Tellus ASNARO-1

Tellus

https://www.tellusxdp.com/ja/

Result

Original data provided by NEC
Original data provided by NEC

Code

import os, json, requests, math
from skimage import io
from io import BytesIO
import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')
TOKEN = "Input your token"

def get_ASNARO_scene(min_lat, min_lon, max_lat, max_lon):
	url = "https://gisapi.tellusxdp.com/api/v1/asnaro1/scene"     	+ "?min_lat={}&min_lon={}&max_lat={}&max_lon={}".format(min_lat, min_lon, max_lat, max_lon)
	headers = {
    	"content-type": "application/json",
    	"Authorization": "Bearer " + TOKEN
	}
	r = requests.get(url, headers=headers)
	return r.json()

scenes = 0
# Orig
# scenes = get_ASNARO_scene(20.425278, 122.933611, 45.557222, 153.986389)
# Fuji
scenes = get_ASNARO_scene(35.365, 138.705, 35.36, 138.71)
# Tokyo Tower
# scenes = get_ASNARO_scene(35.6505805,139.7402442, 35.6585805,139.74324421)
# Sky Tree 35.710067,139.8085064
# scenes = get_ASNARO_scene(35.7100627,139.8085117, 35.7200627,139.8085117)
# Tokyo Station 35.6812405,139.7649308
# scenes = get_ASNARO_scene(35.6812405,139.7649308, 35.6812405,139.7659308)
# home 35.6034393,139.320697
# scenes = get_ASNARO_scene(35.6034393,139.315697, 35.7234393,139.39697)

print(len(scenes))
print(scenes[0]['thumbs_url'])
      
ext_scene = scenes[0]
img_thumbs = io.imread(ext_scene['thumbs_url'])
print(len(img_thumbs))
io.imshow(img_thumbs)
def get_ASNARO_scene(min_lat, min_lon, max_lat, max_lon):
	url = "https://gisapi.tellusxdp.com/api/v1/asnaro1/scene"     	+ "?min_lat={}&min_lon={}&max_lat={}&max_lon={}".format(min_lat, min_lon, max_lat, max_lon)
	headers = {
    	"content-type": "application/json",
    	"Authorization": "Bearer " + TOKEN
	}
	r = requests.get(url, headers=headers)
	return r.json()

def get_tile_num(lat_deg, lon_deg, zoom):
    # https://wiki.openstreetmap.org/wiki/Slippy_map_tilenames#Python
    lat_rad = math.radians(lat_deg)
    n = 2.0 ** zoom
    xtile = int((lon_deg + 180.0) / 360.0 * n)
    ytile = int((1.0 - math.log(math.tan(lat_rad) + (1 / math.cos(lat_rad))) / math.pi) / 2.0 * n)
    return (xtile, ytile)

def get_ASNARO_image(scene_id, zoom, xtile, ytile):
    url = " https://gisapi.tellusxdp.com/ASNARO-1/{}/{}/{}/{}.png".format(scene_id, zoom, xtile, ytile)
    headers = {
        "Authorization": "Bearer " + TOKEN
    }
    r = requests.get(url, headers=headers)
    return io.imread(BytesIO(r.content))

def get_NxN_series_image(scene_id, zoom, topleft_x, topleft_y, size_x=1, size_y=1):
    """切り出したタイル画像を結合する"""
    img = []
    for y in range(size_y):
        row = []
        for x in range(size_x):
            row.append(get_ASNARO_image(scene_id, zoom, topleft_x + x, topleft_y + y))
        img.append(np.hstack(row))
    return  np.vstack(img)

def get_4x4(lat, log, zoom):
    xtile, ytile = get_tile_num(lat, log, zoom)
    img_4x4 = get_NxN_series_image(scenes[0]["entityId"], zoom, xtile, ytile, 4, 4)
    return img_4x4 

