How to use test_pyqt5 method in Pyscreenshot

Best Python code snippet using pyscreenshot_python

data.py

Source:data.py

1import os, math, random2import numpy as np3from abc import ABCMeta, abstractclassmethod4from road_detect_project.augmenter.aerial import Creator5from road_detect_project.model.params import dataset_params6# class DataLoader():7#     """load a class """8#9#     @staticmethod10#     def create():11#         pass12class AbstractDataset(metaclass=ABCMeta):13    """14    All dataloader should inherit from this class. 15    Implements chunking of the dataset. For instance,16    subsets of examples are loaded onto GPU iteratively during an epoch.17    This also includes a chunk switching method.18    """19    def __init__(self):20        self.data_set = {"test_PyQt5": None,21                         "train": None,22                         "valid": None}23        self.all_training = []24        self.active = []25        self.all_shared_hook = []  # WHen casted, cannot set_value on them26        self.nr_examples = {}27    @abstractclassmethod28    def load(self, dataset_path):29        """Loading and transforming logic for dataset"""30        return31    def destroy(self):32        pass33    def get_chunk_number(self):34        return len(self.all_training)35    def get_elements(self, idx):36        return len(self.all_training[idx][0])37    def get_total_number_of_batches(self, batch_size):38        s = sum(len(c[0]) for c in self.all_training)39        return math.ceil(s / batch_size)40    def _chunkify(self, dataset, nr_of_chunks, batch_size):41        # Round items per chunk down until there is an exact number of minibatches.42        # Multiple of batch_size43        items_per_chunk = len(dataset[0]) / nr_of_chunks44        if items_per_chunk < batch_size:45            print("Chunk limit too small,or batch size too large.\n"46                  "Each chunk must include at least one batch.")47            raise Exception("Fix chunk_size and batch size.")48        temp = int(items_per_chunk / batch_size)49        items_per_chunk = batch_size * temp50        data, labels = dataset51        # TODO:do floatX operation twice.52        chunks = [[AbstractDataset._float32(data[x:x + items_per_chunk]),53                   AbstractDataset._float32(labels[x:x + items_per_chunk])]54                  for x in range(0, len(dataset[0]), items_per_chunk)]55        # If the last chunk is less than batch size, it is cut.56        # No reason for an unnecessary swap.57        last_chunk_size = len(chunks[-1][0])58        if last_chunk_size < batch_size * 15:59            chunks.pop(-1)60            print("------ Remove last chunk."61                  " {} elements not enough for at least one minibatch of {}".format(62                last_chunk_size, batch_size))63        return chunks64    def set_nr_examples(self, train, valid, test):65        self.nr_examples["train"] = train[0].shape[0]66        self.nr_examples["valid"] = valid[0].shape[0]67        self.nr_examples["test_PyQt5"] = test[0].shape[0]68    def get_report(self):69        return self.nr_examples70    def switch_active_training_set(self, idx):71        """72        Each epoch a large number of examples will be seen by model. 73        Often all examples will not fit on the GPU atthe same time.74        This method, switches the data that are currently reciding in the gpu.75        Will be called nr_of_chunks times per epoch.76        """77        new_chunk_x, new_chunk_y = AbstractDataset._list_to_arr(self.all_training[idx])78        self.active[0] = new_chunk_x79        self.active[1] = new_chunk_y80    def shared_dataset(self, data_xy, cast_to_int=True):81        data_x, data_y = data_xy82        # print(data_x.shape)83        # print(data_y.shape)84        # shared_x = tf.Variable(data_x)85        # shared_y = tf.Variable(data_y)86        shared_x = data_x87        shared_y = data_y88        self.all_shared_hook.append(shared_y)89        if cast_to_int:90            # print("---- Casted to int")91            # Since labels are index integers they have to be treated as such during computations.92            # Shared_y is therefore cast to int.93            return shared_x, shared_y94        else:95            return shared_x, shared_y96    @staticmethod97    def _float32(d):98        return np.asarray(d, dtype="float32")99    @staticmethod100    def _list_to_arr(d):101        d_x, d_y = d102        d_x = np.asarray(d_x, dtype="float32")103        d_y = np.asarray(d_y, dtype="float32")104        return d_x, d_y105    @staticmethod106    def _get_file_path(dataset):107        data_dir, data_file = os.path.split(dataset)108        # TODO: Add some robustness, like checking if file is folder and correct that109        assert os.path.isfile(dataset)110        return dataset111    @staticmethod112    def dataset_check(name, dataset, batch_size):113        # If there are are to few examples for at least one batch,114        # the dataset is invalid.115        if len(dataset[0]) < batch_size:116            print("Insufficent examples in {}. {} examples not enough "117                  "for at least one minibatch".format(118                name, len(dataset[0])))119            raise Exception("Decrease batch_size or increase samples_per_image")120    @staticmethod121    def