove it from potentials
                    color_space_to_remove.append(c_space)
            if len(color_space_to_remove) == 0:
                break
            color_space_to_remove = np.unique(color_space_to_remove)
            for remove_col_space in color_space_to_remove:
                potentials.pop(remove_col_space)
            i += 1

    def process_first_binary_image(self):
        """
        Process the binary image to identify and validate shapes.

        This method processes a binary image to detect connected components,
        validate their sizes, and handle bio and back masks if specified.
        It ensures that the number of validated shapes matches the expected
        sample number or applies additional filtering if necessary.

        """
        shapes_features = shape_selection(self.binary_image, true_shape_number=self.params['blob_nb'],
                        horizontal_size=self.params['blob_size'], spot_shape=self.params['blob_shape'],
                        several_blob_per_arena=self.params['several_blob_per_arena'],
                        bio_mask=self.params['bio_mask'], back_mask=self.params['back_mask'])
        self.validated_shapes, self.fact['blob_nb'], self.stats, self.centroids = shapes_features

    def save_combination(self):
        """
        Saves the calculated features and masks for a combination of shapes.

        This method calculates various statistical properties (std) and sums for the
        validated shapes, and optionally computes sums for bio and back masks if they are
        specified in the parameters.

        Parameters
        ----------
        self : object
            The instance of the class containing this method. Must have attributes:
            - ``validated_shapes``: (ndarray) Array of validated shapes.
            - ``stats``: (ndarray) Statistics array containing width, height, and area.
            - ``params``: dictionary with parameters including 'bio_mask' and 'back_mask'.
            - ``fact``: dictionary to store the calculated features.
            - ``parent``: The parent object containing the method `save_combination_features`.
        """
        self.fact['total_area'] = self.validated_shapes.sum()
        self.fact['width_std'] = np.std(self.stats[1:, 2])
        self.fact['height_std'] = np.std(self.stats[1:, 3])
        self.fact['area_std'] = np.std(self.stats[1:, 4])
        if self.params['bio_mask'] is not None:
            self.fact['bio_sum'] = self.validated_shapes[self.params['bio_mask'][0], self.params['bio_mask'][1]].sum()
        if self.params['back_mask'] is not None:
            self.fact['back_sum'] = (1 - self.validated_shapes)[self.params['back_mask'][0], self.params['back_mask'][1]].sum()
        self.parent.save_combination_features(self)

__init__(l)

Arguments: list : list

Source code in src/cellects/image_analysis/one_image_analysis_threads.py 
35
36
37
38
39
40
41
42
43
def __init__(self, l):
    """
    Arguments:
        list : list
    """
    self.stats = None
    self.greyscale = None
    self.csc_dict = None
    self.start_processing(l)

apply_logical_operation()

Apply a logical operation between two saved images.

This method applies a specified logical operation ('And' or 'Or') between two images stored in the parent's saved_images_list. The result is stored as a binary image and validated, with color space information updated accordingly.

Notes

This method modifies the following instance attributes: - binary_image - validated_shapes - image - csc_dict

Source code in src/cellects/image_analysis/one_image_analysis_threads.py 
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
def apply_logical_operation(self):
    """
    Apply a logical operation between two saved images.

    This method applies a specified logical operation ('And' or 'Or')
    between two images stored in the parent's saved_images_list. The result
    is stored as a binary image and validated, with color space information
    updated accordingly.