# 東京タワー 35.6585805,139.7432442
# xtile = 232830
# ytile = 103246
zoom=18
lat = 35.6585805  
log = 139.7432442 

scenes = get_ASNARO_scene(lat, log, lat + 0.1, log + 0.1)

(xtile, ytile) = get_tile_num(scenes[0]['clat'], scenes[0]['clon'], zoom)

print(zoom)
print(xtile, ytile)

# img = get_ASNARO_image(scenes[0]['entityId'], zoom, xtile, ytile)
# io.imshow(img)

img_4x4 = get_4x4(lat, log, zoom)

# io.imshow(img_4x4)

import cv2

def gamma(_img):
    # テーブルを作成する。
    table = np.clip(np.arange(256)*2.5,0, 255)
    # [0, 255] でクリップし、uint8 型にする。
    table = np.clip(table, 0, 255).astype(np.uint8)
    return cv2.LUT(_img, table)


from PIL import Image
dst = gamma(img_4x4)

pil_img = Image.fromarray(dst)

pil_img.save('tt.png')

from IPython.display import Image
Image(url="tt.png")

Tellus SLATS

Tellus

https://www.tellusxdp.com/ja/

Result

Original data provided by JAXA
Original data provided by JAXA

Code

import os, json, requests, math
import numpy as np
import dateutil.parser
from datetime import datetime
from datetime import timezone
from skimage import io
from io import BytesIO
import matplotlib.pyplot as plt

TOKEN = "Input your token"

def get_tsubame_scene(min_lat, min_lon, max_lat, max_lon):
    url = "https://gisapi.tellusxdp.com/api/v1/tsubame/scene" \
        + "?min_lat={}&min_lon={}&max_lat={}&max_lon={}".format(min_lat, min_lon, max_lat, max_lon)
    headers = {
        "Authorization": "Bearer " + TOKEN
    }
    r = requests.get(url, headers=headers)
    return r.json()

tsubame_scenes = get_tsubame_scene(35.524375, 139.585347, 35.724375, 140.005347)

print(len(tsubame_scenes))
print(tsubame_scenes[0])

ext_scene = tsubame_scenes[0]

# 表示
io.imshow(io.imread(tsubame_scenes[0]["thumbs_url"]))
from PIL import Image

def get_tile_num(lat_deg, lon_deg, zoom):
    # https://wiki.openstreetmap.org/wiki/Slippy_map_tilenames#Python
    lat_rad = math.radians(lat_deg)
    n = 2.0 ** zoom
    xtile = int((lon_deg + 180.0) / 360.0 * n)
    ytile = int((1.0 - math.log(math.tan(lat_rad) + (1 / math.cos(lat_rad))) / math.pi) / 2.0 * n)
    return (xtile, ytile)

def get_tsubame_image(scene_id, zoom, xtile, ytile):
    url = " https://gisapi.tellusxdp.com/tsubame/{}/{}/{}/{}.png".format(scene_id, zoom, xtile, ytile)
    headers = {
        "Authorization": "Bearer " + TOKEN
    }
    r = requests.get(url, headers=headers)
    return io.imread(BytesIO(r.content))

def get_slat_series_image(scene_id, zoom, topleft_x, topleft_y, size_x=1, size_y=1):
    """切り出したタイル画像を結合する"""
    img = []
    for y in range(size_y):
        row = []
        for x in range(size_x):
            row.append(get_tsubame_image(scene_id, zoom, topleft_x + x, topleft_y + y))
        img.append(np.hstack(row))
    return  np.vstack(img)

def main():
    # 新国立競技場
    zoom=18
    lat, log = 35.6796057, 139.711851
    xtile, ytile = get_tile_num(lat, log, zoom)
    
    tsubame_image = get_tsubame_image(tsubame_scenes[0]["entityId"], zoom, xtile, ytile)

    img_4x4 = get_slat_series_image(tsubame_scenes[0]["entityId"], zoom, xtile, ytile, 4, 4)
    io.imshow(img_4x4)

if __name__ == '__main__':
    main()

