#!/usr/bin/env python
# coding: utf-8

# # Aircraft Detection
# 
# - Get Pleiades imagery for the given airports
# - Execute tiling and aircraft detection blocks via parallel jobs
# - Visualize the results
# 
# - The example costs around 1700 UP42 credits

# ## Setup
# 
# Import required libraries

# In[2]:


import up42
import geopandas as gpd
import rasterio
from rasterio.plot import show
import matplotlib.pyplot as plt
from shapely.geometry import box


# Configure areas of interest

# In[3]:


aoi_txl = {"type":"FeatureCollection","features":[{"type":"Feature","properties":{},
                "geometry":{"type":"Polygon","coordinates":[[[13.286740779876709,52.5509016976356],
                                                             [13.300495147705078,52.5509016976356],
                                                             [13.300495147705078,52.556890079685594],
                                                             [13.286740779876709,52.556890079685594],
                                                             [13.286740779876709,52.5509016976356]]]}}]}
aoi_muc = {"type":"FeatureCollection","features":[{"type":"Feature","properties":{},
                "geometry":{"type":"Polygon","coordinates":[[[11.789016723632812,48.348577346994944],
                                                             [11.809401512145996,48.348577346994944],
                                                             [11.809401512145996,48.360155725059116],
                                                             [11.789016723632812,48.360155725059116],
                                                             [11.789016723632812,48.348577346994944]]]}}]}


aois = [{'title': 'TXL', 'geometry': aoi_txl},
        {'title': 'MUC', 'geometry': aoi_muc}]


# Authenticate with UP42

# In[4]:


#up42.authenticate(project_id="123", project_api_key="456")
up42.authenticate(cfg_file="config.json")
up42.settings(log=False)


# ## Catalog Search
# 
# Search cloudfree Pleiades image for the two aois and visualise the quicklooks.

# In[5]:


catalog = up42.initialize_catalog()


for aoi in aois:
    print("\n---------" + aoi["title"] + "---------\n")
    search_paramaters = catalog.construct_parameters(geometry=aoi['geometry'], 
                                                     start_date="2020-04-01",
                                                     end_date="2020-04-30",
                                                     sensors=["pleiades"],
                                                     max_cloudcover=10,
                                                     sortby="acquisitionDate",
                                                     ascending=False,
                                                     limit=3)
    search_results = catalog.search(search_paramaters)
    
    # Download & Visualise quicklooks
    catalog.download_quicklooks(image_ids=search_results.id.to_list(), sensor="pleiades")
    display(search_results.head())
    catalog.plot_quicklooks(figsize=(18,5), titles=search_results.scene_id.to_list())

    # Select least cloud scene for further workflow
    aoi["scene_id"] = search_results.scene_id.to_list()[0]


# In[10]:


# Optional: Select ideal scenes manually
aois[0]["scene_id"] = "DS_PHR1B_202004281031350_FR1_PX_E013N52_0513_01239"
aois[1]["scene_id"] = "DS_PHR1B_202004161025425_FR1_PX_E011N48_1009_00822"


# ## Download selected Pleiades images for aois

# In[14]:


up42.settings(log=True)
project = up42.initialize_project()

# Increase the parallel job limit for the project.
#project.update_project_settings(max_concurrent_jobs=10)


# Create or update a workflow for the aircraft detection

# In[15]:


workflow = project.create_workflow("Aircraft detection", use_existing=True)


# Add or update workflows tasks

# In[23]:


#up42.get_blocks(basic=True)
input_tasks= ['oneatlas-pleiades-aoiclipped', 'tiling', 'orbital_pleiades_aircraft']
workflow.add_workflow_tasks(input_tasks=input_tasks)
workflow


# ### Run jobs in parallel
# 
# Construct workflow input parameters & run jobs

# In[24]:


input_parameters_list = []

for aoi in aois:
    input_parameters = workflow.construct_parameters(geometry=aoi['geometry'], 
                                                     geometry_operation="bbox", 
                                                     scene_ids=[aoi["scene_id"]])
    input_parameters['tiling:1']['tile_width']  = 1024
    input_parameters['tiling:1']['tile_height'] = 1024
    input_parameters_list.append(input_parameters)

input_parameters_list


# In[25]:


jobs = workflow.run_jobs_parallel(input_parameters_list=input_parameters_list)


# ### Download & Visualise results

# In[43]:


data_results_paths, detection_results = [], []
for job in jobs:
    data_task, _, detection_task = job.get_jobtasks()
    data_paths = data_task.download_results()
    data_results_paths.append([p for p in data_paths if p.endswith(".tif")])
    detection_results.append(detection_task.get_results_json())


# In[48]:


for i, (paths, detection) in enumerate(zip(data_results_paths, detection_results)):
    with rasterio.open(paths[0]) as src:
        fig, ax = plt.subplots(figsize=(18, 18))

        planes = gpd.GeoDataFrame.from_features(detection, crs={'init': "EPSG:4326"})
        planes = planes.to_crs(epsg=3857)
        planes.geometry = planes.geometry.buffer(0.0001)
        planes.geometry = planes.geometry.apply(lambda geo:box(*geo.bounds))

        show(src.read(), transform=src.transform, ax=ax, title=f"{aois[i]['title']}: {planes.shape[0]} planes detected")
    
        planes.plot(ax=ax, facecolor=(0,0,0,0), edgecolor='red', linewidth=2)
        
        plt.show()


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