Update biomap/utils_gee.py

biomap/utils_gee.py

@@ -1,157 +1,157 @@
-import io
-import requests
-import ee
-import numpy as np
-import matplotlib.pyplot as plt
-
-#Initialize
-service_account = 'cvimg-355@cvimg-377115.iam.gserviceaccount.com'
-credentials = ee.ServiceAccountCredentials(service_account, '.private-key.json')
-ee.Initialize(credentials)
-
-#delete clouds
-def maskS2clouds(image):
-        qa = image.select('QA60');
-
-    #   // Bits 10 and 11 are clouds and cirrus, respectively.
-        cloudBitMask = 1 << 10;
-        cirrusBitMask = 1 << 11;
-
-    #   // Both flags should be set to zero, indicating clear conditions.
-        mask = (qa.bitwiseAnd(cloudBitMask).eq(0))and(qa.bitwiseAnd(cirrusBitMask).eq(0))
-
-        return image.updateMask(mask).divide(10000);
-
-
-#find ee_img
-def extract_ee_img(location,start_date,end_date, width = 0.01 , len = 0.01) :
-    """Extract the earth engine image
-
-    Args:
-        location (list[float]): 
-        start_date (str): the start date for finding an image
-        end_date (str): the end date for finding an image
-        width (float, optional): _description_. Defaults to 0.01.
-        len (float, optional): _description_. Defaults to 0.01.
-
-    Returns:
-        _type_: _description_
-    """
-    # define the polygone
-    polygone =[[[float(location[0])-0.01,float(location[1])+0.01],
-                [float(location[0])-0.01,float(location[1])-0.01],
-                [float(location[0])+0.01,float(location[1])-0.01],
-                [float(location[0])+0.01,float(location[1])+0.01],
-    ]]    
-
-    #define the ee geometry 
-    geometry = ee.Geometry.Polygon(polygone, None, False);
-
-    #extract the dataset
-    dataset = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED')\
-        .filterDate(start_date, end_date)\
-        .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE',1))\
-        .map(maskS2clouds)
-    return dataset.mean(), geometry
-
-
-
-# Get URL
-def get_url(ee_img, geometry, scale=5):
-    """Get the url of a dataset and a geometry 
-
-    Args:
-        ee_img (ee.ImageCollection: meta data on the image 
-        geometry (ee.Geometry.Polygon): geometry of the desired landscape
-        scale (int, optional): _description_. Defaults to 5.
-
-    Returns:
-        str: the url to use to ask the server
-    """
-    region = geometry
-
-    # collectionList = ee_img.toList(ee_img.size())
-    # collectionSize = collectionList.size().getInfo()
-    # for i in xrange(collectionSize):
-    #     ee.batch.Export.image.toDrive(
-    #         image = ee.Image(collectionList.get(i)).clip(rectangle), 
-    #         fileNamePrefix = 'foo' + str(i + 1), 
-    #         dimensions = '128x128').start()
-
-    url = ee_img.getDownloadURL({
-        # 'min': 0.0,
-        # 'max': 0.3,
-        'bands': ['B4', 'B3', 'B2'],
-        'region' : region,
-        'scale' : scale,
-        'format' : 'NPY'
-    })
-    
-    return url
-
-def extract_np_from_url(url):
-    """extract a numpy array based on a url
-
-    Args:
-        url (str): _description_
-
-    Returns:
-        numpyarray: response from earth engine as numpy
-    """
-    #get the response from url
-    response = requests.get(url)
-
-    #transform it into numpy
-    data = np.load(io.BytesIO(response.content))
-    
-    #transform numpy of tuples to 3D numpy
-    temp1 = []
-
-    for x in data:
-        temp2 = []
-        for y in x :
-            temp2.append([z for z in y])
-        temp1.append(temp2)
-
-    data = np.array(temp1)
-
-    return data
-    
-#Fonction globale
-def extract_img(location,start_date,end_date, width = 0.01 , len = 0.01,scale=5):
-    """Extract an image of the landscape at the selected longitude and latitude with the selected width and length 
-
-    Args:
-        location (list[float]): [latitude of the center of the landscape, longitude of the center of the landscape]
-        start_date (str): the start date  
-        end_date (str): _description_
-        width (float, optional): _description_. Defaults to 0.01.
-        len (float, optional): _description_. Defaults to 0.01.
-        scale (int, optional): _description_. Defaults to 5.
-
-    Returns:
-        img: image as numpy array
-    """
-    ee_img, geometry = extract_ee_img(location, width,start_date,end_date , len)
-    url = get_url(ee_img, geometry, scale)
-    img = extract_np_from_url(url)
-    
-    return img
-
-# transform img from numpy to PIL
-def transform_ee_img(img, min = 0, max=0.3):
-    """Transform an img from numpy to PIL
-
-    Args:
-        img (numpy array): the original image as a numpy array 
-        min (int, optional): _description_. Defaults to 0.
-        max (float, optional): _description_. Defaults to 0.3.