秩父雲海発生予想エリア by ASTER GDEM 2

目的

秩父の雲海は標高の低いところに滞留すると仮定し、ASTER GDEM2 の標高モデルより、秩父雲海の発生エリアを推定する。

Tellus

https://www.tellusxdp.com/ja/

Result

秩父市標高データ ASTER GDEM提供:METI and NASA
秩父市 部分的雲海の発生予想エリア
ASTER GDEM提供:METI and NASA
秩父市 全面的雲海の発生予想エリア
ASTER GDEM提供:METI and NASA

Code

import requests, json, math
from skimage import io
from io import BytesIO
import numpy as np

TOKEN = 'Enter your code'

# 秩父市
# 35.9827619,138.8036913
# 秩父市 ミューズパーク
zoom = 12
lat, lon = 35.9951626,139.0531834
y_slice = 160

def get_tile_num(lat_deg, lon_deg, zoom):
    # https://wiki.openstreetmap.org/wiki/Slippy_map_tilenames#Python
    lat_rad = math.radians(lat_deg)
    n = 2.0 ** zoom
    xtile = int((lon_deg + 180.0) / 360.0 * n)
    ytile = int((1.0 - math.log(math.tan(lat_rad) + (1 / math.cos(lat_rad))) / math.pi) / 2.0 * n)
    print("Zoom =", zoom, ", xtile =", xtile, ", ytile =", ytile)
    return (xtile, ytile)

def get_astergdem2_dsm(zoom, xtile, ytile):
    """
    """
    url = " https://gisapi.tellusxdp.com/astergdem2/dsm/{}/{}/{}.png".format(zoom, xtile, ytile)
    headers = {
        "Authorization": "Bearer " + TOKEN
    }
    r = requests.get(url, headers=headers)
    return io.imread(BytesIO(r.content))

def get_height_data(astergdem2_dsm):
    R = 255 * 255 * (astergdem2_dsm[:,:,0].astype(np.uint32))
    G = 255 * (astergdem2_dsm[:,:,1].astype(np.uint32))    
    B = astergdem2_dsm[:,:,2].astype(np.uint32)
    height =  R + G + B
    return height.astype(np.uint32)

def get_max_height(imgin):
    max_height = np.max(imgin)
    print("標高:", max_height)
    return max_height

def get_height_img(imgin, sea_level):
    gray =  imgin * 255.0 / get_max_height(imgin)
    _img = np.zeros([256,256,3],np.uint8)
    _img[:,:,0] = gray[:,:]
    _img[:,:,1] = gray[:,:]
    _img[:,:,2] = gray[:,:]

    # 標高 0m をブルーで塗りつぶす
    _img[:,:,2][_img[:,:,2] <= sea_level] = 255
    
    return _img.astype(np.uint8)

def get_height(zoom, lat,lon):
    (xtile, ytile) = get_tile_num(lat,lon, zoom)

    _astergdem2_dsm = get_astergdem2_dsm(zoom, xtile, ytile)

    _height = get_height_data(_img)
    
    return _height_data


def show_slice(height, y_slice):
    '''
    断面図を表示
    '''
    import matplotlib.pyplot as plt

    plt.figure(figsize=(10, 2.0))

    x = range(0,255)
    # 断面の Y座標

    plt.plot(x, height[y_slice, x].astype(np.float), label='mix', color='red')

    plt.grid(True)

    plt.show()
    
    
def main(zoom, lat, lon, y_slice, sea_level):
    (xtile, ytile) = get_tile_num(lat,lon, zoom)
    astergdem2_dsm = get_astergdem2_dsm(zoom, xtile, ytile)
    height_data = get_height_data(astergdem2_dsm)
    height_img = get_height_img(height_data, sea_level)
    io.imshow(height_img)
    show_slice(height_data, y_slice)

if __name__ == '__main__':
    main(zoom, lat, lon, y_slice, sea_level)

