I am trying to modify existing metadata within python 3. More specifically I have GPS coordinates and altitude in a my metadata, and I need to modify it.
I'm using piexif mudule, and I ancounter two problems.
First, I managed to change Altitude, using
exif_dict['GPS'][piexif.GPSIFD.GPSAltitude] = (140, 1)
and it works.
But I can't understand how to change Latitude and Longtitude? as they consist of three fields, like ((53, 1), (291191, 10000), (0, 1)).
The second problem occurs when I try to save tiff file with modified metadata. If I save it as TIFF file:
img.save(fname_2, 'tiff', exif=exif_bytes),
the fname_2 file is created, but it's metadata isn't changed. If Isave as JPEG -
img.save(fname_2, 'jpeg', exif=exif_bytes)
- metadata changes, but the file is compressed from 289 MB to 15 MB, that makes it impossible to use it for my purposes.
Has anyone managed to do this? It sounds like it would be very simple, but I can't seem to work it out.
import piexif
from PIL import Image
Image.MAX_IMAGE_PIXELS = 1000000000
fname_1='D:\EZG\Codding\photo\iiq/eee.tif'
fname_2='D:\EZG\Codding\photo\iiq/eee_change.tif'
img = Image.open(fname_1)
exif_dict = piexif.load(fname_1)
latitide = exif_dict['GPS'][piexif.GPSIFD.GPSLatitude]
longtitude = exif_dict['GPS'][piexif.GPSIFD.GPSLongitude]
altitude = exif_dict['GPS'][piexif.GPSIFD.GPSAltitude]
print(latitide)
print(longtitude)
print(altitude)
exif_dict['GPS'][piexif.GPSIFD.GPSAltitude] = (140, 1)
exif_bytes = piexif.dump(exif_dict)
img.save(fname_2, 'tiff', exif=exif_bytes)
the fname_2 file is created, but it's metadata isn't changed
Based on other questions and answers on SO it seems that the values are encoded as fractions:
((53, 1), (291191, 10000), (0, 1))
is 53 degrees 291191/10000 = 29.1191 minutes North (0 == N; 1 == S)
You may also want to check this answer, as there is a better package to edit GPS coordinates in photo metadata.
Related
Problem Statement: I have a pdf which contains n number of pages and each page has 1 image whose text I need to read and perform some operation.
What I tried: I have to do this in python, and the only library I found with the best result is pytesserac.
I am pasting the sample code which I tried
fn = kw['fn'] = self.env.context.get('wfg_pg', kw['fn'])
zoom, zoom_config = self.get_zoom_for_doc(index), ' -c tessedit_do_invert=0'
if 3.3 < zoom < 3.5:
zoom_config += ' --oem 3 --psm 4'
elif 0 != page_number_list[0]:
zoom_config += ' --psm 6'
full_text, page_length = '', kw['doc'].pageCount
if recursion and index >= 10:
return fn.get('most_correct') or fn.get(page_number_list[0])
mat = fitz.Matrix(zoom, zoom) # increase resolution
for page_no in page_number_list:
page = kw['doc'].loadPage(page_no) # number of page
pix = page.getPixmap(matrix=mat)
with Image.open(io.BytesIO(pix.getImageData())) as img:
text_of_each_page = str(pytesseract.image_to_string(img, config='%s' % zoom_config)).strip()
fn[page_no] = text_of_each_page
full_text = '\n'.join((full_text, text_of_each_page, '\n'))
_logger.critical(f"full text in load immage {full_text}")
args = (full_text, page_number_list)
load = recursion and self.run_recursion_to_load_new_image_to_text(*args, **kw)
if recursion and load:
return self.load_image
return full_text
The issue: My pdf is having dates like 1/13, 1/7 the library is reading them as 143, 1n and in some places, it is reading 17 as 1). Also after the text, it is also giving some symbols like { & . , = randomly whereas in pdf these things are not even there.
For accuracy
1. I tried converting the image to .tiff format but it didn't work for me.
2. Tried adjusting the resolution of the image.
You can use pdftoppm tool for converting you images really fast as it provides you to use multi-threading feature by just passing thread_count=(no of threads).
You can refer to this link for more info on this tool. Also better images can increase the accuracy of tesseract.
I would like to reproject a raster and keep working on that reprojected raster instead of loading it again from a file.
To project a raster I use either gdal:
# Source
src = gdal.Open(vv_path, gdalconst.GA_ReadOnly)
src_proj = src.GetProjection()
src_geotrans = src.GetGeoTransform()
# We want a section of source that matches this:
match_ds = gdal.Open(sn2_red_path, gdalconst.GA_ReadOnly)
match_proj = match_ds.GetProjection()
match_geotrans = match_ds.GetGeoTransform()
wide = match_ds.RasterXSize
high = match_ds.RasterYSize
# Output / destination
dst_filename = os.path.join(sn1_processed_path,'vv.tif')
dst = gdal.GetDriverByName('Gtiff').Create(dst_filename, wide, high, 1, gdalconst.GDT_Float32)
dst.SetGeoTransform( match_geotrans )
dst.SetProjection( match_proj)
# Do the work
aa = gdal.ReprojectImage(src, dst, src_proj, match_proj, gdalconst.GRA_NearestNeighbour)
del dst # Flush
or rasterio from here
In both cases, the projected raster is saved to a file, and I have to load it again to procees it. Is it possible to save the projected raster also as a variable?
