dict 10m, 20m, 60m et metadata

Dockerfile

+FROM kosted/bmap-stack-python:1.0
+
+RUN pip install property
+ 
+WORKDIR /projects/{THE-NAME-OF-YOUR-PROJECT}
+ADD . /projects/{THE-NAME-OF-YOUR-PROJECT}
+
+ENTRYPOINT  ["python3", "/projects/{THE-NAME-OF-YOUR-PROJECT}/src/sampleExample.py"] 

README

+You have created a blank project.
+This is really a blank project! This is the only file.
+
+You can add files to your project:
+1. Right click on the project tree.
+2. In the Project menu.
+3. Create files on the command line. Click the "+" button in the Console panel to open a terminal window.
+
+You can perform install software, use the included version control tools, or run scaffolders like `rails` (if your workspace has it installed).
+You can change the project type in `Project > Configuration`.
\ No newline at end of file

conf/configuration.properties

+# Inputs
+s2_folder = /projects/read_s2/input/MTD_MSIL2A.xml
+
+
+# Outputs
+output = /projects/data/read_s2/
+bands_10m = Bands_10m.pkl
+bands_20m = Bands_20m.pkl
+bands_60m = Bands_60m.pkl
+tile_meta = Tile_metadata.pkl
+
+

conf/datacriteria.properties

+# Names of ground campaigns (e.g. biosar, tropisar, ...).
+collection_Names=afrisar_onera
+
+# Names of sub-regions (e.g. paracou, ...).
+sub_region_names=La Lope
+
+# Scene name
+scene_name=lopeTB5
+
+# Start date / end date to specify a range for the data acquisition date (e.g. 2007-12-03).
+start_date=
+
+# Start date / end date to specify a range for the data acquisition date (e.g. 2007-12-03).
+end_date=
+
+# Data product types (e.g. SLC, DEM, ...).
+product_types=
+
+# Instrument names used to capture the data (e.g. P-band, ...).
+instrument_names=
+
+# Data polarizations (e.g. HH, HV, ...).
+polarizations=
+
+# Data geometry types (e.g. non-geolocated ...).
+geometry_types=
+
+# Data processing levels (e.g. L0, L1, ...).
+processing_levels=

conf/sharedata.properties

+#MANDATORY FIELDS
+
+# User id (Find this number in the ESA MAAP portal, in the left panel > My account > Account Setings). mandatory
+user_id=
+
+# The path of the data (e.g. /projects/myProject/output/output_data_example.tiff). mandatory
+data_path=
+
+# The format of the data (e.g. AZ, RG, DEM,ROI, INC, KZ, GRD). mandatory
+# Complex Data are not supported yet
+product_type=
+
+
+
+#OPTIONAL FIELDS
+
+# Name of sub-region (e.g. La Lope, ...). optional
+sub_region_name=
+
+# Raster Data polarization (e.g. HH, HV, VV, VH). optional
+polarization=
\ No newline at end of file

doc/documentation.md

+## Short description of the algorithm
+
+
+
+## Description of data used
+*  Description of inputs
+
+
+*  Description of outputs
+
+
+
+## Description of the different functions
+*  Function 1
+    *  Short description
+    *  Description of inputs
+    *  Description of outputs
+
+
+*  Function 2
+    *  Short description
+    *  Description of inputs
+    *  Description of outputs
+
+
+
+
+

doc/syntaxe.md

+This is a template of documentation. Please change each section by correct information
+Headings
+
+# This is an H1
+## This is an H2
+###### This is an H6
+
+This is also an H1
+==================
+
+This is also an H2
+------------------
+
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+
+Ordered list
+
+1.  Step 1
+2.  Step 2
+3.  Step 3
+    1.  Step 3.1
+    2.  Step 3.2
+    3.  Step 3.3
+
+List in list
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+2.  Step 2
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+
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+
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+    saveCoverage();
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+        return oldUnload.apply(this, arguments);
+    }
+};
+```
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+
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+
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+
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+
+Inline image syntax looks like this:
+
+![Alt text](/path/to/image.jpg)
+![Alt text](/path/to/image.png "Optional title attribute")
+![Alt text](/url/to/image.jpg)
+
+For example:
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+...
+
+Reference image links look like this:
+
+![Alt text][id]
+
+where 'id' is the name of a previously defined image reference, using syntax similar to link references:
+
+[id]: url/to/image.jpg "Optional title attribute"
+
+For example:
+
+...
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+