# 秩父市
# 35.9827619,138.8036913
# 秩父市 ミューズパーク
zoom = 12
lat, lon = 35.9951626,139.0531834
sea_level = 80

main(zoom, lat, lon, y_slice, sea_level)

ASTER GDEM 2

Tellus

https://www.tellusxdp.com/ja/

Result

ASTER GDEM提供:METI and NASA
ASTER GDEM提供:METI and NASA
ASTER GDEM提供:METI and NASA

Code

import requests, json, math
from skimage import io
from io import BytesIO
import numpy as np

TOKEN = 'Enter your token'

# 阿蘇山 32.8985056,131.0787207
# zoom = 9
# lat,lon = 32.8985056,131.0787207
# y_slice = 110

# 霧島山 31.9341693,130.8527772
# zoom = 9
# lat,lon = 31.9341693,130.8527772
# y_slice = 10

# 桜島 31.5833323,130.6412453
# zoom = 9
# lat,lon = 31.5833323,130.6412453
# y_slice = 155

# # イエローストーン 44.4122632,-110.7319387
# zoom = 8
# lat,lon = 44.4122632,-110.7319387
# y_slice = 110

# 富士山 35.3586732,138.719563
zoom = 9
lat,lon = 35.3586732,138.719563
y_slice = 44

def get_tile_num(lat_deg, lon_deg, zoom):
    # https://wiki.openstreetmap.org/wiki/Slippy_map_tilenames#Python
    lat_rad = math.radians(lat_deg)
    n = 2.0 ** zoom
    xtile = int((lon_deg + 180.0) / 360.0 * n)
    ytile = int((1.0 - math.log(math.tan(lat_rad) + (1 / math.cos(lat_rad))) / math.pi) / 2.0 * n)
    print("Zoom =", zoom, ", xtile =", xtile, ", ytile =", ytile)
    return (xtile, ytile)

def get_astergdem2_dsm(zoom, xtile, ytile):
    """
    """
    url = " https://gisapi.tellusxdp.com/astergdem2/dsm/{}/{}/{}.png".format(zoom, xtile, ytile)
    headers = {
        "Authorization": "Bearer " + TOKEN
    }
    r = requests.get(url, headers=headers)
    return io.imread(BytesIO(r.content))

def get_height_data(astergdem2_dsm):
    R = 255 * 255 * (astergdem2_dsm[:,:,0].astype(np.uint32))
    G = 255 * (astergdem2_dsm[:,:,1].astype(np.uint32))
    B = (astergdem2_dsm[:,:,2].astype(np.uint32))
    height =  R + G + B
    return height.astype(np.uint32)

def get_max_height(imgin):
    max_height = np.max(imgin)
    print("標高:", max_height)
    return max_height

def get_height_img(imgin):
    gray =  imgin * 255.0 / get_max_height(imgin)
    _img = np.zeros([256,256,3],np.uint8)
    _img[:,:,0] = gray[:,:]
    _img[:,:,1] = gray[:,:]
    _img[:,:,2] = gray[:,:]

    # 標高 0m をブルーで塗りつぶす
    _img[:,:,2][_img[:,:,2] == 0] = 255
    
    return _img.astype(np.uint8)

def get_height(zoom, lat,lon):
    (xtile, ytile) = get_tile_num(lat,lon, zoom)

    _astergdem2_dsm = get_astergdem2_dsm(zoom, xtile, ytile)

    _height = get_height_data(_img)
    
    return _height_data


def show_slice(height, y_slice):
    '''
    断面図を表示
    '''
    import matplotlib.pyplot as plt

    plt.figure(figsize=(10, 2.0))

    x = range(0,255)
    # 断面の Y座標

    plt.plot(x, height[y_slice, x].astype(np.float), label='mix', color='red')

    plt.grid(True)

    plt.show()
    
if __name__ == '__main__':
    main(zoom, lat, lon, y_slice)
    
def main(zoom, lat, lon, y_slice):
    (xtile, ytile) = get_tile_num(lat,lon, zoom)
    astergdem2_dsm = get_astergdem2_dsm(zoom, xtile, ytile)
    height_data = get_height_data(astergdem2_dsm)
    height_img = get_height_img(height_data)
    io.imshow(height_img)
    show_slice(height_data, y_slice)

Xcode compile error

Symptom

After adding DatePicker, set properties and compile, and then the following error message was reported.

