For Matplotlib plots in iPython/Jupyter you can make the notebook plot plots inline with
%matplotlib inline
How can one do the same for NLTK draw() for trees? Here is the documentation http://www.nltk.org/api/nltk.draw.html
Based on this answer:
import os
from IPython.display import Image, display
from nltk.draw import TreeWidget
from nltk.draw.util import CanvasFrame
def jupyter_draw_nltk_tree(tree):
cf = CanvasFrame()
tc = TreeWidget(cf.canvas(), tree)
tc['node_font'] = 'arial 13 bold'
tc['leaf_font'] = 'arial 14'
tc['node_color'] = '#005990'
tc['leaf_color'] = '#3F8F57'
tc['line_color'] = '#175252'
cf.add_widget(tc, 10, 10)
cf.print_to_file('tmp_tree_output.ps')
cf.destroy()
os.system('convert tmp_tree_output.ps tmp_tree_output.png')
display(Image(filename='tmp_tree_output.png'))
os.system('rm tmp_tree_output.ps tmp_tree_output.png')
Little slow, but does the job. If you're doing it remotely, don't forget to run your ssh session with -X key (like ssh -X user#server.com) so that Tk could initialize itself (no display name and no $DISPLAY environment variable-kind of error)
UPD: it seems like last versions of jupyter and nltk work nicely together, so you can just do IPython.core.display.display(tree) to get a nice-looking tree-render embedded into the output.
2019 Update:
This runs on Jupyter Notebook:
from nltk.tree import Tree
from IPython.display import display
tree = Tree.fromstring('(S (NP this tree) (VP (V is) (AdjP pretty)))')
display(tree)
Requirements:
NLTK
Ghostscript
Related
I have not solved the following problem (Python 3.9.13, Win10).
import cv2
import numpy as np
import matplotlib.pyplot as plt
img = cv2.imread('cat.jpg,', 3)
print(img)
cv.imshow('cat', img)
plt.imshow(img)
plt.show()
print(img) was None
The size of cat.jpg is 372Kb.
There is cat.jpg which located at the same level of python code1.py.
Also, I installed opencv-python-headless.
Then, I ran it. Following waning appeared;
[ WARN:0#0.758].......oadsave.cpp (239) cv::findDecoder imread_('cat.jpg'):
can't open/read file: check file path/integrity
I am using tensorRT to perform inference with CUDA. I'd like to use CuPy to preprocess some images that I'll feed to the tensorRT engine. The preprocessing function, called my_function, works fine as long as tensorRT is not run between different calls of the my_function method (see code below). Specifically, the issue is not strictly related by tensorRT but by the fact that tensorRT inference requires to be wrapped by push and pop operations of the pycuda context.
With respect to the following code, the last execution of my_function will raise the following error:
File "/home/ubuntu/myfile.py", line 188, in _pre_process_cuda
img = ndimage.zoom(img, scaling_factor)
File "/home/ubuntu/.local/lib/python3.6/site-packages/cupyx/scipy/ndimage/interpolation.py", line 482, in zoom
kern(input, zoom, output)
File "cupy/core/_kernel.pyx", line 822, in cupy.core._kernel.ElementwiseKernel.__call__
File "cupy/cuda/function.pyx", line 196, in cupy.cuda.function.Function.linear_launch
File "cupy/cuda/function.pyx", line 164, in cupy.cuda.function._launch
File "cupy_backends/cuda/api/driver.pyx", line 299, in cupy_backends.cuda.api.driver.launchKernel
File "cupy_backends/cuda/api/driver.pyx", line 124, in cupy_backends.cuda.api.driver.check_status
cupy_backends.cuda.api.driver.CUDADriverError: CUDA_ERROR_INVALID_HANDLE: invalid resource handle
Note: in the following code I haven't reported the entire tensorRT inference code. In fact, simply pushing and popping a pycuda context generates the error
Code:
import numpy as np
import cv2
import time
from PIL import Image
import requests
from io import BytesIO
from matplotlib import pyplot as plt
import cupy as cp
from cupyx.scipy import ndimage
import tensorrt as trt
import pycuda.driver as cuda
import pycuda.autoinit
def my_function(numpy_frame):
dtype = 'float32'
img = cp.array(numpy_frame, dtype='float32')
# print(img)
img = ndimage.zoom(img, (0.5, 0.5, 3))
img = (cp.array(2, dtype=dtype) / cp.array(255, dtype=dtype)) * img - cp.array(1, dtype=dtype)
img = img.transpose((2, 0, 1))
img = img.ravel()
return img
# load image
url = "https://www.pexels.com/photo/109919/download/?search_query=&tracking_id=411xe21veam"
response = requests.get(url)
img = Image.open(BytesIO(response.content))
img = np.array(img)
# initialize tensorrt
TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
trt_runtime = trt.Runtime(TRT_LOGGER)
cfx = cuda.Device(0).make_context()
my_function(img) # ok
my_function(img) # ok
# ----- TENSORRT ---------
cfx.push()