dataset_sizes(train, valid, test, chunks):122        mb = 1000000.0123        train_size = sum(data.nbytes for data in train) / mb124        valid_size = sum(data.nbytes for data in valid) / mb125        test_size = sum(data.nbytes for data in test) / mb126        nr_of_chunks = math.ceil(train_size / chunks)127        print('---- Minimum number of training chunks: {}'.format(nr_of_chunks))128        print('---- Dataset at least:')129        print('---- Training: \t {}mb'.format(train_size))130        print('---- Validation: {}mb'.format(valid_size))131        print('---- Testing: \t {}mb'.format(test_size))132        return nr_of_chunks133    @staticmethod134    def dataset_shared_stats(image_shape, label_shape, chunks):135        print('')136        print('Preparing shared variables for datasets')137        print('---- Image data shape: {}, label data shape: {}'.format(image_shape, label_shape))138        print('---- Max chunk size of {}mb'.format(chunks))139    @staticmethod140    def dataset_chunk_stats(nr_training_chunks, elements_pr_chunk, elements_last_chunk):141        print('---- Actual number of training chunks: {}'.format(nr_training_chunks))142        print('---- Elements per chunk: {}'.format(elements_pr_chunk))143        print('---- Last chunk size: {}'.format(elements_last_chunk))144class AerialDataset(AbstractDataset):145    def load(self, dataset_path, params=None, batch_size=16):146        print("Creating aerial image dataset")147        self.std = params.dataset_std148        chunks = params.chunk_size149        # TODO: ensure that the dataset is as expected.150        creator = Creator(dataset_path,151                          dim=(params.input_dim, params.output_dim),152                          rotation=params.use_rotation,153                          preprocessing=params.use_preprocessing,154                          std=self.std,155                          only_mixed=params.only_mixed_labels,156                          reduce_testing=params.reduce_testing,157                          reduce_training=params.reduce_training,158                          reduce_validation=params.reduce_validation)159        train, valid, test = creator.dynamically_create(160            params.samples_per_image,161            enable_label_noise=params.use_label_noise,162            label_noise=params.label_noise,163            only_mixed=params.only_mixed_labels)164        # Testing dataset size requirements165        AerialDataset.dataset_check("train", train, batch_size)166        AerialDataset.dataset_check("valid", valid, batch_size)167        AerialDataset.dataset_check("test_PyQt5", test, batch_size)168        # print("*********************************************")169        # print(train[1].shape)170        # print(test_PyQt5[0].shape)171        AbstractDataset.dataset_shared_stats(train[0].shape, train[1].shape, chunks)172        self.set_nr_examples(train, valid, test)173        nr_of_chunks = AbstractDataset.dataset_sizes(train, valid, test, chunks)174        training_chunks = self._chunkify(train, nr_of_chunks, batch_size)175        AerialDataset.dataset_chunk_stats(len(training_chunks),176                                          len(training_chunks[0][0]),177                                          len(training_chunks[-1][0]))178        AbstractDataset.dataset_chunk_stats(179            len(training_chunks),180            len(training_chunks[0][0]),181            len(training_chunks[-1][0]))182        self.active = list(self.shared_dataset(training_chunks[0], cast_to_int=False))183        self.data_set['train'] = self.active184        self.data_set['valid'] = self.shared_dataset(valid, cast_to_int=True)185        self.data_set['test_PyQt5'] = self.shared_dataset(test, cast_to_int=True)186        # Not stored on the GPU, unlike the shared variables defined above.187        self.all_training = training_chunks188        return True189    def gen_data(self, data_name, epoch=1000, batch_size=16):190        chunks = self.get_chunk_number()191        for i in range(epoch):192            if data_name == "train":193                for chunk in range(chunks):194                    self.switch_active_training_set(chunk)195                    nr_elements = self.get_elements(chunk)196                    train_data = self.data_set[data_name]197                    batches = [[train_data[0][x:x + batch_size], train_data[1][x:x + batch_size]]198                               for x in range(0, nr_elements, batch_size)]199                    for batch in batches:200                        yield batch201            else:202                data = self.data_set[data_name]203                nr_elements = data[0].shape[0]204                batches = [[data[0][x:x + batch_size], data[1][x:x + batch_size]]205                           for x in range(0, nr_elements, batch_size)]206                for batch in batches:207                    yield batch208class AerialCurriculumDataset(AbstractDataset):209    """210    Data loader for pre-generated dataset. 211    IE, curriculum learning and datasets too big to fit in main memory.212    The class includes  a method for stage switching and mixing. 