Update lib.py

lib.py

@@ -1,14 +1,10 @@
 import os
 import ee
-import geemap.foliumap as geemap
-import gradio as gr
-from matplotlib import pyplot as plt
-import requests
+import folium
+import folium.plugins as plugins
 
-# geemap.update_package()
-
-# Connect GEE API
-GEE_CREDENTIALS_FILE = "./data/gee/geospatial_api_key.json"
+# GEE Authentication
+GEE_CREDENTIALS_FILE = "./data/key/geospatial.json"
 if not os.path.exists(GEE_CREDENTIALS_FILE):
     credentials = ee.ServiceAccountCredentials(None, key_data=os.getenv("geospatial_api_key"))
 else:
@@ -17,205 +13,126 @@ else:
 ee.Initialize(credentials)
 
 
-##! --------------- PARAMS --------------- !##
-ROI = ee.Geometry.Polygon([[37.279358,36.412414],[37.279358, 39.925815],[42.393494, 39.925815],[42.393494, 36.412414],[37.279358, 36.412414]])
-
+# Constants
 CLASSES = {
-    "names" : ["Water", "Trees", "Flooded Vegetation", "Crops", "Built Area", "Bare Ground", "Snow/Ice", "Clouds", "Rangeland"],
-    "colors" : ["1a5bab", "358221", "87d19e", "ffdb5c", "ed022a", "ede9e4", "f2faff", "c8c8c8", "c6ad8d"]
+    "names": ["Water", "Trees", "Flooded Vegetation", "Crops", "Built Area", "Bare Ground", "Snow/Ice", "Clouds", "Rangeland"],
+    "colors": ["1a5bab", "358221", "87d19e", "ffdb5c", "ed022a", "ede9e4", "f2faff", "c8c8c8", "c6ad8d"],
 }
-CLOUD_FILTER = 60
-CLD_PRB_THRESH = 50
-NIR_DRK_THRESH = 0.15
-CLD_PRJ_DIST = 1
-BUFFER = 50
+
 CLOUDY_PIXEL_PERCENTAGE = 12
 S2_START_DATE = "2023-08-01"
 S2_END_DATE = "2023-08-31"
 S2_LULC_START_DATE = "2023-01-01"
 S2_LULC_END_DATE = "2023-12-31"
 
-S2_VIS_PARAMS = {
-    "bands": ["B4", "B3", "B2"],
-    "min": 0,
-    "max": 3000,
-    "gamma": [1, 1, 1],
-}
-
-
-
-
-##! --------------- Functions --------------- !##
-def VisualizeMatplot(image, roi):
-    """Görüntüyü numpy array olarak alma"""
-    image = geemap.ee_to_numpy(image, region=roi, bands=["B4", "B3", "B2"])
-
-    plt.figure(figsize=(10, 10))
-    plt.imshow(image)
-    plt.axis("off")
-    plt.show()
-
-
-def GMap2Gradio(image, vis_params):
-    # Görüntüyü RGB bantları ile görselleştirme
-    vis_params = {"bands": ["B4", "B3", "B2"], "min": 0, "max": 3000}
-    gMap = geemap.Map()
-    gMap.add_layer(image, vis_params, "Sentinel-2 Image")
-    return gMap.to_gradio()
-
-
-def ApplyBasicS2CloudMask(image):
-    cloud_mask = image.select("MSK_CLASSI_CIRRUS").lt(1)
-    return image.updateMask(cloud_mask)
+S2_VIS_PARAMS = {"bands": ["B4", "B3", "B2"], "min": 0, "max": 3000, "gamma": [1, 1, 1]}
 
 
+# UTILS
 def ApplyBitwiseS2CloudMask(image):
+    """QA60 bandı üzerinden bitwise bulut maskesi uygular."""
     qa = image.select("QA60")
-
-    # QA Bandının Bit 10 ve 11 bulut ve bulut gölgeleri için kullanılır
-    cloudBitMask = 1 << 10
-
-    cirrusBitMask = 1 << 11
-
+    cloudBitMask = 1 << 10  # Bit 10: yoğun bulut
+    cirrusBitMask = 1 << 11  # Bit 11: cirrus bulut
     mask = qa.bitwiseAnd(cloudBitMask).eq(0).And(qa.bitwiseAnd(cirrusBitMask).eq(0))
-
-    return image.updateMask(mask) #.divide(10000)
+    return image.updateMask(mask)
 
 
 def ReColormap(image, old_colors: list, new_colors: list):
+    """LULC sınıf değerlerini yeniden eşler."""
     return image.remap(old_colors, new_colors)
 