# .... tensorrt inference....
cfx.pop()
# ----- TENSORRT ---------
my_function(img) # <---- error
I even tried to do it other ways, but unfortunately with the same result:
cfx.push()
my_function(img) # ok
cfx.pop()
cfx.push()
my_function(img) # error
cfx.pop()
#admin: if you can think of a better name for this question feel free to edit it :)
There were multiple contexts open. For instance, it seems that all of the following open a context:
import pycuda.autoinit
cfx.cuda.Device(0).make_context()
cfx.push()
So if you run the three command above, then simply running one cfx.pop() won't be enough. You will need to run cfx.pop() three times to pop all the contexts.
Hi i want to overlay or paste an image on bigger images(have a folder containinf 10 images and want to overlay the smaller images on all 10) and save them in a different folder. I did try somethings but ran into errors.
import scipy.misc
import numpy as np
import os
import cv2
outPath = "C:\darkflow\Augmented Images\augmented_images\.."
cov = cv2.imread("C:\darkflow\Augmented Images\extracted\cover\extracted_cover.jpg")
bgs = [cv2.imread(file) for file in glob.glob("C:\darkflow\Augmented Images\images\*.jpg")]
for bg in bgs:
bg[y_offset:y_offset+s_img.shape[0], x_offset:x_offset+s_img.shape[1]] = cov
f_image = cv2.cvtColor(bg, cv2.COLOR_BGR2RGB)
fullpath = os.path.join(outPath, 'augmented_'+ bg)
misc.imsave(fullpath, f_image)
with this code i get an error : ufunc 'add' did not contain a loop with signature matching types dtype('
I found the answer while looking into the code. My code is
from scipy import ndimage, misc
import scipy.misc
import numpy as np
import os
import cv2
cov = cv2.imread("C:\darkflow\Augmented Images\extracted\cover\extracted_cover.jpg")
bgs = [cv2.imread(file) for file in glob.glob("C:\darkflow\Augmented Images\images\*.jpg")]
d=1
x_offset=100
y_offset= 100
for bg in bgs:
bg[y_offset:y_offset+ cov.shape[0], x_offset:x_offset+ cov.shape[1]] = cov
filename = "images/file_%d.jpg"%d
cv2.imwrite(filename, bg)
d+=1
Anybody tried to use numba in google collaboratory? I just can not figure out how to set it up in this environment.
At the moment, I'm stuck with the error library nvvm not found.
Copy this code into cell. It works for me.
!apt-get install nvidia-cuda-toolkit
!pip3 install numba
import os
os.environ['NUMBAPRO_LIBDEVICE'] = "/usr/lib/nvidia-cuda-toolkit/libdevice"
os.environ['NUMBAPRO_NVVM'] = "/usr/lib/x86_64-linux-gnu/libnvvm.so"
from numba import cuda
import numpy as np
import time
#cuda.jit
def hello(data):
data[cuda.blockIdx.x, cuda.threadIdx.x] = cuda.blockIdx.x
numBlocks = 5
threadsPerBlock = 10
data = np.ones((numBlocks, threadsPerBlock), dtype=np.uint8)
hello[numBlocks, threadsPerBlock](data)
print(data)
I didn't have to install the packages #Algis suggested, but the paths to the drivers were different. So I had to do the following.