-
-    Returns:
-        img_test: a PIL image 
-    """
-    img_test=img
-    img_test=np.minimum(img_test*255/max,np.ones(img.shape)*255)
-    img_test=np.uint8((np.rint(img_test)).astype(int))
-    plt.imshow(img_test)
+import io
+import requests
+import ee
+import numpy as np
+import matplotlib.pyplot as plt
+
+#Initialize
+service_account = 'cvimg-355@cvimg-377115.iam.gserviceaccount.com'
+credentials = ee.ServiceAccountCredentials(service_account, 'biomap/.private-key.json')
+ee.Initialize(credentials)
+
+#delete clouds
+def maskS2clouds(image):
+        qa = image.select('QA60');
+
+    #   // Bits 10 and 11 are clouds and cirrus, respectively.
+        cloudBitMask = 1 << 10;
+        cirrusBitMask = 1 << 11;
+
+    #   // Both flags should be set to zero, indicating clear conditions.
+        mask = (qa.bitwiseAnd(cloudBitMask).eq(0))and(qa.bitwiseAnd(cirrusBitMask).eq(0))
+
+        return image.updateMask(mask).divide(10000);
+
+
+#find ee_img
+def extract_ee_img(location,start_date,end_date, width = 0.01 , len = 0.01) :
+    """Extract the earth engine image
+
+    Args:
+        location (list[float]): 
+        start_date (str): the start date for finding an image
+        end_date (str): the end date for finding an image
+        width (float, optional): _description_. Defaults to 0.01.
+        len (float, optional): _description_. Defaults to 0.01.
+
+    Returns:
+        _type_: _description_
+    """
+    # define the polygone
+    polygone =[[[float(location[0])-0.01,float(location[1])+0.01],
+                [float(location[0])-0.01,float(location[1])-0.01],
+                [float(location[0])+0.01,float(location[1])-0.01],
+                [float(location[0])+0.01,float(location[1])+0.01],
+    ]]    
+
+    #define the ee geometry 
+    geometry = ee.Geometry.Polygon(polygone, None, False);
+
+    #extract the dataset
+    dataset = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED')\
+        .filterDate(start_date, end_date)\
+        .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE',1))\
+        .map(maskS2clouds)
+    return dataset.mean(), geometry
+
+
+
+# Get URL
+def get_url(ee_img, geometry, scale=5):
+    """Get the url of a dataset and a geometry 
+
+    Args:
+        ee_img (ee.ImageCollection: meta data on the image 
+        geometry (ee.Geometry.Polygon): geometry of the desired landscape
+        scale (int, optional): _description_. Defaults to 5.
+
+    Returns:
+        str: the url to use to ask the server
+    """
+    region = geometry
+
+    # collectionList = ee_img.toList(ee_img.size())
+    # collectionSize = collectionList.size().getInfo()
+    # for i in xrange(collectionSize):
+    #     ee.batch.Export.image.toDrive(
+    #         image = ee.Image(collectionList.get(i)).clip(rectangle), 
+    #         fileNamePrefix = 'foo' + str(i + 1), 
+    #         dimensions = '128x128').start()
+
+    url = ee_img.getDownloadURL({
+        # 'min': 0.0,
+        # 'max': 0.3,
+        'bands': ['B4', 'B3', 'B2'],
+        'region' : region,
+        'scale' : scale,
+        'format' : 'NPY'
+    })
+    
+    return url
+
+def extract_np_from_url(url):
+    """extract a numpy array based on a url
+
+    Args:
+        url (str): _description_
+
+    Returns:
+        numpyarray: response from earth engine as numpy
+    """
+    #get the response from url
+    response = requests.get(url)
+
+    #transform it into numpy
+    data = np.load(io.BytesIO(response.content))
+    
+    #transform numpy of tuples to 3D numpy
+    temp1 = []
+
+    for x in data:
+        temp2 = []
+        for y in x :
+            temp2.append([z for z in y])
+        temp1.append(temp2)
+
+    data = np.array(temp1)
+
+    return data
+    
+#Fonction globale
+def extract_img(location,start_date,end_date, width = 0.01 , len = 0.01,scale=5):
+    """Extract an image of the landscape at the selected longitude and latitude with the selected width and length 
+
+    Args:
+        location (list[float]): [latitude of the center of the landscape, longitude of the center of the landscape]
+        start_date (str): the start date  
+        end_date (str): _description_
+        width (float, optional): _description_. Defaults to 0.01.
+        len (float, optional): _description_. Defaults to 0.01.
+        scale (int, optional): _description_. Defaults to 5.
+
+    Returns:
+        img: image as numpy array
+    """
+    ee_img, geometry = extract_ee_img(location, width,start_date,end_date , len)
+    url = get_url(ee_img, geometry, scale)
+    img = extract_np_from_url(url)
+    
+    return img
+
+# transform img from numpy to PIL
+def transform_ee_img(img, min = 0, max=0.3):
+    """Transform an img from numpy to PIL
+
+    Args:
+        img (numpy array): the original image as a numpy array 
+        min (int, optional): _description_. Defaults to 0.
+        max (float, optional): _description_. Defaults to 0.3.
+
+    Returns:
+        img_test: a PIL image 
+    """
+    img_test=img
+    img_test=np.minimum(img_test*255/max,np.ones(img.shape)*255)
+    img_test=np.uint8((np.rint(img_test)).astype(int))
+    plt.imshow(img_test)
     return img_test