 
-def CalculateRasterOverlapByROI(image, roi: ee.Geometry):
-    intersection = image.clip(roi).reduceRegion(
-        reducer=ee.Reducer.count(),
-        geometry=roi,
-        scale=10,
-        maxPixels=1e9
-    )
-    count = ee.Number(intersection.get("B4"))  # B4 bandı kullanılıyor
-    return image.set("overlap", count)
-
-
-def GetS2SRCloudCollection(s2_collection: ee.ImageCollection, roi: ee.Geometry, start_date: str, end_date: str):
-    s2_cloudless_col = (ee.ImageCollection("COPERNICUS/S2_CLOUD_PROBABILITY")
-        .filterBounds(roi)
-        .filterDate(start_date, end_date))
-
-    return ee.ImageCollection(ee.Join.saveFirst("s2cloudless")
-        .apply(
-            primary=s2_collection,
-            secondary=s2_cloudless_col,
-            condition=ee.Filter.equals(leftField="system:index", rightField="system:index")
-        )
-    )
-
-
-
-# Her bir görüntü için alanı hesapla ve NoData piksellerini dışla
-def add_area_attribute(image, roi):
-    # Belirli geometri içindeki piksel alanlarını hesapla
-    pixel_area = image.select(0).multiply(ee.Image.pixelArea())
-    
-    # Hesaplanan alanı, istenen geometri içinde özetle
-    region_area = pixel_area.reduceRegion(
-        reducer=ee.Reducer.sum(),
-        geometry=roi,
-        scale=10,         # Sentinel-2 için 10 metre çözünürlük
-        maxPixels=1e13
-    ).get("B1")  # Burada B1 rastgele bir banttır. İlgili bantlardan biri kullanılabilir
-    
-    # Yeni bir attribute olarak alanı ekle
-    return image.set("REGION_AREA", region_area)
-
-
-def CalculateIntersectionArea(image, roi):
-    # Görüntüyü maskele ve konveks kaplamasını hesapla
+def CalculateIntersectionArea(image, roi: ee.Geometry):
+    """Görüntü geometrisi ile ROI arasındaki kesişim alanını hesaplar."""
     convex_hull = image.geometry().convexHull()
-    
-    # Konveks kaplaman��n region ile kesişimini hesapla
     intersection = convex_hull.intersection(roi)
-    
-    # Kesişimin alanını hesapla
-    intersection_area = intersection.area()
-    
-    # Yeni bir attribute olarak kesişim alanını ekle
-    return image.set("INTERSECTION_AREA", intersection_area)
-
+    return image.set("INTERSECTION_AREA", intersection.area())
 
 
 def FilterBestAreaOfMGRSJoin(image):
-    return ee.ImageCollection.fromImages(
-        image.get("mgrs_tile_match")
-    ).sort("INTERSECTION_AREA", False).first()
+    """Aynı MGRS tile içinden en büyük kesişim alanına sahip görüntüyü seçer."""
+    return ee.ImageCollection.fromImages(image.get("mgrs_tile_match")).sort("INTERSECTION_AREA", False).first()
+
 
+def AddGEELayer(fmap: folium.Map, ee_image, vis_params: dict, name: str, show: bool = True):
+    """GEE imajını folium haritasına tile katmanı olarak ekler."""
+    map_id = ee_image.getMapId(vis_params)
+    tile_url = map_id["tile_fetcher"].url_format
+    folium.TileLayer(tiles=tile_url, attr="Google Earth Engine", name=name, overlay=True, control=True, show=show).add_to(fmap)
 
 
-def RequestFunction(roi):
-    if roi is None:
+def RequestFunction(roi_coords: list) -> folium.Map:
+    """
+    Verilen koordinat listesinden Sentinel-2 ve ESRI LULC katmanlarını
+    içeren bir Folium haritası döndürür.
+    """
+    if not roi_coords:
         return None
 
-    roi = ee.Geometry.Polygon(roi)
+    roi = ee.Geometry.Polygon(roi_coords)
 
-    ##! --------------- Dataset --------------- !##
-    # Gather S2 Data
+    # ROI merkezini hesapla (harita başlangıç konumu)
+    centroid = roi.centroid().getInfo()["coordinates"]
+    center_lat = centroid[1]
+    center_lon = centroid[0]
+
+    # Sentinel-2 koleksiyonu
     S2Collection = (
         ee.ImageCollection("COPERNICUS/S2_HARMONIZED")
-            .filterBounds(roi)
-            .filterDate(S2_START_DATE, S2_END_DATE)
-            .filter(ee.Filter.lt("CLOUDY_PIXEL_PERCENTAGE", CLOUDY_PIXEL_PERCENTAGE))
-            .sort("CLOUDY_PIXEL_PERCENTAGE", ascending=True)
+        .filterBounds(roi)
+        .filterDate(S2_START_DATE, S2_END_DATE)
+        .filter(ee.Filter.lt("CLOUDY_PIXEL_PERCENTAGE", CLOUDY_PIXEL_PERCENTAGE))
+        .sort("CLOUDY_PIXEL_PERCENTAGE", ascending=True)
     )
 