213    this method switches the training set and control the behavior of the switch.214    """215    def load_set(self, path, set, stage=None):216        base_path = ''217        if stage is not None:218            base_path = os.path.join(path, set, stage)219        else:220            base_path = os.path.join(path, set)221        data = np.load(os.path.join(base_path, "data", "examples.npy"))222        labels = np.load(os.path.join(base_path, "labels", "examples.npy"))223        return data, labels224    def mix_in_next_stage(self):225        self.stage += 1226        if self.nr_of_stages <= self.stage:227            print("No more stage available")228            return229        current_stage = "stage{}".format(self.stage)230        labels = np.load(os.path.join(self.stage_path, current_stage, "labels", "examples.npy"))231        data = np.load(os.path.join(self.stage_path, current_stage, "data", "examples.npy"))232        print("------ Mixing in {} with {} examples".format(current_stage, data.shape[0]))233        if not dataset_params.with_replacement:234            elements = data.shape[0]235            shuffle_count = 0236            shuffle_index = list(range(elements))237            random.shuffle(shuffle_index)238            for c in range(len(self.all_training)):239                nr_chunk_examples = self.all_training[c][0].shape[0]240                for x in range(nr_chunk_examples):241                    if shuffle_count < elements:242                        i = shuffle_index.pop()243                        self.all_training[c][0][x] = data[i]244                        self.all_training[c][1][x] = labels[i]245                    else:246                        break247                    shuffle_count += 1248        else:249            nr_chunks = len(self.all_training)250            for i in range(data.shape[0]):251                c = random.randint(0, nr_chunks - 1)252                nr_chunk_examples = self.all_training[c][0].shape[0]253                x = random.randint(0, nr_chunk_examples - 1)254                self.all_training[c][0][x] = data[i]255                self.all_training[c][0][x] = labels[i]256    def load(self, dataset_path, params=None, batch_size=16):257        print("------- Loading aerial curriculum dataset")258        chunks = params.chunk_size259        self.std = params.dataset_std260        # For later stage loading261        self.stage = 0262        self.stage_path = os.path.join(dataset_path, "train")263        self.nr_of_stages = len(os.listdir(self.stage_path))264        train = self.load_set(dataset_path, "train", stage="stage{}".format(self.stage))265        valid = self.load_set(dataset_path, "valid")266        test = self.load_set(dataset_path, "test_PyQt5")267        # Testing dataset size requirements268        AerialCurriculumDataset.dataset_check("train", train, batch_size)269        AerialCurriculumDataset.dataset_check("valid", valid, batch_size)270        AerialCurriculumDataset.dataset_check("test_PyQt5", test, batch_size)271        AerialCurriculumDataset.dataset_shared_stats(train[0].shape, train[1].shape, chunks)272        self.set_nr_examples(train, valid, test)273        nr_of_chunks = AerialCurriculumDataset.dataset_sizes(train, valid, test, chunks)274        training_chunks = self._chunkify(train, nr_of_chunks, batch_size)275        AerialCurriculumDataset.dataset_chunk_stats(len(training_chunks),276                                                    len(training_chunks[0][0]),277                                                    len(training_chunks[-1][0]))278        self.active = list(self.shared_dataset(training_chunks[0], cast_to_int=False))279        self.data_set["train"] = self.active280        self.data_set["valid"] = self.shared_dataset(valid, cast_to_int=True)281        self.data_set["test_PyQt5"] = self.shared_dataset(test, cast_to_int=True)282        # Not stored on the GPU, unlike the shared variables defined above.283        self.all_training = training_chunks284        return True285if __name__ == '__main__':286    # dataset = AerialDataset()287    # path = r"/media/sunwl/sunwl/datum/roadDetect_project/Massachusetts/"288    # params = dataset_params289    # dataset.load(path, params=params)290    # dataset.switch_active_training_set(0)291    # train_data = dataset.data_set['train']292    # print("train")293    # print(train_data[0].shape)294    # print(train_data[1].shape)295    # valid_data = dataset.data_set['valid']296    # print("valid")297    # next(dataset.gen_data("valid", epoch=1))298    # print(valid_data[0].shape)299    # print(valid_data[1].shape)300    # test_data = dataset.data_set['test_PyQt5']301    # print("test_PyQt5")302    # print(test_data[0].shape)303    # print(test_data[1].shape)304    # print(dataset.get_report())305    # dataset.switch_active_training_set(2)306    # dataset.get_elements(2)307    # new_data = dataset.data_set['train']308    # print(new_data[0].shape)309    # Test curriculum dataset310    dataset_2 = AerialCurriculumDataset()311    path = "../tools/my_data/"312    dataset_2.load(path, params=dataset_params)313    dataset_2.switch_active_training_set(0)314    print("train\n")315    train_data = dataset_2.data_set["train"]316    print(train_data[0].shape)317    print(train_data[1].shape)318    print("valid\n")319    valid_data = dataset_2.data_set["valid"]320    print(train_data[0].shape)321    print(train_data[1].shape)322    print("test_PyQt5\n")323    test_data = dataset_2.data_set["test_PyQt5"]324    print(train_data[0].shape)...

