Rasha G. Salim rasha-salim

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Working from home

Lead Machine Learning Engineer | Passionate about Creating solutions for world problems | Problem solver

rasha-salim / Georefrencing_PNG_Full.py

Created October 2, 2022 21:59

	import rasterio
	from rasterio import features
	from rasterio.io import MemoryFile
	import math
	import geopandas as gpd
	import os


	w = 400
	h = 400

rasha-salim / Generating_Goejason.py

Last active October 2, 2022 21:47

	def getGeojson():
	# Store the mask coordinates into a geojson file
	bounds = getImgBounds()
	dataset = rasterio.open(png_path)
	bands = [1]
	data = dataset.read(bands)
	transform = rasterio.transform.from_bounds(bounds[0], bounds[1], bounds[2], bounds[3], data.shape[1], data.shape[2])
	crs = rasterio.crs.CRS({"init": "epsg:4326"}) # 3857 Google Maps Projection 4326 World wide (3D)
	with MemoryFile() as memfile:
	meta = {"count": 1, "width": data.shape[1], "height": data.shape[2], "transform": transform, "nodata": 0, "crs": crs, "dtype":data.dtype}

rasha-salim / Generating_Goejason.py

Created October 2, 2022 21:39

	def getGeojson():
	# Store the mask coordinates into a geojson file
	bounds = getImgBounds()
	dataset = rasterio.open(png_path)
	bands = [1]
	data = dataset.read(bands)
	transform = rasterio.transform.from_bounds(bounds[0], bounds[1], bounds[2], bounds[3], data.shape[1], data.shape[2])
	crs = rasterio.crs.CRS({"init": "epsg:4326"}) # 3857 Google Maps Projection 4326 World wide (3D)
	with MemoryFile() as memfile:
	meta = {"count": 1, "width": data.shape[1], "height": data.shape[2], "transform": transform, "nodata": 0, "crs": crs, "dtype":data.dtype}

rasha-salim / Georefrencing_PNG.py

Last active October 6, 2022 21:06

	import os
	import math
	import rasterio
	from rasterio import features

	# Specify the image parameters
	w = 400
	h = 400
	zoom = 8
	lat = -19.361500892883598

rasha-salim / uploading_data_hub.py

Last active May 12, 2021 19:49

Passing our images to hub Dataset object and upload it to server

	# I just extracted the input, rgb, and segmentation to separate arrays for convenience
	inputs = []
	masks = []
	rgb_inputs = []
	for pairs in images_target:
	inputs.append(pairs['input'])
	masks.append(pairs['mask'])
	rgb_inputs.append(pairs['input_rgb'])

	# Pass the data to our Dataset instance we created earlier

rasha-salim / reading_lcn_dataset.py

Created February 7, 2021 09:08

Reading LandCoverNet dataset into array before uploading it to activeloop

	inputs_dir = '/content/landcovernet/inputs' # The directory where our input resides
	targets_dir = '/content/landcovernet/targets' # The directory where our target resides
	# First stack all the bands togather

	def process_tiffs(inputs_dir, target_dir):
	data = []
	sub_dir_list = []
	images_target = {}
	stacked_imgs = []
	list_bands = []

rasha-salim / reading_landcovernet_data.py

Created February 7, 2021 09:06

	inputs_dir = '/content/landcovernet/inputs' # The directory where our input resides
	targets_dir = '/content/landcovernet/targets' # The directory where our target resides
	# First stack all the bands togather

	def process_tiffs(inputs_dir, target_dir):
	data = []
	sub_dir_list = []
	images_target = {}
	stacked_imgs = []
	list_bands = []

rasha-salim / reading_landcovernet_data

Created February 7, 2021 09:05

Reading our data into a numpy array

	inputs_dir = '/content/landcovernet/inputs' # The directory where our input resides
	targets_dir = '/content/landcovernet/targets' # The directory where our target resides
	# First stack all the bands togather

	def process_tiffs(inputs_dir, target_dir):
	data = []
	sub_dir_list = []
	images_target = {}
	stacked_imgs = []
	list_bands = []

rasha-salim / creating_dataset.py

Created February 7, 2021 08:45

Createing Dataset object in activeloop hub

	import hub
	from hub.schema import Image
	from hub.schema import Tensor
	from hub.schema import Mask
	from hub.schema import Segmentation

	# include your user anme and a name for your dataset
	tag = "rasha/landCoverNet_Omdena_Sample"
	ds = {}
	# Define youe dataset object

rasha-salim / PY0101EN-5-1-Numpy1D.ipynb

Created May 6, 2020 13:20

Created on Skills Network Labs

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