use local cos2mat function

app.py

@@ -5,8 +5,6 @@ import numpy as np
 
 from deepdespeckling.despeckling import get_denoiser, get_model_weights_path
 from deepdespeckling.utils.constants import PATCH_SIZE, STRIDE_SIZE
-from deepdespeckling.utils.load_cosar import cos2mat
-from deepdespeckling.utils.utils import load_sar_image
 
 st.set_page_config(layout="wide", page_title="Deepdespeckling")
 
@@ -16,7 +14,61 @@ st.write(
 )
 st.sidebar.write("## Upload and download :gear:")
 
-# Download the fixed image
+
+def cos2mat(path_to_cosar_image: str) -> np.array:
+    """Convert a CoSAR imge to a numpy array of size [ncolumns,nlines,2]
+
+    Args:
+        path_to_cosar_image (str): path to the image which is a cos file
+
+    Returns:
+        numpy array : the image in a numpy array
+    """
+
+    print('Converting CoSAR to numpy array of size [ncolumns,nlines,2]')
+
+    try:
+        fin = open(path_to_cosar_image, 'rb')
+    except IOError:
+        legx = path_to_cosar_image + ': it is a not openable file'
+        print(legx)
+        print(u'failed to call cos2mat')
+        return 0, 0, 0, 0
+
+    ibib = struct.unpack(">i", fin.read(4))[0]
+    irsri = struct.unpack(">i", fin.read(4))[0]
+    irs = struct.unpack(">i", fin.read(4))[0]
+    ias = struct.unpack(">i", fin.read(4))[0]
+    ibi = struct.unpack(">i", fin.read(4))[0]
+    irtnb = struct.unpack(">i", fin.read(4))[0]
+    itnl = struct.unpack(">i", fin.read(4))[0]
+
+    nlig = struct.unpack(">i", fin.read(4))[0]
+    ncoltot = int(irtnb / 4)
+    ncol = ncoltot - 2
+    nlig = ias
+
+    print(u'Reading image in CoSAR format.  ncolumns=%d  nlines=%d' % (ncol, nlig))
+
+    firm = np.zeros(4 * ncoltot, dtype=np.byte())
+    imgcxs = np.empty([nlig, ncol], dtype=np.complex64())
+
+    fin.seek(0)
+    firm = fin.read(4 * ncoltot)
+    firm = fin.read(4 * ncoltot)
+    firm = fin.read(4 * ncoltot)
+    firm = fin.read(4 * ncoltot)
+
+    for iut in range(nlig):
+        firm = fin.read(4 * ncoltot)
+        imgligne = np.ndarray(2 * ncoltot, '>h', firm)
+        imgcxs[iut, :] = imgligne[4:2 * ncoltot:2] + \
+            1j * imgligne[5:2 * ncoltot:2]
+
+    print('[:,:,0] contains the real part of the SLC image data')
+    print('[:,:,1] contains the imaginary part of the SLC image data')
+
+    return np.stack((np.real(imgcxs), np.imag(imgcxs)), axis=2)
 
 
 def convert_image(img):

requirements.txt

@@ -1,7 +1,3 @@
 deepdespeckling
 pillow
 numpy
-geopandas
-osgeo
-./wheelhouse/GDAL-3.4.1-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.whl
-

wheelhouse/GDAL-3.4.1-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.whl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2da982c14b04a6a5181f8ea423cff3081f7447cccb861e27b02dbdc59e1d3ee4
-size 76026316