You could use VRT datasets:
src = gdal.Open(“reference.tif”)
dst = gdal.Warp(“warped.vrt”, src, format=“vrt”, dstSRS=“EPSG:3857”)
This way only a small VRT file will be created, and you can use the dst dataset in downstream processing at which point the warping will be actually performed.
You can even create the VRT itself in memory, so nothing is written to disk at all:
dst = gdal.Warp(“”, src, format=“vrt”, dstSRS=“EPSG:3857”)
If your dataset fits entirely in memory, you can create the actual dataset in memory using the vsimem virtual file system driver, which has the upside that you have to perform the processing only once if you want to use it downstream in multiple functions:
dst = gdal.Warp(“/vsimem/result_inmemory.tif”, src, format=“tif”, dstSRS=“EPSG:3857”)
This way the processing will be performed immediately, but then you can use the dataset object to e.g. write it to disk and then perform additional processing.
While trying to download sentinel image for a specific location, the tif file is generated by default in drive but its not readable by openCV or PIL.Image().Below is the code for the same. If I use the file format as tfrecord. There are no Images downloaded in the drive.
starting_time = '2018-12-15'
delta = 15
L = -96.98
B = 28.78
R = -97.02
T = 28.74
cordinates = [L,B,R,T]
my_scale = 30
fname = 'sinton_texas_30'
llx = cordinates[0]
lly = cordinates[1]
urx = cordinates[2]
ury = cordinates[3]
geometry = [[llx,lly], [llx,ury], [urx,ury], [urx,lly]]
tstart = datetime.datetime.strptime(starting_time, '%Y-%m-%d') tend =
tstart+datetime.timedelta(days=delta)
collSent = ee.ImageCollection('COPERNICUS/S2').filterDate(str(tstart).split('')[0], str(tend).split(' ')[0]).filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20)).map(mask2clouds)
medianSent = ee.Image(collSent.reduce(ee.Reducer.median())) cropLand = ee.ImageCollection('USDA/NASS/CDL').filterDate('2017-01-01','2017-12-31').first()
task_config = {
'scale': my_scale,
'region': geometry,
'fileFormat':'TFRecord'
}
f1 = medianSent.select(['B1_median','B2_median','B3_median'])
taskSent = ee.batch.Export.image(f1,fname+"_Sent",task_config)
taskSent.start()
I expect the output to be readable in python so I can covert into numpy. In case of file format 'tfrecord', I expect the file to be downloaded in my drive.
I think you should think about the following things:
File format
If you want to open your file with PIL or OpenCV, and not with TensorFlow, you would rather use GeoTIFF. Try with this format and see if things are improved.
Saving to drive
Normally saving to your Drive is the default behavior. However, you can try to force writing to your drive:
ee.batch.Export.image.toDrive(image=f1, ...)
You can further try to setup a folder, where the images should be sent to:
ee.batch.Export.image.toDrive(image=f1, folder='foo', ...)
In addition, the Export data help page and this tutorial are good starting points for further research.
personally pretty new to programming and I am trying to save a high mp Image from an IDS camera using the pyueye module with python.
my Code works to save the Image, but the Problem is it saves the Image as a 1280x720 Image inside a 4192x3104
I have no idea why its saving the small Image inside the larger file and am asking if anyone knows what i am doing wrong and how can I fix it so the Image is the whole 4192x3104
from pyueye import ueye
import ctypes
hcam = ueye.HIDS(0)
pccmem = ueye.c_mem_p()
memID = ueye.c_int()
hWnd = ctypes.c_voidp()
ueye.is_InitCamera(hcam, hWnd)
ueye.is_SetDisplayMode(hcam, 0)
sensorinfo = ueye.SENSORINFO()
ueye.is_GetSensorInfo(hcam, sensorinfo)
ueye.is_AllocImageMem(hcam, sensorinfo.nMaxWidth, sensorinfo.nMaxHeight,24, pccmem, memID)
ueye.is_SetImageMem(hcam, pccmem, memID)
ueye.is_SetDisplayPos(hcam, 100, 100)
nret = ueye.is_FreezeVideo(hcam, ueye.IS_WAIT)
print(nret)
FileParams = ueye.IMAGE_FILE_PARAMS()
FileParams.pwchFileName = "python-test-image.bmp"
FileParams.nFileType = ueye.IS_IMG_BMP
FileParams.ppcImageMem = None
FileParams.pnImageID = None
nret = ueye.is_ImageFile(hcam, ueye.IS_IMAGE_FILE_CMD_SAVE, FileParams, ueye.sizeof(FileParams))
print(nret)
ueye.is_FreeImageMem(hcam, pccmem, memID)
ueye.is_ExitCamera(hcam)
The size of the image depends on the sensor size of the camera.By printing sensorinfo.nMaxWidth and sensorinfo.nMaxHeight you will get the maximum size of the image which the camera captures. I think that it depends on the model of the camera. For me it is 2056x1542.
