How to use get_image_parameters method in avocado

Best Python code snippet using avocado_python

dicom.py

Source:dicom.py

...32        self.target_rois = []  33    def get_patient_id(self):34        return pydicom.read_file(self.rs_file).PatientID35            36    def get_image_parameters(self, ct_files):37        '''Currently this should return the parameters of the last one'''38        slices = []39        scans = [pydicom.read_file(s) for s in ct_files]40        scans.sort(key = lambda x: int(x.InstanceNumber))41        for ct in scans:        42            slices.append(ct.ImagePositionPatient[2])            43        44        center = (ct.ImagePositionPatient[0],ct.ImagePositionPatient[1])45        center = np.around(center, decimals=1)46        47        spacing = (float(ct.PixelSpacing[0]) ,float(ct.PixelSpacing[1]), float(ct.SliceThickness))48        dims = (int(ct.Rows), int(ct.Columns), len(ct_files))49        50        return slices, center, spacing, dims51    def set_target_rois(self,roi_candidates):52        for roi_name in roi_candidates:            53            if self.check_contour_data(roi_name): 54                self.target_rois.append(roi_name)                55        return self.target_rois56    def check_contour_data(self,roi):57        try:58            roi_index = self.get_structures(self.rs_file)[roi]['index']59            contour_data = self.get_contour_data(self.rs_file,roi_index)            60            assert len(contour_data) > 061            return True62        except:63            return False64    65    def get_slice_contours(self, contour_data, slices):66        slice_contours = {k: [] for k in slices}67        for i in range(0, len(contour_data)):            68            slice_contours[contour_data[i][2]].append(contour_data[i])69        return slice_contours70    71    def get_structures(self, rs_file=None):72        if rs_file is None:73            rs_file = self.rs_file        74        rs = pydicom.read_file(rs_file)75        structures = {}        76        for i in range(len(rs.StructureSetROISequence)):77            structures[rs.StructureSetROISequence[i].ROIName] = {}78            structures[rs.StructureSetROISequence[i].ROIName]['index'] = i79        return structures80    def get_contour_data(self, rs_file, roi_index):81        rs = pydicom.read_file(rs_file)82        contour_seq = rs.ROIContourSequence[roi_index].ContourSequence        83        roi_contour_data = []84        for slice_contour in contour_seq:            85            slice_contour_data = slice_contour.ContourData           86            for i in range(len(slice_contour_data)):87                slice_contour_data[i] = slice_contour_data[i]88            roi_contour_data.append(slice_contour_data)89        return roi_contour_data90    91   92    def get_label_volume(self, roi=None, target_index=0):93        if roi == None:94            roi = self.target_rois[target_index]        95        ds_file = self.rs_file96        patient_dir =self.dicom_dir97        ct_file_list = self.ct_files98        slices, center, spacing, dims = self.get_image_parameters(ct_file_list)    99        100        roi_index = self.get_structures(ds_file)[roi]['index']101        contour_data = self.get_contour_data(ds_file, roi_index)102        slice_contours = self.get_slice_contours(contour_data, slices)103        label_volume = np.zeros(dims, dtype=np.dtype('uint8'))         104        for k, sl  in enumerate(slice_contours):             105            contour = slice_contours[sl]            106            slice_label = self.draw_slice_label(contour, center, spacing, dims)107            label_volume[:, :, k] = slice_label108        109        if slices[0] > slices[1]:            110            label_volume = label_volume[:, :, ::-1]111        label_volume = label_volume.reshape(dims)112        return label_volume113    def draw_slice_label(self, contour, center, spacing, dims):114        img = Image.new("1", (dims[0], dims[1]))115        draw = ImageDraw.Draw(img)116        slice_label = np.zeros([dims[0], dims[1]])117        for c in contour:        118            x = [c[i] for i in range(0, len(c)) if i % 3 == 0]119            y = [c[i] for i in range(0, len(c)) if i % 3 == 1]120            x.append(x[0])121            y.append(y[0])        122            poly = [(int((x - center[0]) / spacing[0]), int((y - center[1]) / spacing[0])) for x, y in zip(x, y)]123            draw.polygon(poly, fill=1, outline=1)124            for i in range(0, dims[1]):125                for j in range(0, dims[0]):126                    slice_label[i, j] = img.getpixel((j, i))127        return slice_label128    def get_image_volume(self):129        ct_file_list = self.ct_files130        