Merge pull request #889 from danforthcenter/write-hyperspectral-data

Write hyperspectral data

Author: HaleySchuhl

docs/updating.md

@@ -390,6 +390,11 @@ pages for more details on the input and output variable types.
 * pre v4.x: NA
 * post v4.x: rot_hsi = **plantcv.hyperspectral.rot90**(*spectral_data, k*)
 
+#### plantcv.hyperspectral.write_data
+
+* pre v4.0: NA
+* post v4.0: **plantcv.hyperspectral.write_data**(*filename, spectral_data*)
+
 #### plantcv.image_add
 
 * pre v3.0dev2: device, added_img = **plantcv.image_add**(*img1, img2, device, debug=None*)

docs/write_data.md

@@ -0,0 +1,41 @@
+## Write data
+
+Write a hyperspectral image in ENVI format to the specified file.
+It creates a text header file with extension .hdr and a binary file with
+extension .raw.  This function only supports Band-Interleaved-by-Line (BIL)
+interleave.
+
+**plantcv.hyperspectral.write_data**(*filename, spectral_data*):
+
+
+- **Parameters:**
+    - filename- desired name of the hyperspectral image file. The extensions are ignored and .hdr and .raw are used.
+    - spectral_data- Hyperspectral data object
+
+- **Context:**
+    - Used to save a modified hyperspectral image
+
+- **Example use:**
+
+```python
+from plantcv import plantcv as pcv
+
+modified_spectral = pcv.Spectral_data(array_data=modified_array_data,
+                          max_wavelength=list(source_spectral.wavelength_dict.keys())[-1],
+                          min_wavelength=list(source_spectral.wavelength_dict.keys())[0],
+                          max_value=float(np.amax(modified_array_data)),
+                          min_value=float(np.amin(modified_array_data)),
+                          d_type=modified_array_data.dtype,
+                          wavelength_dict=source_spectral.wavelength_dict,
+                          samples=modified_array_data.shape[1],
+                          lines=modified_array_data.shape[0], interleave='bil',
+                          wavelength_units=source_spectral.wavelength_units,
+                          array_type="datacube",
+                          pseudo_rgb=None,
+                          filename=source_spectral.filename,
+                          default_bands=None)
+
+pcv.hyperspectral.write_data('test-hyperspectral', modified_spectral)
+```
+
+**Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/master/plantcv/plantcv/hyperspectral/write_data.py)

mkdocs.yml

@@ -70,8 +70,9 @@ nav:
           - 'Calibrate': calibrate.md
           - 'Spectral Index': spectral_index.md
           - 'Extract wavelength': extract_wavelength.md
-          - 'Spectral data objects': Spectral_data.md
           - 'Rotate Hyperspectral Datacubes ': hyperspectral_rot90.md
+          - 'Spectral data objects': Spectral_data.md
+          - 'Write data': write_data.md
       - 'Image Add': image_add.md
       - 'Image Subtract': image_subtract.md
       - 'Image Fusion': image_fusion.md

plantcv/plantcv/hyperspectral/__init__.py

@@ -8,7 +8,9 @@
 from plantcv.plantcv.hyperspectral._avg_reflectance import _avg_reflectance
 from plantcv.plantcv.hyperspectral._inverse_covariance import _inverse_covariance
 from plantcv.plantcv.hyperspectral.rot90 import rot90
+from plantcv.plantcv.hyperspectral.write_data import write_data
 