Could you please elaborate on the last sentence of the question.
I'm trying to make tensorflow mfcc give me the same results as python lybrosa mfcc
i have tried to match all the default parameters that are used by librosa
in my tensorflow code and got a different result
this is the tensorflow code that i have used :
waveform = contrib_audio.decode_wav(
audio_binary,
desired_channels=1,
desired_samples=sample_rate,
name='decoded_sample_data')
sample_rate = 16000
transwav = tf.transpose(waveform[0])
stfts = tf.contrib.signal.stft(transwav,
frame_length=2048,
frame_step=512,
fft_length=2048,
window_fn=functools.partial(tf.contrib.signal.hann_window,
periodic=False),
pad_end=True)
spectrograms = tf.abs(stfts)
num_spectrogram_bins = stfts.shape[-1].value
lower_edge_hertz, upper_edge_hertz, num_mel_bins = 0.0,8000.0, 128
linear_to_mel_weight_matrix =
tf.contrib.signal.linear_to_mel_weight_matrix(
num_mel_bins, num_spectrogram_bins, sample_rate, lower_edge_hertz,
upper_edge_hertz)
mel_spectrograms = tf.tensordot(
spectrograms,
linear_to_mel_weight_matrix, 1)
mel_spectrograms.set_shape(spectrograms.shape[:-1].concatenate(
linear_to_mel_weight_matrix.shape[-1:]))
log_mel_spectrograms = tf.log(mel_spectrograms + 1e-6)
mfccs = tf.contrib.signal.mfccs_from_log_mel_spectrograms(
log_mel_spectrograms)[..., :20]
the equivalent in librosa:
libr_mfcc = librosa.feature.mfcc(wav, 16000)
the following are the graphs of the results:
I'm the author of tf.signal. Sorry for not seeing this post sooner, but you can get librosa and tf.signal.stft to match if you center-pad the signal before passing it to tf.signal.stft. See this GitHub issue for more details.
I spent a whole 1 day trying to make them match. Even the rryan's solution didn't work for me (center=False in librosa), but I finally found out, that TF and librosa STFT's match only for the case win_length==n_fft in librosa and frame_length==fft_length in TF. That's why rryan's colab example is working, but you can try that if you set frame_length!=fft_length, the amplitudes are very different (although visually, after plotting, the patterns look similar). Typical example - if you choose some win_length/frame_length and then you want to set n_fft/fft_length to the smallest power of 2 greater than win_length/frame_length, then the results will be different. So you need to stick with the inefficient FFT given by your window size... I don't know why it is so, but that's how it is, hopefully it will be helpful for someone.
The output of contrib_audio.decode_wav should be DecodeWav with { audio, sample_rate } and audio shape is (sample_rate, 1), so what is the purpose for getting first item of waveform and do transpose?
transwav = tf.transpose(waveform[0])
No straight forward way, since librosa stft uses center=True which does not comply with tf stft.
Had it been center=False, stft tf/librosa would give near enough results. see colab sniff
But even though, trying to import the librosa code into tf is a big headache. Here is what I started and gave up. Near but not near enough.
def pow2db_tf(X):
amin=1e-10
top_db=80.0
ref_value = 1.0
log10 = 2.302585092994046
log_spec = (10.0/log10) * tf.log(tf.maximum(amin, X))
log_spec -= (10.0/log10) * tf.log(tf.maximum(amin, ref_value))
pow2db = tf.maximum(log_spec, tf.reduce_max(log_spec) - top_db)
return pow2db
def librosa_feature_like_tf(x, sr=16000, n_fft=2048, n_mfcc=20):
mel_basis = librosa.filters.mel(sr, n_fft).astype(np.float32)
mel_basis = mel_basis.reshape(1, int(n_fft/2+1), -1)
tf_stft = tf.contrib.signal.stft(x, frame_length=n_fft, frame_step=hop_length, fft_length=n_fft)
print ("tf_stft", tf_stft.shape)
tf_S = tf.matmul(tf.abs(tf_stft), mel_basis);
print ("tf_S", tf_S.shape)
tfdct = tf.spectral.dct(pow2db_tf(tf_S), norm='ortho'); print ("tfdct", tfdct.shape)
print ("tfdct before cut", tfdct.shape)
tfdct = tfdct[:,:,:n_mfcc];
print ("tfdct afer cut", tfdct.shape)
#tfdct = tf.transpose(tfdct,[0,2,1]);print ("tfdct afer traspose", tfdct.shape)
return tfdct
x = tf.placeholder(tf.float32, shape=[None, 16000], name ='x')
tf_feature = librosa_feature_like_tf(x)
print("tf_feature", tf_feature.shape)
mfcc_rosa = librosa.feature.mfcc(wav, sr).T
print("mfcc_rosa", mfcc_rosa.shape)
For anyone still looking for this: I had a similar problem some time ago: Matching librosa's mel filterbanks/mel spectrogram to a tensorflow implementation. The solution was to use a different windowing approach for the spectrogram and librosa's mel matrix as constant tensor. See here and here.