Handle float32

plantcv/geospatial/images.py

@@ -46,6 +46,7 @@ def __init__(self, input_array: np.ndarray, filename: str, wavelengths: list,
                  default_wavelengths: list, crs: str, transform: affine.Affine, nodata: float):
         super().__init__()
         self.thumb = self._create_thumb()
+        self.nodata = 0
 
     def __array_finalize__(self, obj):
         super().__array_finalize__(obj)
@@ -107,6 +108,7 @@ def __init__(self, input_array: np.ndarray, filename: str, crs: str,
         super().__init__()
         self.data_array = self._gray_cutoff()
         self.thumb = self._create_thumb()
+        #self.nodata = 0
 
     def __array_finalize__(self, obj):
         super().__array_finalize__(obj)
@@ -127,7 +129,7 @@ def _gray_cutoff(self):
         img_copy = np.squeeze(self)
         if self.cutoff is not None :
             quantile = np.quantile(img_copy, self.cutoff)
-            img_copy[img_copy >= quantile] = np.nan
+            img_copy[img_copy >= quantile] = 0
         return img_copy
 
     def _create_thumb(self):
@@ -138,7 +140,7 @@ def _create_thumb(self):
         numpy.ndarray
             Stretched thumbnail
         """
-        img_copy = self.data_array
+        img_copy = self.data_array.astype(np.float64)
         # Change nodata values to Nan
         img_copy[img_copy == self.nodata] = np.nan
         # Stretch values to min/max for visualization

plantcv/geospatial/read/geotif.py

@@ -66,6 +66,8 @@ def _read_geotif_and_shapefile(filename, cropto):
     -------
     img_data : numpy.ndarray
         Image data array with shape ``(bands, height, width)``.
+    nodata_indicies : numpy.ndarray
+        Coordinates of missing data that has been replaced with 0s.
     metadata : dict
         Rasterio metadata dictionary including CRS, transform, and driver
         information.
@@ -152,7 +154,16 @@ def geotif(filename, bands="R,G,B", cropto=None, cutoff=None):
 
     # Check if img is uint16
     if img_data.dtype == "uint16":
+        img_data = np.where(img_data == metadata["nodata"], 0, img_data)
         img_data = ((img_data/65535.0) * 255.0).astype(np.uint8)
+
+    # Check if img is float32
+    if img_data.dtype == "float32":
+        # Replace nodata with 0
+        img_data = np.where(img_data == metadata["nodata"], 0, img_data)
+        img_data = img_data ** (1 / 2.2)
+        img_data = (img_data * 255).astype("uint8")
+
     if depth > 1:
         # Make a GEO instance before calculating a pseudo-rgb
         obj = GEO(input_array=img_data,