main.py

Source:main.py

1from sources.PyGS import *2import sys3def test_pyqt5(style):4    app = QApplication(sys.argv)5    # Force the style to be the same on all OSs:6    app.setStyle('Windows')7    # Now use a palette to switch to dark colors:8    palette = QPalette()9    palette.setColor(QPalette.Window, QColor(56, 56, 56))10    palette.setColor(QPalette.WindowText, Qt.white)11    palette.setColor(QPalette.Base, QColor(56, 56, 56))12    palette.setColor(QPalette.AlternateBase, QColor(63, 63, 63))13    palette.setColor(QPalette.ToolTipBase, Qt.white)14    palette.setColor(QPalette.ToolTipText, Qt.white)15    palette.setColor(QPalette.Text, Qt.white)16    palette.setColor(QPalette.Button, QColor(56, 56, 56))17    palette.setColor(QPalette.ButtonText, Qt.white)18    palette.setColor(QPalette.BrightText, QColor(0, 128, 152))19    palette.setColor(QPalette.Link, QColor(42, 130, 218))20    palette.setColor(QPalette.Highlight, QColor(0, 128, 152))21    palette.setColor(QPalette.HighlightedText, Qt.white)22    palette.setColor(QPalette.Disabled, QPalette.Window, QColor(51, 51, 51))23    palette.setColor(QPalette.Disabled, QPalette.ButtonText, QColor(111, 111, 111))24    palette.setColor(QPalette.Disabled, QPalette.Text, QColor(122, 118, 113))25    palette.setColor(QPalette.Disabled, QPalette.WindowText, QColor(122, 118, 113))26    palette.setColor(QPalette.Disabled, QPalette.Base, QColor(32, 32, 32))27    if style:28        app.setPalette(palette)29    path = os.path.dirname(os.path.realpath(__file__))30    ex = PyGS(path)31    sys.exit(app.exec_())32if __name__ == '__main__':...

test_pyqt5.py

Source:test_pyqt5.py

1'''2*********************************************************************************************3  *File: test_pyqt5.py4  *Project: servodogVersion2.05  *Filepath: /home/guoyucan/ServoDogVersion1.0/servodogVersion2.0/test_pyqt5.py 6  *File Created: Sunday, 28th November 2021 8:24:59 pm7  *Author: Guo Yucan, 12032421@mail.sustech.edu.cn 8  *Last Modified: Sunday, 28th November 2021 8:25:02 pm9  *Modified By: Guo Yucan, 12032421@mail.sustech.edu.cn 10  *Copyright @ 2021 , BionicDL LAB, SUSTECH, Shenzhen, China 11*********************************************************************************************12'''13import sys 14from PyQt5.QtWidgets import (QWidget, QToolTip, QPushButton, QApplication)15from PyQt5.QtGui import QFont 16class Example(QWidget):17    def __init__(self):18        super().__init__()19        self.initUI()20    21    def initUI(self):...

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