src/read_s2.py

+##########################################################################                
+            
+if __name__ == '__main__':
+    
+##########################################################################        
+    import os, sys
+    import numpy as np
+    os.sys.path.append('/projects/read_s2/src')
+    import re
+    
+    from properties.p import Property
+    from xml.dom import minidom 
+    import xml.etree.ElementTree as ET
+
+    
+    if os.path.isfile('/projects/read_s2/conf/configuration.properties'):
+        configfile='/projects/read_s2/conf/configuration.properties'
+        prop=Property()
+        prop_dict=prop.load_property_files(configfile)
+        print("The configuration file is loaded")
+
+        output_path = prop_dict['output']
+        if not os.path.exists(output_path):
+            os.makedirs(output_path)
+
+
+
+        xml_metadata_path = prop_dict["s2_folder"]
+        metadata_tree = minidom.parse(xml_metadata_path)
+        root = metadata_tree.documentElement
+
+        dict_10m = {}
+        dict_20m = {}
+        dict_60m = {}
+        for element in root.getElementsByTagName('IMAGE_FILE'):
+            data_path = element.firstChild.data
+            if data_path.find('B02_10m') > 0:
+                dict_10m["B02"] = data_path + '.jp2'
+            if data_path.find('B03_10m') > 0:
+                dict_10m["B03"] = data_path + '.jp2'
+            if data_path.find('B04_10m') > 0:
+                dict_10m["B04"] = data_path + '.jp2'
+            if data_path.find('B05_20m') > 0:
+                dict_20m["B05"] = data_path + '.jp2'
+            if data_path.find('B06_20m') > 0:
+                dict_20m["B06"] = data_path + '.jp2'
+            if data_path.find('B07_20m') > 0:
+                dict_20m["B07"] = data_path + '.jp2'
+            if data_path.find('B08_10m') > 0:
+                dict_10m["B08"] = data_path + '.jp2'
+            if data_path.find('B08A_20m') > 0:
+                dict_20m["B08A"] = data_path + '.jp2'
+            if data_path.find('B09_60m') > 0:
+                dict_60m["B09"] = data_path + '.jp2'
+            if data_path.find('B11_20m') > 0:
+                dict_20m["B11"] = data_path + '.jp2'
+            if data_path.find('B12_20m') > 0:
+                dict_20m["B12"] = data_path + '.jp2'
+        
+        import pickle
+        with open(output_path + os.sep + prop_dict['bands_10m'], 'wb') as save_10m :
+            pickle.dump(dict_10m, save_10m)
+            print('Bands_10m.pkl : Done')
+        with open(output_path + os.sep + prop_dict['bands_20m'], 'wb') as save_20m :
+            pickle.dump(dict_20m, save_20m)
+            print('Bands_20m.pkl : Done')
+        with open(output_path + os.sep + prop_dict['bands_60m'], 'wb') as save_60m :
+            pickle.dump(dict_60m, save_60m)  
+            print('Bands_60m.pkl : Done')
+
+
+        dict_metadata = {}
+        dict_metadata['PROCESSING_LEVEL'] = root.getElementsByTagName('PROCESSING_LEVEL')[0].firstChild.nodeValue
+        dict_metadata['PRODUCT_TYPE'] = root.getElementsByTagName('PRODUCT_TYPE')[0].firstChild.data
+        dict_metadata['GENERATION_TIME'] = root.getElementsByTagName('GENERATION_TIME')[0].firstChild.data
+        dict_metadata['NODATA_PIXEL_PERCENTAGE'] = root.getElementsByTagName('NODATA_PIXEL_PERCENTAGE')[0].firstChild.nodeValue
+        dict_metadata['SATURATED_DEFECTIVE_PIXEL_PERCENTAGE'] = root.getElementsByTagName('SATURATED_DEFECTIVE_PIXEL_PERCENTAGE')[0].firstChild.data
+        dict_metadata['DARK_FEATURES_PERCENTAGE'] = root.getElementsByTagName('DARK_FEATURES_PERCENTAGE')[0].firstChild.data
+        dict_metadata['CLOUD_SHADOW_PERCENTAGE'] = root.getElementsByTagName('CLOUD_SHADOW_PERCENTAGE')[0].firstChild.nodeValue
+        dict_metadata['VEGETATION_PERCENTAGE'] = root.getElementsByTagName('VEGETATION_PERCENTAGE')[0].firstChild.data
+        dict_metadata['NOT_VEGETATED_PERCENTAGE'] = root.getElementsByTagName('NOT_VEGETATED_PERCENTAGE')[0].firstChild.data
+        dict_metadata['WATER_PERCENTAGE'] = root.getElementsByTagName('WATER_PERCENTAGE')[0].firstChild.nodeValue
+        dict_metadata['UNCLASSIFIED_PERCENTAGE'] = root.getElementsByTagName('UNCLASSIFIED_PERCENTAGE')[0].firstChild.data
+        dict_metadata['MEDIUM_PROBA_CLOUDS_PERCENTAGE'] = root.getElementsByTagName('MEDIUM_PROBA_CLOUDS_PERCENTAGE')[0].firstChild.data
+        dict_metadata['HIGH_PROBA_CLOUDS_PERCENTAGE'] = root.getElementsByTagName('HIGH_PROBA_CLOUDS_PERCENTAGE')[0].firstChild.nodeValue
+        dict_metadata['THIN_CIRRUS_PERCENTAGE'] = root.getElementsByTagName('THIN_CIRRUS_PERCENTAGE')[0].firstChild.data
+        dict_metadata['SNOW_ICE_PERCENTAGE'] = root.getElementsByTagName('SNOW_ICE_PERCENTAGE')[0].firstChild.data
+
+        PRODUCT_URI = root.getElementsByTagName('PRODUCT_URI')[0].firstChild.data
+        dict_metadata['TILE_DATE'] = PRODUCT_URI[11:19]
+        dict_metadata['TILE_NAME'] = PRODUCT_URI[38:44]
+
+
+        with open(output_path + os.sep + prop_dict['tile_meta'], 'wb') as meta :
+            pickle.dump(dict_metadata, meta)
+            print('Tile_metadata.pkl : Done')