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annotate points2label.py @ 5:dc91192a7150 draft default tip

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planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/points2labelimage/ commit edac062b00490276ef00d094e0594abdb3a3f23c
author imgteam
date Thu, 06 Nov 2025 09:59:19 +0000
parents 64c155acb864
children
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1 import argparse
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2 import json
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3 import warnings
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4 from typing import (
5
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5 Any,
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6 Dict,
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7 Optional,
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8 Tuple,
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9 )
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10
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11 import giatools.pandas
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12 import numpy as np
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13 import numpy.typing as npt
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14 import pandas as pd
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15 import scipy.ndimage as ndi
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16 import skimage.draw
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17 import skimage.io
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18 import skimage.segmentation
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21 def get_list_depth(nested_list: Any) -> int:
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22 if isinstance(nested_list, list):
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23 if len(nested_list) > 0:
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24 return 1 + max(map(get_list_depth, nested_list))
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25 else:
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26 return 1
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27 else:
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28 return 0
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30
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31 class AutoLabel:
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32 """
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33 Creates a sequence of unique labels (non-negative values).
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34 """
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35
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36 def __init__(self, reserved_labels):
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37 self.reserved_labels = reserved_labels
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38 self.next_autolabel = 0
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39
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40 def next(self):
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41 """
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42 Retrieve the next auto-label (post-increment).
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43 """
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44 # Fast-forward `next_autolabel` to the first free label
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45 while self.next_autolabel in self.reserved_labels:
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46 self.next_autolabel += 1
4
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47
5
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48 # Return the free label, then advance `next_autolabel`
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49 try:
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50 return self.next_autolabel
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51 finally:
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52 self.next_autolabel += 1
4
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53
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54
5
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55 def get_feature_label(feature: Dict) -> Optional[int]:
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56 """
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57 Get the label of a GeoJSON feature, or `None` if there is no proper label.
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58 """
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59 label = feature.get('properties', {}).get('name', None)
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60 if label is None:
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61 return None
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62
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63 # If the `label` is given as a string, try to parse as integer
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64 if isinstance(label, str):
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65 try:
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66 label = int(label)
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67 except ValueError:
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68 pass
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69
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70 # Finally, if `label` is an integer, only use it if it is non-negative
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71 if isinstance(label, int) and label >= 0:
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72 return label
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73 else:
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74 return None
4
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75
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76
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77 def rasterize(
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78 geojson: Dict,
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79 shape: Tuple[int, int],
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80 bg_value: int = 0,
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81 fg_value: Optional[int] = None,
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82 ) -> npt.NDArray:
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83 """
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84 Rasterize GeoJSON into a pixel image, that is returned as a NumPy array.
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85 """
2
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86
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87 # Determine which labels are reserved (not used by auto-label)
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88 reserved_labels = [bg_value]
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89 if fg_value is None:
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90 for feature in geojson['features']:
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91 label = get_feature_label(feature)
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92 if label is not None:
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93 reserved_labels.append(label)
2
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94
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95 # Convert `reserved_labels` into a `set` for faster look-ups
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96 reserved_labels = frozenset(reserved_labels)
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97
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98 # Define routine to retrieve the next auto-label
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99 autolabel = AutoLabel(reserved_labels)
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100
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101 # Rasterize the image
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102 img = np.full(shape, dtype=np.uint16, fill_value=bg_value)
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103 for feature in geojson['features']:
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104 geom_type = feature['geometry']['type'].lower()
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105 coords = feature['geometry']['coordinates']
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106
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107 # Rasterize a `mask` separately for each feature
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108 if geom_type == 'polygon':
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109
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110 # Normalization: Let there always be a list of polygons
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111 if get_list_depth(coords) == 2:
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112 coords = [coords]
2
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113
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114 # Rasterize each polygon separately, then join via XOR
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115 mask = np.zeros(shape, dtype=bool)
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116 for polygon_coords in coords:
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117 polygon_mask = skimage.draw.polygon2mask(
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118 shape,
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119 [point[::-1] for point in polygon_coords],
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120 )
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121 mask = np.logical_xor(mask, polygon_mask)
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122
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123 elif geom_type == 'point':
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124 mask = np.zeros(shape, dtype=bool)
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125 mask[coords[1], coords[0]] = True
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126 radius = feature.get('properties', {}).get('radius', 0)
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127 if radius > 0:
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128 mask = (ndi.distance_transform_edt(~mask) <= radius)
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129
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130 else:
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131 raise ValueError(
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132 f'Unsupported geometry type: "{feature["geometry"]["type"]}"',
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133 )
2
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134
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135 # Determine the `label` for the current `mask`
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136 if fg_value is None:
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137 label = get_feature_label(feature)
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138 if label is None:
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139 label = autolabel.next()
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140 else:
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141 label = fg_value
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142
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143 # Blend the current `mask` with the rasterized image
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144 img[mask] = label
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145
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146 # Return the rasterized image
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147 return img
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148
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149
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150 def convert_tabular_to_geojson(
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151 tabular_file: str,
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152 has_header: bool,
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153 ) -> dict:
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154 """
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155 Read a tabular file and convert it to GeoJSON.
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156
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157 The GeoJSON data is returned as a dictionary.