    Notes
    -----
    This method modifies the following instance attributes:
    - `binary_image`
    - `validated_shapes`
    - `image`
    - `csc_dict`
    """
    im1 = self.parent.saved_images_list[self.operation[0]]
    im2 = self.parent.saved_images_list[self.operation[1]]
    if self.operation['logical'] == 'And':
        self.binary_image = np.logical_and(im1, im2).astype(np.uint8)
    elif self.operation['logical'] == 'Or':
        self.binary_image = np.logical_or(im1, im2).astype(np.uint8)
    self.validated_shapes = self.binary_image
    self.greyscale = self.parent.converted_images_list[self.operation[0]]
    csc1 = self.parent.saved_color_space_list[self.operation[0]]
    csc2 = self.parent.saved_color_space_list[self.operation[1]]
    self.csc_dict = {}
    for k, v in csc1.items():
        self.csc_dict[k] = v
    for k, v in csc2.items():
        self.csc_dict[k] = v
    self.csc_dict['logical'] = self.operation['logical']
    self.evaluate_segmentation()

combine_and_segment(csc_dict=None)

Combines color spaces and segments images into binary masks using either K-means clustering or Otsu's thresholding based on specified parameters.

Parameters:

Name Type Description Default
csc_dict dict or None

Optional dictionary mapping color space abbreviations (e.g., 'bgr', 'hsv') to their relative contribution coefficients for combination. If not provided, uses the instance's self.csc_dict attribute instead.

 None
Source code in src/cellects/image_analysis/one_image_analysis_threads.py 
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
def combine_and_segment(self, csc_dict: Dict=None):
    """
    Combines color spaces and segments images into binary masks using either K-means clustering or Otsu's thresholding based on specified parameters.

    Parameters
    ----------
    csc_dict : dict or None
        Optional dictionary mapping color space abbreviations (e.g., 'bgr', 'hsv') to their relative contribution coefficients for combination. If not provided, uses the instance's `self.csc_dict` attribute instead.

    """
    if csc_dict is None:
        csc_dict = self.csc_dict
    self.image = combine_color_spaces(translate_dict(csc_dict), self.all_c_spaces)
    self.apply_filter_and_segment()

eval_any_image()

Summarizes the binary image analysis and determines blob characteristics.

Evaluates a binary image to determine various attributes like surface area, blob number, and their relative positions within specified masks. It also checks for common areas with a reference image and saves the combination if certain conditions are met.

Source code in src/cellects/image_analysis/one_image_analysis_threads.py 
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
def eval_any_image(self):
    """

    Summarizes the binary image analysis and determines blob characteristics.

    Evaluates a binary image to determine various attributes like surface area,
    blob number, and their relative positions within specified masks. It also
    checks for common areas with a reference image and saves the combination if
    certain conditions are met.
    """
    surf = self.binary_image.sum()
    if surf < self.params['total_surface_area']:
        self.fact['unaltered_blob_nb'], shapes = cv2.connectedComponents(self.binary_image)
        self.fact['unaltered_blob_nb'] -= 1
        test: bool = True
        if self.params['arenas_mask'] is not None:
            self.fact['out_of_arenas'] = np.sum(self.binary_image * self.params['out_of_arenas_mask'])
            self.fact['in_arenas'] = np.sum(self.binary_image * self.params['arenas_mask'])
            test = self.fact['out_of_arenas'].values[0] < self.fact['in_arenas'].values[0]
        if test:
            if self.params['con_comp_extent'][0] <= self.fact['unaltered_blob_nb'].values[0] <= self.params['con_comp_extent'][1]:
                if self.params['ref_image'] is not None:
                    self.fact['common_with_ref'] = np.sum(self.params['ref_image'] * self.binary_image)
                    test = self.fact['common_with_ref'].values[0] > 0
                if test:
                    self.fact['blob_nb'], shapes, self.stats, centroids = cv2.connectedComponentsWithStats(self.binary_image)
                    self.fact['blob_nb'] -= 1
                    if np.all(np.sort(self.stats[:, 4])[:-1] < self.params['max_blob_size']):
                        self.save_combination()

eval_first_image()

First image filtering process for binary images.

This method processes the first binary image by identifying connected components, computing the total area of the image and its potential size limits. If the number of blobs and their total area fall within acceptable thresholds, it proceeds with additional processing and saving.

Source code in src/cellects/image_analysis/one_image_analysis_threads.py 
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
def eval_first_image(self):
    """
    First image filtering process for binary images.