-    # Gather Mask Data
-    esriLULCCollection = ee.ImageCollection("projects/sat-io/open-datasets/landcover/ESRI_Global-LULC_10m_TS").filterDate(S2_LULC_START_DATE, S2_LULC_END_DATE)
-    esriLULCCollection = (
-        ee.ImageCollection(esriLULCCollection.mosaic().clip(roi))
-        .map(lambda img: ReColormap(img, [1, 2, 4, 5, 7, 8, 9, 10, 11], [1, 2, 3, 4, 5, 6, 7, 8, 9]))
+    # ESRI LULC koleksiyonu
+    esriLULCCollection = ee.ImageCollection("projects/sat-io/open-datasets/landcover/ESRI_Global-LULC_10m_TS").filterDate(
+        S2_LULC_START_DATE, S2_LULC_END_DATE
+    )
+    esriLULCCollection = ee.ImageCollection(esriLULCCollection.mosaic().clip(roi)).map(
+        lambda img: ReColormap(img, [1, 2, 4, 5, 7, 8, 9, 10, 11], [1, 2, 3, 4, 5, 6, 7, 8, 9])
     )
 
-    #! Get Info
-    image_info = S2Collection.first().getInfo()
-    print(list(zip(list(image_info["properties"].keys()), list(image_info["properties"].values()))))
-    print(image_info["bands"])
-
-    # Cloud Masking
+    # Bulut maskeleme
     S2Collection = S2Collection.map(ApplyBitwiseS2CloudMask)
 
-    # Area Calculation
-    S2Collection = S2Collection.map(lambda image: CalculateIntersectionArea(image, roi)).sort("INTERSECTION_AREA", False)
-    
-    # MGRS_TILE Match
+    # Alan hesaplama ve MGRS tile eşleştirme
+    S2Collection = S2Collection.map(lambda img: CalculateIntersectionArea(img, roi)).sort("INTERSECTION_AREA", False)
+
     joinCol = ee.Join.saveAll("mgrs_tile_match").apply(
-        S2Collection.distinct("MGRS_TILE"),
-        S2Collection,
-        ee.Filter.equals(leftField="MGRS_TILE", rightField="MGRS_TILE")
+        primary=S2Collection.distinct("MGRS_TILE"),
+        secondary=S2Collection,
+        condition=ee.Filter.equals(leftField="MGRS_TILE", rightField="MGRS_TILE"),
     )
 
     bestCollection = ee.ImageCollection(joinCol.map(FilterBestAreaOfMGRSJoin))
     s2Image = bestCollection.mosaic().clip(roi)
-    # print(bestCollection.aggregate_array("INTERSECTION_AREA").getInfo())
 
-
-    #! --------------- Mask Match --------------- !##
+    # LULC maskesi: yalnızca geçerli S2 piksellerini göster
     esriLULCImage = esriLULCCollection.first().updateMask(s2Image.select("B4").gt(0))
 
+    # Folium MAP
+    fmap = folium.Map(location=[center_lat, center_lon], zoom_start=10, tiles=None)
 
-    ##! --------------- Create FoliumGee Map --------------- !##
-    map = geemap.Map(center=[37.279358, 36.412414], zoom=10, add_google_map=False)
-    map.add_layer(roi, {"color": "black"}, "Pilot Bölge")
-    map.add_layer(s2Image, S2_VIS_PARAMS, "RGB")
-    map.add_layer(esriLULCImage, {"min": 1, "max": 9, "palette": CLASSES["colors"]}, "LULC")
-
-    return map
-
+    # Temel katman
+    folium.TileLayer(tiles="https://tile.openstreetmap.org/{z}/{x}/{y}.png", attr="OpenStreetMap", name="OpenStreetMap", show=True).add_to(
+        fmap
+    )
 
+    folium.TileLayer(
+        tiles="https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}", attr="Esri", name="Esri Uydu"
+    ).add_to(fmap)
 
-if "__main__" == __name__:
+    # ROI sınırı
+    roi_geojson = roi.getInfo()
+    folium.GeoJson(roi_geojson, name="Pilot Bölge", style_function=lambda x: {"color": "black", "weight": 2, "fillOpacity": 0}).add_to(fmap)
 
-    html = RequestFunction(ROI)
+    # GEE katmanları
+    AddGEELayer(fmap, s2Image, S2_VIS_PARAMS, "Sentinel-2 RGB")
+    AddGEELayer(fmap, esriLULCImage, {"min": 1, "max": 9, "palette": CLASSES["colors"]}, "ESRI LULC")
 
-    with gr.Blocks() as app:
-        web = gr.HTML(html, label="Map")
+    folium.LayerControl(position="bottomleft").add_to(fmap)
+    plugins.Fullscreen(position="topright").add_to(fmap)
 
-    app.launch()
+    return fmap