First determine the correct paths for the drivers
!find / -iname 'libdevice'
!find / -iname 'libnvvm.so'
# Output:
# /usr/local/cuda-9.2/nvvm/lib64/libnvvm.so
# /usr/local/cuda-9.2/nvvm/libdevice
Then set the paths as #Algis described
import os
os.environ['NUMBAPRO_LIBDEVICE'] = "/usr/local/cuda-9.2/nvvm/libdevice"
os.environ['NUMBAPRO_NVVM'] = "/usr/local/cuda-9.2/nvvm/lib64/libnvvm.so"
You can do #Stan's work in one simple sweep if you have this block at the beginning of your colab notebook (this also automatically updates as CUDA gets updated)
import os
dev_lib_path = !find / -iname 'libdevice'
nvvm_lib_path = !find / -iname 'libnvvm.so'
assert len(dev_lib_path)>0, "Device Lib Missing"
assert len(nvvm_lib_path)>0, "NVVM Missing"
os.environ['NUMBAPRO_LIBDEVICE'] = dev_lib_path[0]
os.environ['NUMBAPRO_NVVM'] = nvvm_lib_path[0]
I literally copy and pasted the example of how to use the hover tool from bokeh's documentation and I still can't get this damn thing to work. I just want bokeh's hover tool to display the x and y coordinates. I think I've implemented it correctly but let me know if anything's wrong. (The ASCII file reads in flawlessly and the graph plots correctly and all the other tools work)
from bokeh.plotting import *
from bokeh.objects import HoverTool
from collections import OrderedDict
output_notebook()
%matplotlib inline
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import mpld3
from mpld3 import plugins, utils
mpld3.enable_notebook()
from pylab import *
import pandas as pd
chip1 = pd.io.parsers.read_table("Chip1_Buffer_ASCII", sep=";")
chip2 = pd.io.parsers.read_table("Chip2_Buffer_ASCII", sep=";")
chip3 = pd.io.parsers.read_table("Chip3_Buffer_ASCII", sep=";")
chip1_1=chip1
chip1_2=chip2
chip1_3=chip3
chip1_1["Frequency (Hz)"]=chip1["Frequency (Hz)"].map(lambda x: math.log10(x))
chip1_2["Frequency (Hz)"]=chip2["Frequency (Hz)"].map(lambda x: math.log10(x))
chip1_3["Frequency (Hz)"]=chip3["Frequency (Hz)"].map(lambda x: math.log10(x))
diff_1_2 = chip1 - chip2
diff_1_2["Frequency (Hz)"] = chip1_1["Frequency (Hz)"]
source1 = ColumnDataSource(chip1_1.to_dict("list"))
source2 = ColumnDataSource(chip1_2.to_dict("list"))
source3 = ColumnDataSource(chip1_3.to_dict("list"))
source4=ColumnDataSource(diff_1_2.to_dict("list"))
import bokeh.plotting as bk
bk.figure(plot_width=600, # in units of px
plot_height=600,
title="Hello World!",
tools="pan,wheel_zoom,box_zoom,select,reset,hover")
bk.hold()
bk.line("Frequency (Hz)", "-Phase (°)",line_width=2,source=source1,logx=True,color="red",xlim=[0, 10000])
bk.line("Frequency (Hz)", "-Phase (°)",line_width=2,source=source2,logx=True,color="green",xlim=[0, 10000])
bk.line("Frequency (Hz)", "-Phase (°)",line_width=2,source=source3,logx=True,color="orange",xlim=[0, 10000])
bk.line("Frequency (Hz)", "-Phase (°)",line_width=2,source=source4,logx=True,color="orange",xlim=[0, 10000])
hover = bk.curplot().select(dict(type=HoverTool))
hover.tooltips=OrderedDict([
("(x,y)", "($x, $y)"),
("index", "$index")
])
bk.show()