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ee.data.computePixels (Python only)
透過集合功能整理內容
你可以依據偏好儲存及分類內容。
對圖片資料執行任意計算,藉此計算圖塊。
傳回:
以原始圖片資料形式傳回像素。
用量 | 傳回 |
ee.data.computePixels(params) | 物件|值 |
引數 | 類型 | 詳細資料 |
params | 物件 | 物件,內含下列可能值的參數:
expression - 要計算的運算式。
fileFormat :產生的檔案格式。預設值為 png。如需可用格式,請參閱「ImageFileFormat」。此外,還有其他格式可將下載的物件轉換為 Python 資料物件。包括:
NUMPY_NDARRAY ,可轉換為結構化 NumPy 陣列。
grid - 描述要擷取資料的像素格線的參數。
預設為資料的原生像素格線。
bandIds - 如有,則指定要從中取得像素的特定頻帶組合。
visualizationOptions - 如果存在,則為一組要套用的視覺化選項,用來產生資料的 8 位元 RGB 視覺化結果,而不是傳回原始資料。
workloadTag - 使用者提供的標記,用於追蹤這項運算。 |
範例
Python 設定
請參閱
Python 環境頁面,瞭解 Python API 和如何使用 geemap
進行互動式開發。
import ee
import geemap.core as geemap
Colab (Python)
# Region of interest.
coords = [
-121.58626826832939,
38.059141484827485,
]
region = ee.Geometry.Point(coords)
# Sentinel-2 median composite.
image = (ee.ImageCollection('COPERNICUS/S2')
.filterBounds(region)
.filterDate('2020-04-01', '2020-09-01')
.median())
# Make a projection to discover the scale in degrees.
proj = ee.Projection('EPSG:4326').atScale(10).getInfo()
# Get scales out of the transform.
scale_x = proj['transform'][0]
scale_y = -proj['transform'][4]
# Make a request object.
request = {
'expression': image,
'fileFormat': 'PNG',
'bandIds': ['B4', 'B3', 'B2'],
'grid': {
'dimensions': {
'width': 640,
'height': 640
},
'affineTransform': {
'scaleX': scale_x,
'shearX': 0,
'translateX': coords[0],
'shearY': 0,
'scaleY': scale_y,
'translateY': coords[1]
},
'crsCode': proj['crs'],
},
'visualizationOptions': {'ranges': [{'min': 0, 'max': 3000}]},
}
image_png = ee.data.computePixels(request)
# Do something with the image...
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上次更新時間:2025-07-26 (世界標準時間)。
[null,null,["上次更新時間:2025-07-26 (世界標準時間)。"],[[["\u003cp\u003e\u003ccode\u003eee.data.computePixels\u003c/code\u003e computes a tile by performing an arbitrary computation on image data and returns the pixels as raw image data.\u003c/p\u003e\n"],["\u003cp\u003eThe \u003ccode\u003eparams\u003c/code\u003e argument to \u003ccode\u003eee.data.computePixels\u003c/code\u003e allows for customizing the computation through an expression, file format, pixel grid, band selection, visualization options, and workload tag.\u003c/p\u003e\n"],["\u003cp\u003eThe provided Python example demonstrates using \u003ccode\u003eee.data.computePixels\u003c/code\u003e to retrieve a PNG image tile from a Sentinel-2 median composite with specified visualization and grid parameters.\u003c/p\u003e\n"]]],[],null,["# ee.data.computePixels (Python only)\n\n\u003cbr /\u003e\n\nComputes a tile by performing an arbitrary computation on image data.\n\n\u003cbr /\u003e\n\nReturns:\nThe pixels as raw image data.\n\n| Usage | Returns |\n|---------------------------------|---------------|\n| `ee.data.computePixels(params)` | Object\\|Value |\n\n| Argument | Type | Details |\n|----------|--------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `params` | Object | An object containing parameters with the following possible values: `expression` - The expression to compute. `fileFormat` - The resulting file format. Defaults to png. See [ImageFileFormat](https://developers.google.com/earth-engine/reference/rest/v1/ImageFileFormat) for the available formats. There are additional formats that convert the downloaded object to a Python data object. These include: `NUMPY_NDARRAY`, which converts to a structured NumPy array. `grid` - Parameters describing the pixel grid in which to fetch data. Defaults to the native pixel grid of the data. `bandIds` - If present, specifies a specific set of bands from which to get pixels. `visualizationOptions` - If present, a set of visualization options to apply to produce an 8-bit RGB visualization of the data, rather than returning the raw data. `workloadTag` - User supplied tag to track this computation. |\n\nExamples\n--------\n\nPython setup\n\nSee the [Python Environment](/earth-engine/guides/python_install) page for information on the Python API and using\n`geemap` for interactive development. \n\n```python\nimport ee\nimport geemap.core as geemap\n```\n\n### Colab (Python)\n\n```python\n# Region of interest.\ncoords = [\n -121.58626826832939,\n 38.059141484827485,\n]\nregion = ee.Geometry.Point(coords)\n\n# Sentinel-2 median composite.\nimage = (ee.ImageCollection('COPERNICUS/S2')\n .filterBounds(region)\n .filterDate('2020-04-01', '2020-09-01')\n .median())\n\n# Make a projection to discover the scale in degrees.\nproj = ee.Projection('EPSG:4326').atScale(10).getInfo()\n\n# Get scales out of the transform.\nscale_x = proj['transform'][0]\nscale_y = -proj['transform'][4]\n\n# Make a request object.\nrequest = {\n 'expression': image,\n 'fileFormat': 'PNG',\n 'bandIds': ['B4', 'B3', 'B2'],\n 'grid': {\n 'dimensions': {\n 'width': 640,\n 'height': 640\n },\n 'affineTransform': {\n 'scaleX': scale_x,\n 'shearX': 0,\n 'translateX': coords[0],\n 'shearY': 0,\n 'scaleY': scale_y,\n 'translateY': coords[1]\n },\n 'crsCode': proj['crs'],\n },\n 'visualizationOptions': {'ranges': [{'min': 0, 'max': 3000}]},\n}\n\nimage_png = ee.data.computePixels(request)\n# Do something with the image...\n```"]]