Best Python code snippet using prospector_python
__init__.py
Source: __init__.py
...28 self._zoom = zoom29 @property30 def zoom(self):31 return self._zoom32def _example_path(extension):33 return os.path.join(os.path.dirname(__file__),'files',extension)34def _icon_path(suffix):35 return os.path.join(os.path.dirname(__file__), 'icons', suffix)36_grid_obstacle_dict = dict({'ego-xvar-module' : 'robot',37 'ego-xvar-name' : 'ax',38 'ego-yvar-module' : 'robot',39 'ego-yvar-name': 'ay',40 'xmax-constant': 'axMAX',41 'ymax-constant': 'ayMAX',42 'target-label': 'goal',43 'traps-label': 'traps'44 })45def obstacle(N, full_observable = False):46 if full_observable:47 return Model(_example_path("obstacle_full_observable.nm"), ProgramAnnotation(_grid_obstacle_dict),48 ["Pmax=? [ \"notbad\" U \"goal\"]"], constants=f"N={N}")49 return Model(_example_path("obstacle.nm"), ProgramAnnotation(_grid_obstacle_dict),50 ["Pmax=? [ \"notbad\" U \"goal\"]"], constants=f"N={N}")51_drone_dict = dict({'ego-xvar-module' : 'drone',52 'ego-xvar-name' : 'dx',53 'ego-yvar-module' : 'drone',54 'ego-yvar-name': 'dy',55 'adv-xvar-module': 'agent',56 'adv-xvar-name': 'ax',57 'adv-yvar-module': 'agent',58 'adv-yvar-name': 'ay',59 'xmax-constant': 'xMAX',60 'ymax-constant': 'yMAX',61 'target-label': 'goal',62 'ego-radius-constant' : "RADIUS",63 'scan-action': 'scan',64 'adv-area': ['a'],65 'traps-label': None66 })67def evade(N,RADIUS):68 return Model(_example_path("evade.nm"), ProgramAnnotation(_drone_dict),69 ["Pmax=? [\"notbad\" U \"goal\"]"], constants=f"N={N},RADIUS={RADIUS}")70_intercept_dict = dict({'ego-xvar-module' : 'drone',71 'ego-xvar-name' : 'dx',72 'ego-yvar-module' : 'drone',73 'ego-yvar-name': 'dy',74 'adv-xvar-module': 'agent',75 'adv-xvar-name': 'ax',76 'adv-yvar-module': 'agent',77 'adv-yvar-name': 'ay',78 'xmax-constant': 'dxMAX',79 'ymax-constant': 'dyMAX',80 'traps-label': None,81 'ego-radius-constant': "RADIUS",82 'camera': ['CAMERA'],83 'adv-goals-label': 'exits'84 })85def intercept(N,RADIUS):86 return Model(_example_path("intercept.nm"), ProgramAnnotation(_intercept_dict),87 ["Pmax=? [\"notbad\" U \"goal\"]"], constants=f"N={N},RADIUS={RADIUS}")88_surveillance_dict = dict({'ego-xvar-module' : 'drone',89 'ego-xvar-name' : 'dx',90 'ego-yvar-module' : 'drone',91 'ego-yvar-name': 'dy',92 'adv-xvar-module': ['agent','agent2'],93 'adv-xvar-name': ['ax','ax2'],94 'adv-yvar-module': ['agent','agent2'],95 'adv-yvar-name': ['ay','ay2'],96 'adv-dirvar-module': ['agent', 'agent2'],97 'adv-dirvar-name': ['dir', 'dir2'],98 'xmax-constant': 'xMAX',99 'ymax-constant': 'yMAX',100 'target-label': 'goal',101 'traps-label': None,102 'adv-dirvalue-mapping': {1: Direction.WEST, 0: Direction.EAST},103 'adv-radius-constant' : "ARADIUS",104 'ego-radius-constant' : "RADIUS"105 })106def surveillance(N,RADIUS=2):107 return Model(_example_path("avoid.nm"), ProgramAnnotation(_surveillance_dict), ["Pmax=? [\"notbad\" U \"goal\"]"], constants=f"N={N},RADIUS={RADIUS}")108_grid_refuel = dict({'ego-xvar-module' : 'rover',109 'ego-xvar-name' : 'ax',110 'ego-yvar-module' : 'rover',111 'ego-yvar-name': 'ay',112 'xmax-constant': 'axMAX',113 'ymax-constant': 'ayMAX',114 