 # add new functions to end of lists
 __all__ = ["read_data", "_find_closest", "analyze_spectral", "analyze_index", "calibrate",
-           "_make_pseudo_rgb", "extract_wavelength", "_avg_reflectance", "_inverse_covariance", "rot90"]
+           "_make_pseudo_rgb", "extract_wavelength", "_avg_reflectance", "_inverse_covariance",
+           "rot90", "write_data"]

plantcv/plantcv/hyperspectral/write_data.py

@@ -0,0 +1,47 @@
+import os
+import numpy as np
+
+from plantcv.plantcv import _version
+
+__version__ = _version.get_versions()['version']
+
+def write_data(filename, spectral_data):
+    """Write hyperspectral image data to a file.
+    Inputs:
+    filename        = Name of file to write
+    spectral_data   = Hyperspectral data object
+
+    Returns:
+
+    :param filename: str
+    :param spectral_data: __main__.Spectral_data
+    """
+
+    filename = os.path.splitext(filename)[0]
+
+    # create header
+    lines, samples, bands = spectral_data.array_data.shape
+    dtype_dict = {'B': "1", 'h': "2", 'i': "3", 'f': "4", 'd': "5", 'F': "6",
+                  'D': "9", 'H': "12", 'I': "13", 'l': "14", 'L': "15"}
+    wavelenghths = list(spectral_data.wavelength_dict.keys())
+    with open(filename+'.hdr', mode='w') as f:
+        f.write('ENVI\n')
+        f.write(f'; this file was created using PlantCV version {__version__}\n')
+        f.write(f'; original file: {spectral_data.filename}\n')
+        f.write('interleave = bil\n')
+        f.write(f'samples = {samples}\n')
+        f.write(f'lines = {lines}\n')
+        f.write(f'bands = {bands}\n')
+        f.write(f'data type = {dtype_dict[spectral_data.array_data.dtype.char]}\n')
+        f.write(f'wavelength units = {spectral_data.wavelength_units}\n')
+        f.write(f'default bands ={spectral_data.default_bands}\n')
+        f.write('wavelength = {\n')
+        for wl in wavelenghths[:-1]:
+            f.write(f'{wl},\n')
+        f.write(f'{wavelenghths[-1]}\n')
+        f.write('}')
+
+    # create raw binary file containing the hyperspectral array values
+    with open(filename+'.raw', mode='w+b') as f:
+        f.write(spectral_data.array_data.transpose(0,2,1).tobytes(order='C'))
+

tests/plantcv/hyperspectral/test_write_data.py

@@ -0,0 +1,52 @@
+import os
+import pytest
+import numpy as np
+
+from plantcv.plantcv import Spectral_data
+from plantcv.plantcv.hyperspectral import read_data
+from plantcv.plantcv.hyperspectral import write_data
+
+def test_write_data_default(tmpdir):
+    """Test for PlantCV."""
+    rng = np.random.default_rng()
+
+    # Create a test tmp directory
+    cache_dir = tmpdir.mkdir("cache")
+
+    lines = 32
+    samples = 32
+    bands = 5
+
+    # Create random array data in the interval [0-65535] and wavelengths in the
+    # interval [400-1000)
+    rand_array = rng.integers(0, 65535, size=(lines, samples, bands), dtype=np.uint16, endpoint=True)
+    rand_wavelengths = np.sort(600.0*rng.random(size=bands) + 400.0)
+    # Create dictionary of wavelengths
+    wavelength_dict = {}
+    for j, wavelength in enumerate(rand_wavelengths):
+        wavelength_dict.update({wavelength: float(j)})
+
+    # Create spectral data object
+    rand_spectral_array = Spectral_data(array_data=rand_array,
+                                max_wavelength=rand_wavelengths[-1],
+                                min_wavelength=rand_wavelengths[0],
+                                max_value=float(np.amax(rand_array)),
+                                min_value=float(np.amin(rand_array)),
+                                d_type=rand_array.dtype,
+                                wavelength_dict=wavelength_dict,
+                                samples=samples,
+                                lines=lines,
+                                interleave='bil',
+                                wavelength_units='nm',
+                                array_type="datacube",
+                                pseudo_rgb=None,
+                                filename='random_hyperspectral_test',
+                                default_bands=None)
+
+
+    filename = os.path.join(cache_dir, 'plantcv_hyperspectral_write_data.raw')
+    write_data(filename=filename, spectral_data=rand_spectral_array)
+
+    # Read written hyperspectral image
+    array_data = read_data(filename=filename)
+    assert np.shape(array_data.array_data) == (lines, samples, bands)