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gdal new test
Browse files- app.py +1 -56
- requirements.txt +1 -0
app.py
CHANGED
@@ -4,6 +4,7 @@ from io import BytesIO, TextIOWrapper
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import numpy as np
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from deepdespeckling.despeckling import get_denoiser, get_model_weights_path
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from deepdespeckling.utils.constants import PATCH_SIZE, STRIDE_SIZE
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st.set_page_config(layout="wide", page_title="Deepdespeckling")
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@@ -15,62 +16,6 @@ st.write(
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st.sidebar.write("## Upload and download :gear:")
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def cos2mat(path_to_cosar_image: str) -> np.array:
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"""Convert a CoSAR imge to a numpy array of size [ncolumns,nlines,2]
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Args:
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path_to_cosar_image (str): path to the image which is a cos file
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Returns:
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numpy array : the image in a numpy array
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"""
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print('Converting CoSAR to numpy array of size [ncolumns,nlines,2]')
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try:
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fin = open(path_to_cosar_image, 'rb')
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except IOError:
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legx = path_to_cosar_image + ': it is a not openable file'
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print(legx)
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print(u'failed to call cos2mat')
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return 0, 0, 0, 0
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ibib = struct.unpack(">i", fin.read(4))[0]
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irsri = struct.unpack(">i", fin.read(4))[0]
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irs = struct.unpack(">i", fin.read(4))[0]
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ias = struct.unpack(">i", fin.read(4))[0]
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ibi = struct.unpack(">i", fin.read(4))[0]
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irtnb = struct.unpack(">i", fin.read(4))[0]
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itnl = struct.unpack(">i", fin.read(4))[0]
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nlig = struct.unpack(">i", fin.read(4))[0]
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ncoltot = int(irtnb / 4)
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ncol = ncoltot - 2
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nlig = ias
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print(u'Reading image in CoSAR format. ncolumns=%d nlines=%d' % (ncol, nlig))
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firm = np.zeros(4 * ncoltot, dtype=np.byte())
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imgcxs = np.empty([nlig, ncol], dtype=np.complex64())
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fin.seek(0)
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firm = fin.read(4 * ncoltot)
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firm = fin.read(4 * ncoltot)
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firm = fin.read(4 * ncoltot)
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firm = fin.read(4 * ncoltot)
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for iut in range(nlig):
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firm = fin.read(4 * ncoltot)
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imgligne = np.ndarray(2 * ncoltot, '>h', firm)
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imgcxs[iut, :] = imgligne[4:2 * ncoltot:2] + \
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1j * imgligne[5:2 * ncoltot:2]
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print('[:,:,0] contains the real part of the SLC image data')
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print('[:,:,1] contains the imaginary part of the SLC image data')
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return np.stack((np.real(imgcxs), np.imag(imgcxs)), axis=2)
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def convert_image(img):
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buf = BytesIO()
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img.save(buf, format="PNG")
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import numpy as np
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from deepdespeckling.despeckling import get_denoiser, get_model_weights_path
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from deepdespeckling.utils.load_cosar import cos2mat
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from deepdespeckling.utils.constants import PATCH_SIZE, STRIDE_SIZE
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st.set_page_config(layout="wide", page_title="Deepdespeckling")
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st.sidebar.write("## Upload and download :gear:")
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def convert_image(img):
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buf = BytesIO()
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img.save(buf, format="PNG")
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requirements.txt
CHANGED
@@ -1,3 +1,4 @@
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deepdespeckling
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pillow
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numpy
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deepdespeckling
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pillow
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numpy
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GDAL
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