    This method processes the first binary image by identifying connected components, computing
    the total area of the image and its potential size limits. If the number of blobs and their
    total area fall within acceptable thresholds, it proceeds with additional processing and saving.
    """
    self.fact['unaltered_blob_nb'], shapes = cv2.connectedComponents(self.binary_image)
    self.fact['unaltered_blob_nb'] -= 1
    if 1 <= self.fact['unaltered_blob_nb'].values[0] < 10000:
        self.fact['total_area'] = np.sum(self.binary_image)
        inf_lim = np.min((20, np.ceil(self.binary_image.size / 1000)))
        if inf_lim < self.fact['total_area'].values[0] < self.binary_image.size * 0.9:
            self.process_first_binary_image()
            self.save_combination()

evaluate_segmentation()

Use the filtering algorithm based on the kind of image to analyse

Source code in src/cellects/image_analysis/one_image_analysis_threads.py 
129
130
131
132
133
134
135
136
def evaluate_segmentation(self):
    """
    Use the filtering algorithm based on the kind of image to analyse
    """
    if self.params['is_first_image']:
        self.eval_first_image()
    else:
        self.eval_any_image()

pca_and_segment()

Extract the first principal component and perform segmentation.

This method extracts the first principal component from the 'bgr' color space and performs k-means clustering or Otsu thresholding to segment the image based on the parameters provided.

Source code in src/cellects/image_analysis/one_image_analysis_threads.py 
103
104
105
106
107
108
109
110
111
112
113
114
def pca_and_segment(self):
    """
    Extract the first principal component and perform segmentation.

    This method extracts the first principal component from the 'bgr' color space
    and performs k-means clustering or Otsu thresholding to segment the image based on the
    parameters provided.
    """
    self.image, _, first_pc_vector = extract_first_pc(self.all_c_spaces['bgr'])
    self.csc_dict = Dict()
    self.csc_dict['bgr'] = List(first_pc_vector)
    self.apply_filter_and_segment()

process_first_binary_image()

Process the binary image to identify and validate shapes.

This method processes a binary image to detect connected components, validate their sizes, and handle bio and back masks if specified. It ensures that the number of validated shapes matches the expected sample number or applies additional filtering if necessary.

Source code in src/cellects/image_analysis/one_image_analysis_threads.py 
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
def process_first_binary_image(self):
    """
    Process the binary image to identify and validate shapes.

    This method processes a binary image to detect connected components,
    validate their sizes, and handle bio and back masks if specified.
    It ensures that the number of validated shapes matches the expected
    sample number or applies additional filtering if necessary.

    """
    shapes_features = shape_selection(self.binary_image, true_shape_number=self.params['blob_nb'],
                    horizontal_size=self.params['blob_size'], spot_shape=self.params['blob_shape'],
                    several_blob_per_arena=self.params['several_blob_per_arena'],
                    bio_mask=self.params['bio_mask'], back_mask=self.params['back_mask'])
    self.validated_shapes, self.fact['blob_nb'], self.stats, self.centroids = shapes_features

save_combination()

Saves the calculated features and masks for a combination of shapes.

This method calculates various statistical properties (std) and sums for the validated shapes, and optionally computes sums for bio and back masks if they are specified in the parameters.

Parameters:

Name Type Description Default
self object

The instance of the class containing this method. Must have attributes: - validated_shapes: (ndarray) Array of validated shapes. - stats: (ndarray) Statistics array containing width, height, and area. - params: dictionary with parameters including 'bio_mask' and 'back_mask'. - fact: dictionary to store the calculated features. - parent: The parent object containing the method save_combination_features.

 required
Source code in src/cellects/image_analysis/one_image_analysis_threads.py 
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
def save_combination(self):
    """
    Saves the calculated features and masks for a combination of shapes.

    This method calculates various statistical properties (std) and sums for the
    validated shapes, and optionally computes sums for bio and back masks if they are
    specified in the parameters.