/Users/*****/Main.storyboard:1:1: Internal error. Please file a bug at feedbackassistant.apple.com and attach “/var/folders/gm/t2l4p33d1r9gpqzscb8kdggw0000gn/T/IB-agent-diagnostics_2020-12-13_14-22-11_398000”.

Cause

Unknown.

In my case, I added DatePicker with count down timer for macCatalyst. And added a few lines of code to change properties. Then, suddenly the error came up. After removing the DatePicker control and then it was solved. I guess it would be a Xcode bug.

Solution

Clean build and rebuild did not work.

Close and reopen the project did not work.

Restart Xcode did not work.

Removed the DatePicker control and rebuild, then it worked.

framework not found after pod install

Symptom

  1. Create a new Swift project on Xcode.
  2. Close Xcode
  3. Run pod init.
  4. Edit the Podfile and add a framework.
  5. Run pod install.
  6. Restart Xcode from ***.xcodeproj
  7. Run build and then “ld: framework not found framework-name” will come up.

Solution

Restart Xcode from ***.xcworkspace, not ***.xcodeproj.

If you restart Xcode from ***.xcodeproj, it does not recognize the newly installed framework.

If you restart Xcode from ***.xcworkspace, Xcode recognizes the newly installed framework as shown below.

Right after the pod install, you can find the ***.xcworkspace file is created at the same directory of ***.xcodeproj.

Style Transfer Demo

 About U^2-Net for human portrait drawing

U^2-Net for human portrait drawing is so cool. I’m just curious that the Style Transfer could do the similar conversion or not. Here is the evaluation result.

Train Image

I used the following partial image from the U2-Net sample image as a training style image.

Parameters

Use Case: Video

Iterations: 400

Other Parameters: Default

Result

I implemented the human portrait drawing by the Style Transfer with Create ML. Here is the result.

Style Transfer Demo on iPhone8

malloc: nano zone abandoned due to inability to preallocate reserved vm space.

Symptom

malloc: nano zone abandoned due to inability to preallocate reserved vm space. message is displayed when the “Thead Sanitizer” of “Diagnostic” is checked.

Solution

A default Swift Project for iPhone with no control shows this message, when the “Thead Sanitizer” of “Diagnostic” is checked.

It means this message can be ignored, I believe.

nw_protocol_get_quic_image_block_invoke dlopen libquic failed

Symptom

nw_protocol_get_quic_image_block_invoke dlopen libquic failed” error always occurs.

        if let url = URL(string: baseUrlStr) {
            let task = URLSession.shared.dataTask(with: url) {(data, response, error) in
            }
            task.resume()
        }

Solution

Just ignore the error.

I tested the code above within main thread and global thread, and the error message was displayed in both cases. There is no way to avaid this error at this moment.

https://forums.swift.org/t/swift-firebase-connection/41632/6

Singleton

final class Settings {
    let userDefaults = UserDefaults.standard
    
    public static let shared = Settings()

    let defaults: [String : Int] = ["minute" : 4, "second" : 59]
    
    private init() {
        self.minute = userDefaults.integer(forKey: "minute")
        self.second = userDefaults.integer(forKey: "second")
    }

    private var _interval: Int = 0
    var interval: Int {
        get {
            return _interval
        }
        set {
            _interval = newValue
        }
    }
    
    private var _minute: Int = 0
    var minute: Int {
        get {
            return _minute
        }
        set {
            _minute = newValue
        }
    }
}

-------------------------
        // 設定を初期化する
        let _ = Settings.shared

        minStepper.value = Double(Settings.shared.minute)