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158 """
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159
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160 # Read the tabular file with information from the header
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161 if has_header:
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162 df = pd.read_csv(tabular_file, delimiter='\t')
3
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163
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164 pos_x_column = giatools.pandas.find_column(df, ['pos_x', 'POS_X'])
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165 pos_y_column = giatools.pandas.find_column(df, ['pos_y', 'POS_Y'])
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166 pos_x_list = df[pos_x_column].round().astype(int)
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167 pos_y_list = df[pos_y_column].round().astype(int)
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168 assert len(pos_x_list) == len(pos_y_list)
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169
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170 try:
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171 radius_column = giatools.pandas.find_column(df, ['radius', 'RADIUS'])
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172 radius_list = df[radius_column]
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173 assert len(pos_x_list) == len(radius_list)
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174 except KeyError:
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175 radius_list = [0] * len(pos_x_list)
2
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176
5
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177 try:
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178 width_column = giatools.pandas.find_column(df, ['width', 'WIDTH'])
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179 height_column = giatools.pandas.find_column(df, ['height', 'HEIGHT'])
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180 width_list = df[width_column]
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181 height_list = df[height_column]
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182 assert len(pos_x_list) == len(width_list)
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183 assert len(pos_x_list) == len(height_list)
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184 except KeyError:
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185 width_list = [0] * len(pos_x_list)
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186 height_list = [0] * len(pos_x_list)
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187
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188 try:
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189 label_column = giatools.pandas.find_column(df, ['label', 'LABEL'])
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190 label_list = df[label_column]
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191 assert len(pos_x_list) == len(label_list)
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192 except KeyError:
2
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193 label_list = list(range(1, len(pos_x_list) + 1))
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194
5
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195 # Read the tabular file without header
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196 else:
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197 df = pd.read_csv(tabular_file, header=None, delimiter='\t')
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198 pos_x_list = df[0].round().astype(int)
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199 pos_y_list = df[1].round().astype(int)
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200 assert len(pos_x_list) == len(pos_y_list)
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201 radius_list, width_list, height_list = [[0] * len(pos_x_list)] * 3
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202 label_list = list(range(1, len(pos_x_list) + 1))
2
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203
5
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204 # Convert to GeoJSON
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205 features = []
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206 geojson = {
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207 'type': 'FeatureCollection',
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208 'features': features,
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209 }
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210 for y, x, radius, width, height, label in zip(
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211 pos_y_list, pos_x_list, radius_list, width_list, height_list, label_list,
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212 ):
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213 if radius > 0 and width > 0 and height > 0:
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214 raise ValueError('Ambiguous shape type (circle or rectangle)')
2
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215
5
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216 # Create a rectangle
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217 if width > 0 and height > 0:
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218 geom_type = 'Polygon'
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219 coords = [
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220 [x, y],
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221 [x + width - 1, y],
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222 [x + width - 1, y + height - 1],
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223 [x, y + height - 1],
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224 ]
0
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225
5
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226 # Create a point or circle
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227 else:
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228 geom_type = 'Point'
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229 coords = [x, y]
2
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230
5
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231 # Create a GeoJSON feature
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232 feature = {
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233 'type': 'Feature',
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234 'geometry': {
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235 'type': geom_type,
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236 'coordinates': coords,
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237 },
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238 'properties': {
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239 'name': label,
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240 },
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241 }
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242 if radius > 0:
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243 feature['properties']['radius'] = radius
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244 feature['properties']['subType'] = 'Circle'
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245 features.append(feature)
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246
5
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247 # Return the GeoJSON object
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248 return geojson
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249
0
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250
4
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251 if __name__ == '__main__':
0
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252 parser = argparse.ArgumentParser()
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253 parser.add_argument('in_ext', type=str, help='Input file format')
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254 parser.add_argument('in_file', type=str, help='Input file path (tabular or GeoJSON)')
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255 parser.add_argument('out_file', type=str, help='Output file path (TIFF)')
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256 parser.add_argument('shapex', type=int, help='Output image width')
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257 parser.add_argument('shapey', type=int, help='Output image height')
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258 parser.add_argument('--has_header', dest='has_header', default=False, help='Set True if tabular file has a header')
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259 parser.add_argument('--swap_xy', dest='swap_xy', default=False, help='Swap X and Y coordinates')
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260 parser.add_argument('--binary', dest='binary', default=False, help='Produce binary image')
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261 args = parser.parse_args()
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262
5
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263 # Validate command-line arguments
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264 assert args.in_ext in ('tabular', 'geojson'), (
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265 f'Unexpected input file format: {args.in_ext}'
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266 )
4
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267
5
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268 # Load the GeoJSON data (if the input file is tabular, convert to GeoJSON)
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269 if args.in_ext == 'tabular':
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270 geojson = convert_tabular_to_geojson(args.in_file, args.has_header)
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271 else:
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272 with open(args.in_file) as f:
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273 geojson = json.load(f)
4
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274
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275 # Rasterize the image from GeoJSON
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276 shape = (args.shapey, args.shapex)
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277 img = rasterize(
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278 geojson,
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279 shape if not args.swap_xy else shape[::-1],
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280 fg_value=0xffff if args.binary else None,
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281 )
5
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282 if args.swap_xy:
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283 img = img.T
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284
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285 # Write the rasterized image as TIFF
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286 with warnings.catch_warnings():
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287 warnings.simplefilter('ignore')
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288 skimage.io.imsave(args.out_file, img, plugin='tifffile') # otherwise we get problems with the .dat extension