    Parameters
    ----------
    self : object
        The instance of the class containing this method. Must have attributes:
        - ``validated_shapes``: (ndarray) Array of validated shapes.
        - ``stats``: (ndarray) Statistics array containing width, height, and area.
        - ``params``: dictionary with parameters including 'bio_mask' and 'back_mask'.
        - ``fact``: dictionary to store the calculated features.
        - ``parent``: The parent object containing the method `save_combination_features`.
    """
    self.fact['total_area'] = self.validated_shapes.sum()
    self.fact['width_std'] = np.std(self.stats[1:, 2])
    self.fact['height_std'] = np.std(self.stats[1:, 3])
    self.fact['area_std'] = np.std(self.stats[1:, 4])
    if self.params['bio_mask'] is not None:
        self.fact['bio_sum'] = self.validated_shapes[self.params['bio_mask'][0], self.params['bio_mask'][1]].sum()
    if self.params['back_mask'] is not None:
        self.fact['back_sum'] = (1 - self.validated_shapes)[self.params['back_mask'][0], self.params['back_mask'][1]].sum()
    self.parent.save_combination_features(self)

start_processing(l)

Begin processing tasks based on the given process type.

Parameters:

Name Type Description Default
l list

A list containing parameters and instructions for processing. Expected structure: - First element: self.parent (object) - Second element: params (dict) - Third element: process type (str) ('one', 'PCA', 'add', 'subtract', or 'logical') - Fourth element (if applicable): additional parameters based on process type.

 required
Notes

This function modifies instance attributes directly and performs various operations based on the process type.

Source code in src/cellects/image_analysis/one_image_analysis_threads.py 
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
def start_processing(self, l: list):
    """
    Begin processing tasks based on the given process type.

    Parameters
    ----------
    l : list
        A list containing parameters and instructions for processing.
        Expected structure:
            - First element: self.parent (object)
            - Second element: params (dict)
            - Third element: process type (str) ('one', 'PCA', 'add', 'subtract', or 'logical')
            - Fourth element (if applicable): additional parameters based on process type.

    Notes
    -----
    This function modifies instance attributes directly and performs various operations based on the `process` type.
    """
    self.parent = l[0]
    self.fact = pd.DataFrame(np.zeros((1, len(self.parent.factors)), dtype=np.uint32), columns=self.parent.factors)
    self.params = l[1]
    process = l[2]
    self.all_c_spaces = self.parent.all_c_spaces
    if process == 'one':
        self.csc_dict = l[3]
        self.combine_and_segment()
        self.evaluate_segmentation()
    if process == 'filter':
        self.params['filter_spec'] = l[3]
        self.image = self.parent.image
        self.apply_filter_and_segment()
        self.evaluate_segmentation()
    elif process == 'PCA':
        self.pca_and_segment()
        self.evaluate_segmentation()
    elif process == 'add':
        self.try_adding_channels(l[3])
    elif process == 'subtract':
        self.try_subtracting_channels()
    elif process == 'logical':
        self.operation = l[3]
        self.apply_logical_operation()

try_adding_channels(i)

Try adding channels to the current color space combination.

Extend the functionality of a selected color space combination by attempting to add channels from other combinations, evaluating the results based on the number of shapes and total area.

Parameters:

Name Type Description Default
i int

The index of the saved color space and combination features to start with.

 required
Source code in src/cellects/image_analysis/one_image_analysis_threads.py 
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
def try_adding_channels(self, i: int):
    """
    Try adding channels to the current color space combination.

    Extend the functionality of a selected color space combination by attempting
    to add channels from other combinations, evaluating the results based on
    the number of shapes and total area.

