Pre-prcessing scripts

Mpreprocessing/inputs/remove_noise_withPywrenIBMcloud.py

+#!/usr/bin/env python
+# coding: utf-8
+"""
+Author: Ammar Okran
+Date  : 03/10/2019
+Email : ammar.okran@gmail.com
+
+Description:
+Classify the outlier points as noise, so it is easier to ignore them in subsequent processes.
+Execute it in the IBM cloud Function.
+You have to substitute "XXXXXX" with your own details.
+
+The documentation of the pdal libery can be found in the following
+address: https://pdal.io/index.html
+"""
+
+import pywren_ibm_cloud as pywren 
+from pywren_ibm_cloud.utils import get_current_memory_usage
+import pdal
+import pandas as pd
+import json
+import os
+import ibm_boto3
+import ibm_botocore 
+from ibm_botocore.client import Config
+import time
+
+
+
+# Constants for IBM COS values
+cos_config = { 'bucket_name' : 'XXXXXXX',
+              'api_key' : 'XXXXXXX',
+              'service_endpoint' : 'XXXXXXXX'
+             }
+
+
+
+api_key = cos_config.get('api_key')
+print(api_key)
+auth_endpoint = cos_config.get('auth_endpoint')
+service_endpoint = cos_config.get('service_endpoint')
+print(service_endpoint)
+cos_client = ibm_boto3.client('s3',
+                              ibm_api_key_id=api_key,
+                              ibm_auth_endpoint=auth_endpoint,
+                              config=Config(signature_version='oauth'),
+                              endpoint_url=service_endpoint)
+
+
+
+def remove_noise(obj, ibm_cos):
+    
+    print("Remove noise is starting ...")
+    key = obj.key
+    print('I am processing the object {}'.format(key))
+    print("The utilized memory befor doing anything is: {}".format(get_current_memory_usage()))
+    workdir = "/tmp" 
+    bucket_name = 'XXXXXX'
+    path_folder = workdir + "/lidar_files/"
+    res_folder = workdir + "/res_files/"
+    
+    #-----------------------------------------------------------------------------------
+
+    # Create folders for files
+    
+    if not os.path.exists(path_folder):
+        try:
+            os.mkdir(path_folder)
+            print("Directory " , path_folder ,  " Created ")
+        except OSError as e:
+            if e.errno != errno.EEXIST:
+                raise
+        
+        
+    if not os.path.exists(res_folder):
+        try:
+            os.mkdir(res_folder)
+            print("Directory " , res_folder ,  " Created ")
+        except OSError as e:
+            if e.errno != errno.EEXIST:
+                raise
+    
+    try:
+        files = os.listdir(path_folder)
+        if len(files) > 0:
+            for file in files:
+                if '.laz' in file or '.LAZ' in file:
+                    file_path = os.path.join(path_folder, file)
+                    print(file_path)
+                    os.unlink(file_path)
+                    print("Folder has cleaned")
+        else:
+            print("Folder is empty")
+    except OSError as e:
+        if e.errno != errno.EEXIST:
+            raise
+            
+    
+    try:
+        files = os.listdir(res_folder)
+        if len(files) > 0:
+            for file in files:
+                if '.laz' in file or '.LAZ' in file:
+                    file_path = os.path.join(res_folder, file)
+                    print(file_path)
+                    os.unlink(file_path)
+                    print("Folder has cleaned")
+        else:
+            print("Folder is empty")
+    except OSError as e:
+        if e.errno != errno.EEXIST:
+            raise
+    # -----------------------------------------------------------------------------------      
+    
+    # Read data stream of object
+    print("#------------ Starting download data -----------#")
+    st_download_time = time.time()
+    data = obj.data_stream.read()    
+    print("The utilized memory after reading data stream is: {}".format(get_current_memory_usage()))
+     
+
+    # Save data into file in order to free the memory
+    s_file = path_folder + key
+    print(s_file)
+    with open(s_file, 'wb') as fname:
+        fname.write(data)
+        print(fname.name)
+    
+    elap_download_time = time.time() - st_download_time
+    print("Downloading data has taken {} seconds".format(elap_download_time))
+    print("The utilized memory after downloaded the file is: {}".format(get_current_memory_usage()))
+         
+    # Delete the variable data to free spaces in the memory
+    del data
+    print("The utilized memory after deleted the data variable is: {}".format(get_current_memory_usage()))
+    
+    # ----------------------------------------------------------------------------------- 
+    
+    # Applying some filter for each file
+  
+    out_file = res_folder + fname.name.split("/")[3].split(".")[0] + '_denoise.laz'
+    print(out_file)
+    file_name = {"type":"readers.las", "filename": fname.name}
+    cr_json = {
+        "pipeline": [
+            file_name, 
+            {
+                # Creates a window to find outliers. If they are found they are classified as noise (7).
+                "type": "filters.outlier",
+                "method": "statistical",
+                "multiplier": 3,
+                "mean_k": 8
+            },
+            {
+                "type": "writers.las",
+                "compression": "laszip",
+                "filename": out_file
+            }
+        ]
+    }
+    
+    pipeline = pdal.Pipeline(json.dumps(cr_json, indent=4))
+    pipeline.validate()   # Check if json options are good
+    pipeline.loglevel = 8
+    count = pipeline.execute()
+    print(count)
+    
+    print("The utilized memory after executing the pipeline is: {}".format(get_current_memory_usage()))
+    del pipeline
+    print("The utilized memory after deleting the pipeline variable is: {}".format(get_current_memory_usage()))
+    
+    
+    #Upload files into COS
+    
+    files = []
+    for r, d, f in os.walk(res_folder): # r=root, d=directories, f = files
+        for file in f:
+            print(file)
+            try:
+                print(key)
+                ibm_cos.delete_object(Bucket=bucket_name, Key=key)
+            except:
+                print('File is not exist in the bucket {}'.format(bucket_name))
+                              
+            # Upload the file
+            ibm_cos.upload_file(Filename = res_folder + file, Bucket = bucket_name, Key = file)
+    
+    return count
+    
+
+
+
+if __name__ == "__main__":
+    # Name of the bucket
+    bucket_name = 'XXXXX'
+    
+    # Define variable
+    func = []
+
+    # Get the contents of bucket
+    list_obj = cos_client.list_objects(Bucket=bucket_name)['Contents']
+    obj_key = []
+    for obj in list_obj:
+        obj_key.append(bucket_name + '/' + obj['Key'])
+    print(len(obj_key))
+
+    # Run pywren 
+    pw = pywren.ibm_cf_executor(runtime='ammarokran/pywren-pdal:1.0.2',runtime_memory=1024, log_level='DEBUG')
+    exec_time = {}
+    iterdata = obj_key
+    print(iterdata)
+    st_process_time = time.time()
+    pw.map(remove_noise, bucket_name, chunk_size=None)
+    result = pw.get_result()
+    print(result)
+
+
+
+