'target-label': 'goal',115 'traps-label': 'traps',116 'landmarks': 'stationvisit',117 'resource-module': 'tank',118 'resource-variable': 'fuel',119 'resource-name': 'fuel',120 'resource-maximum-constant': 'fuelCAP'121 })122def refuel(N, ENERGY):123 return Model(_example_path("refuel.nm"), ProgramAnnotation(_grid_refuel), ["Pmax=? [\"notbad\" U \"goal\"]"], constants=f"N={N},ENERGY={ENERGY}")124_grid_rocks = dict({'ego-xvar-module' : 'robot',125 'ego-xvar-name' : 'x',126 'ego-yvar-module' : 'robot',127 'ego-yvar-name': 'y',128 'xmax-constant': 'xMAX',129 'ymax-constant': 'yMAX',130 'target-label': 'goal',131 'traps-label' : None,132 'interactive-landmarks-x': ['r1x', 'r2x'],133 'interactive-landmarks-y': ['r1y', 'r2y'],134 'il-statusvar-module': ['rock1', 'rock2'],135 'il-statusvar-name': ['r1qual', 'r2qual'],136 'il-clearancevar-module': ['rock1', 'rock2'],137 'il-clearancevar-name': ['r1taken', 'r2taken'],138 'goal-action' : True139 })140def rocks(N, K=2):141 K = int(K)142 if K == 2:143 return Model(_example_path("rocks2.nm"), ProgramAnnotation(_grid_rocks), ["Pmax=? [\"notbad\" U \"goal\"]"], constants=f"N={N}")144 else:...
tf_io_pipline_tools.py
Source: tf_io_pipline_tools.py
1#!/usr/bin/env python32# -*- coding: utf-8 -*-3# @Time : 19-2-15 ä¸å2:134# @Author : MaybeShewill-CV5# @Site : https://github.com/MaybeShewill-CV/CRNN_Tensorflow6# @File : tf_io_pipline_tools.py7# @IDE: PyCharm8"""9Some tensorflow records io tools10"""11import os12import os.path as ops13import cv214import tensorflow as tf15import glog as log16from config import global_config17CFG = global_config.cfg18_R_MEAN = 123.6819_G_MEAN = 116.7820_B_MEAN = 103.9421_CHANNEL_MEANS = [_B_MEAN, _G_MEAN, _R_MEAN]22def int64_feature(value):23 """24 :return:25 """26 return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))27def bytes_feature(value):28 """29 :param value:30 :return:31 """32 return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))33def write_example_tfrecords(example_paths, example_labels, tfrecords_path):34 """35 write tfrecords36 :param example_paths:37 :param example_labels:38 :param tfrecords_path:39 :return:40 """41 _tfrecords_dir = ops.split(tfrecords_path)[0]42 os.makedirs(_tfrecords_dir, exist_ok=True)43 log.info('Writing {:s}....'.format(tfrecords_path))44 with tf.python_io.TFRecordWriter(tfrecords_path) as _writer:45 for _index, _example_path in enumerate(example_paths):46 with open(_example_path, 'rb') as f:47 check_chars = f.read()[-2:]48 if check_chars != b'\xff\xd9':49 log.error('Image file {:s} is not complete'.format(_example_path))50 continue51 else:52 _example_image = cv2.imread(_example_path, cv2.IMREAD_COLOR)53 _example_image = cv2.resize(_example_image,54 dsize=(CFG.TRAIN.IMG_WIDTH, CFG.TRAIN.IMG_HEIGHT),55 interpolation=cv2.INTER_CUBIC)56 _example_image_raw = _example_image.tostring()57 _example = tf.train.Example(58 features=tf.train.Features(59 feature={60 'height': int64_feature(CFG.TRAIN.IMG_HEIGHT),61 'width': int64_feature(CFG.TRAIN.IMG_WIDTH),62 'depth': int64_feature(3),63 'label': int64_feature(example_labels[_index]),64 'image_raw': bytes_feature(_example_image_raw)65 }))66 _writer.write(_example.SerializeToString())67 log.info('Writing {:s} complete'.format(tfrecords_path))68 return69def decode(serialized_example):70 """71 Parses an image and label from the given `serialized_example`72 :param serialized_example:73 :return:74 """75 features = tf.parse_single_example(76 serialized_example,77 # Defaults are not specified since both keys are required.78 features={79 'image_raw': tf.FixedLenFeature([], tf.string),80 'label': tf.FixedLenFeature([], tf.int64),81 'height': tf.FixedLenFeature([], tf.int64),82 'width': tf.FixedLenFeature([], tf.int64),83 'depth': tf.FixedLenFeature([], tf.int64)84 })85 # decode image86 image = tf.decode_raw(features['image_raw'], tf.uint8)87 image_shape = tf.stack([CFG.TRAIN.IMG_HEIGHT, CFG.TRAIN.IMG_WIDTH, 3])88 image = tf.reshape(image, image_shape)89 # Convert label from a scalar int64 tensor to an int32 scalar.90 label = tf.cast(features['label'], tf.int32)91 return image, label92def augment_for_train(image, label):93 """94 :param image:95 :param label:96 :return:97 """98 # first apply random crop99 image = tf.image.random_crop(value=image,100 size=[CFG.TRAIN.CROP_IMG_HEIGHT, CFG.TRAIN.CROP_IMG_WIDTH, 3],101 seed=tf.set_random_seed(1234),102 name='crop_image')103 # apply random flip104 image = tf.image.random_flip_left_right(image=image, seed=tf.set_random_seed(1234))105 return image, label106def augment_for_validation(image, label):107 """108 :param image:109 :param label:110 :return:111 """112 assert CFG.TRAIN.IMG_HEIGHT == CFG.TRAIN.IMG_WIDTH113 assert CFG.TRAIN.CROP_IMG_HEIGHT == CFG.TRAIN.CROP_IMG_WIDTH114 # apply central crop115 central_fraction = CFG.TRAIN.CROP_IMG_HEIGHT / CFG.TRAIN.IMG_HEIGHT116 image = tf.image.central_crop(image=image, central_fraction=central_fraction)117 return image, label118def normalize(image, label):119 """120 Normalize the image data by substracting the imagenet mean value121 :param image:122 :param label:123 :return:124 """125 if image.get_shape().ndims != 3:126 raise ValueError('Input must be of size [height, width, C>0]')127 image_fp = tf.cast(image, dtype=tf.float32)128 means = tf.expand_dims(tf.expand_dims(_CHANNEL_MEANS, 0), 0)...
utils.py
Source: utils.py
1import re2class DataFiles:3 _code_path = ""4 _input_path = ""5 _example_path = ""6 input = ""7 inputEX = ""8 raw_input = ""9 raw_inputEX = ""10 def __init__(self, path):11 self._code_path = path12 self._input_path = path.replace("code", "input").replace("Day", "").replace(".py", ".txt")13 self._example_path = re.sub(r"(\w+)(.txt$)", r"Example\g<2>", self._input_path)14 self._inputFile = open(self._input_path)15 self._inputEXFile = open(self._example_path)16 self.input = self.get_input()17 self.inputEX = self.get_inputEX()18 def __del__(self):19 self._inputFile.close()20 self._inputEXFile.close()21 def print_paths(self):22 print(self._code_path)23 print(self._input_path)24 print(self._example_path)25 def get_input(self):26 return self._inputFile.read().splitlines()27 def get_inputEX(self):28 return self._inputEXFile.read().splitlines()29 def get_input_raw(self):30 return self._inputFile.read()31 def get_inputEX_raw(self):...
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