    Parameters
    ----------
    i : int
        The index of the saved color space and combination features to start with.
    """
    saved_color_space_list = self.parent.saved_color_space_list
    combination_features = self.parent.combination_features
    self.csc_dict = saved_color_space_list[i]
    previous_shape_number = combination_features.loc[i, 'blob_nb']
    previous_sum = combination_features.loc[i, 'total_area']
    for j in self.params['possibilities'][::-1]:
        csc_dict2 = saved_color_space_list[j]
        csc_dict = self.csc_dict.copy()
        keys = list(csc_dict.keys())

        k2 = list(csc_dict2.keys())[0]
        v2 = csc_dict2[k2]
        if np.isin(k2, keys) and np.sum(v2 * csc_dict[k2]) != 0:
            break
        for factor in [2, 1]:
            if np.isin(k2, keys):
                csc_dict[k2] += v2 * factor
            else:
                csc_dict[k2] = v2 * factor
            self.combine_and_segment(csc_dict)
            if self.params['is_first_image']:
                self.process_first_binary_image()
            else:
                self.fact['blob_nb'], shapes, self.stats, centroids = cv2.connectedComponentsWithStats(self.validated_shapes)
                self.fact['blob_nb'] -= 1
            self.fact['total_area'] = self.validated_shapes.sum()
            if self.fact['blob_nb'].values[0] < previous_shape_number  and self.fact['total_area'].values[0] > previous_sum * 0.9:
                previous_shape_number = self.fact['blob_nb'].values[0]
                previous_sum = self.fact['total_area'].values[0]
                self.csc_dict = csc_dict.copy()
                self.fact['unaltered_blob_nb'] = combination_features.loc[i, 'unaltered_blob_nb']
                self.save_combination()

try_subtracting_channels()

Tries to subtract channels to find the optimal color space combination.

This method attempts to remove color spaces one by one from the image to find a combination that maintains the majority of areas while reducing the number of color spaces. This process is repeated until no further improvements are possible.

Source code in src/cellects/image_analysis/one_image_analysis_threads.py 
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
def try_subtracting_channels(self):
    """
    Tries to subtract channels to find the optimal color space combination.

    This method attempts to remove color spaces one by one from the image
    to find a combination that maintains the majority of areas while reducing
    the number of color spaces. This process is repeated until no further
    improvements are possible.
    """
    potentials = self.parent.all_combined
    # Try to remove color space one by one
    i = 0
    original_length = len(potentials)
    # The while loop until one col space remains or the removal of one implies a strong enough area change
    while np.logical_and(len(potentials) > 1, i < original_length - 1):
        self.combine_and_segment(potentials)
        if self.params['is_first_image']:
            self.process_first_binary_image()
            previous_blob_nb = self.fact['blob_nb'].values[0]
        else:
            previous_blob_nb, shapes, self.stats, centroids = cv2.connectedComponentsWithStats(
                self.validated_shapes)
            previous_blob_nb -= 1
        previous_sum = self.validated_shapes.sum()
        color_space_to_remove = List()
        for c_space in potentials.keys():
            try_potentials = potentials.copy()
            try_potentials.pop(c_space)
            self.combine_and_segment(try_potentials)
            if self.params['is_first_image']:
                self.process_first_binary_image()
            else:
                self.fact['blob_nb'], shapes, self.stats, centroids = cv2.connectedComponentsWithStats(
                    self.validated_shapes)
                self.fact['blob_nb'] -= 1
            self.fact['total_area'] = self.validated_shapes.sum()
            if self.fact['blob_nb'].values[0] < previous_blob_nb and self.fact['total_area'].values[0] > previous_sum * 0.9:
                previous_blob_nb = self.fact['blob_nb'].values[0]
                previous_sum = self.fact['total_area'].values[0]
                self.csc_dict = try_potentials.copy()
                self.fact['unaltered_blob_nb'] = previous_blob_nb
                self.save_combination()
                # If removing that color space helps, we remove it from potentials
                color_space_to_remove.append(c_space)
        if len(color_space_to_remove) == 0:
            break
        color_space_to_remove = np.unique(color_space_to_remove)
        for remove_col_space in color_space_to_remove:
            potentials.pop(remove_col_space)
        i += 1