slices, _, _, dims = self.get_image_parameters(ct_file_list)131        image_volume = np.zeros(dims)132        idx = []133        for ct_file in ct_file_list:  134            idx.append(ct_file_list.index(ct_file))135            ct = pydicom.read_file(ct_file)136            image_volume[:, :, ct.InstanceNumber-1] = ct.pixel_array        137        if slices[0] > slices[1]:            138            image_volume = image_volume[:, :, ::-1]139        image_volume = image_volume * ct.RescaleSlope + ct.RescaleIntercept    140        image_volume = image_volume.reshape(dims)141        return image_volume142def get_patient_dict(rs_file_list):143    patient_dict = {}144    for rs_file in rs_file_list:    145        rs = pydicom.read_file(rs_file)146        patient_dict[int(rs.PatientID)] = rs_file147    return patient_dict148def parse_directory(directory, all_scans = False): 149    p_dict = {}150    for patient_id, respiratory_phases in directory.items():            151            p_dict[patient_id] = {}152            for i, respiratory_phase in enumerate(respiratory_phases):                   153                    ct_dir, rs_dir = respiratory_phase['CT'], respiratory_phase['RT']154                    p = DicomInterface(dicom_dir=ct_dir, rs_dir=rs_dir)155                    p_dict[patient_id][i] = p156   157    return p_dict158def discover_names(p_dict, roi_tags, not_roi_tags=None):159    '''This method searches for a specific set of indicator strings within each found ROI tag to find a specific ROI.160    '''161    # roi_tags should be a list of strings162    tags = set()163    roi_tags = list(map(lambda x: x.lower(), roi_tags))    164    for patient in p_dict.keys():                   165            for j, phase in p_dict[patient].items():               166                structures = phase.get_structures()                                           167                for s in structures:168                    flag = False169                    for tag in roi_tags:                        170                        if tag in s.lower():171                            flag = True172                        else:173                            flag = False174                            break175        176                    for not_tag in not_roi_tags:177                        if not_tag in s:178                            flag = False179                        else:180                            continue                        181                    182                    if flag:183                        tags.add(s)                                             184                        185    return tags186def assign_roi_label(p_dict, roi_candidates):187    del_list = []188    m_count = 0    189    s_count = 0190    for patient in p_dict.keys():                 191            for i, phase in p_dict[patient].items(): 192                p_dict[patient][i].set_target_rois(roi_candidates)   193                if len( p_dict[patient][i].target_rois) == 1:194                    s_count = s_count + 1195                if len( p_dict[patient][i].target_rois) > 1:196                    m_count = m_count + 1197                if len( p_dict[patient][i].target_rois) < 1:198                    del_list.append(patient + '_' + str(i))199  200    return len(del_list), m_count, s_count, p_dict201def extract_cases(p_dict, data_dir, base_name):202    if not os.path.exists(data_dir):203        os.makedirs(data_dir) 204    205    patients = list(p_dict.keys())206    207    error = {}208    count = 0209    for patient in patients:       210        patient_dir = data_dir + patient + '/'211        if not os.path.exists(patient_dir):212            os.makedirs(patient_dir)213        scan_dir = patient_dir + 'original_data/'214        if not os.path.exists(scan_dir):215            os.makedirs(scan_dir)   216            217           218        for i, phase in p_dict[patient].items(): 219                _, _, spacing, _ =  p_dict[patient][i].get_image_parameters(p_dict[patient][i].ct_files)                220                count = count + 1                221                if 0 in spacing:222                    continue223                #new_spacing = (1.0, 1.0, 1.0)224                225                labs = {}226                img = p_dict[patient][i].get_image_volume()227                # applying Hounsfield Unit window228                #window_center = -300229                #window_width = 1400230                #img = HU_window(img, window_center, window_width)                231                232                for idx, label in enumerate(p_dict[patient][i].target_rois):233                    lab = p_dict[patient][i].get_label_volume(target_index = idx)                     ...

test_vision.py

Source:test_vision.py

...53        self.assertTrue(cam_event.wait(10))54        self.assertIsNone(self.vision.get_img_compressed.unsubscribe())55    def test_image_parameters(self):56        self.assertIsInstance(self.vision.get_image_parameters, NiryoTopic)57        self.assertIsInstance(self.vision.get_image_parameters(), ImageParameters)58        img_param_event = Event()59        img_param_event.clear()60        def img_param_callback(img_param):61            self.assertIsInstance(img_param, ImageParameters)62            img_param_event.set()63        self.assertIsNone(self.vision.get_image_parameters.subscribe(img_param_callback))64        self.assertTrue(img_param_event.wait(10))65        self.assertIsNone(self.vision.get_image_parameters.unsubscribe())66        old_img_param = self.vision.get_image_parameters.value67        for i, function in enumerate(68                [self.vision.set_brightness, self.vision.set_contrast, self.vision.set_saturation]):69            new_value = old_img_param[i] + 1.070            self.assertIsNone(function(new_value))71            self.assertEqual(self.vision.get_image_parameters()[i], new_value)72            self.assertIsNone(function(1))73            self.assertEqual(self.vision.get_image_parameters()[i], 1)74            with self.assertRaises(RobotCommandException):75                function("1")76            with self.assertRaises(RobotCommandException):77                function(True)78    def test_workspace(self):79        ws_name = "unit_test_ws"80        self.assertIsNone(self.vision.delete_workspace(ws_name))81        unit_test_ws_poses = [ws_name,82                              [0.3, -0.1, 0.0, 0.0, 1.57, 0.0],83                              PoseObject(0.3, 0.1, 0.0, 0.0, 1.57, 0.0),84                              PoseObject(0.1, 0.1, 0.0, 0.0, 1.57, 0.0),85                              [0.1, -0.1, 0.0, 0.0, 1.57, 0.0]]86        unit_test_ws_points = [ws_name,87                               [0.3, -0.1, 0.0],...

snrseries.py

Source:snrseries.py

...9other_particle_range = 2510particle_distance = 10111213def get_image_parameters():14    (particles_center_x, particles_center_y) = deeptracknew.particle_positions(particle_number, first_particle_range, other_particle_range, particle_distance)15    image_parameters = {}1617    image_parameters['Particle Center X List'] = particles_center_x18    image_parameters['Particle Center Y List'] = particles_center_y19    image_parameters['Particle Radius List'] = uniform(1, 3, particle_number)20    image_parameters['Particle Bessel Orders List'] = [[1, ], 21                                                       [1, ],22                                                       [1, ], 23                                                       [1, ]]24    image_parameters['Particle Intensities List'] = [[uniform(0.5, 0.7, 1), ], 25                                                     [uniform(0.5, 0.7, 1), ],26                                                     [uniform(0.5, 0.7, 1), ], 27                                                     [uniform(0.5, 0.7, 1), ]]28    image_parameters['Image Half-Size'] = 2529    image_parameters['Image Background Level'] = uniform(0,.2)30    image_parameters['Signal to Noise Ratio'] = uniform(8,10)31    image_parameters['Gradient Intensity'] = uniform(0, 0.1)32    image_parameters['Gradient Direction'] = uniform(-0.2, 0.2)#-pi to pi usually33    image_parameters['Ellipsoid Orientation'] = uniform(-pi, pi, particle_number)34    image_parameters['Ellipticity'] = uniform(1,1.3)3536    return image_parameters373839image_parameters_function = lambda : get_image_parameters()4041### Define image generator42image_generator = lambda : deeptracknew.get_image_generator(image_parameters_function)4344### Show some examples of generated images45number_of_images_to_show = 104647for image_number, image, image_parameters in image_generator():48    if image_number>=number_of_images_to_show:49        break50    51    deeptracknew.plot_sample_image(image, image_parameters)52### Load the pretrained network53saved_network_file_name = 'neuromorph_psf_new Network 2020-07-25-172716 C-16-32-64 D-32-32 training-8x5001-32x4001-128x2501-512x1001-1024x101.h5'
...

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