I am trying to generate random images of text and store them as image files in my computer so that I can use them to train a model later. But I don't know how make sure all the characters falls within the image boundaries. When I plot them out in python they always show, but if I looked at the saved image, some times the strings are cut. Also, I want to automate the process instead of plotting each out to check.
Furthermore, setting bbox_inches='tight' changes the image size, while I want to be able to specify the image size.
This is what I have tried so far
import matplotlib.pyplot as plt
import numpy as np
dpi = 100
h, w = 50, 100
plt.figure(figsize=(w / dpi, h / dpi), dpi=dpi)# so I will get w columns and h rows
text = str(np.random.uniform(100000, 1000000))# my string will always only be 6 characters
x = np.random.uniform(0, .3)# random positions
y = np.random.uniform(0, .5)
size = np.random.uniform(16, 23)# random text size
plt.text(x, y, text, fontdict={'size': size})
plt.axis('off')
plt.savefig(text + '.jpg'))
I figured a way to get around this. .get_window_extent() can help locate the edges of the text. Since I just want to generate random images, I can drop the image and generate the next one if the text it out of bounds. For non-random text, I suppose one can also use it to determine which way to shift text if it goes out of bounds.
Here is a sample solution with my random text case:
import matplotlib.pyplot as plt
import numpy as np
dpi = 100
w = 120 # number of columns
h = 50 # number of rows
N = 100 # number of random images to generate
count = 0
while count < N:
fig = plt.figure(frameon=False, dpi=dpi)
fig.set_size_inches(w / dpi, h / dpi)
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
number = str(np.random.randint(100000, 1000000))# random text
x = np.random.uniform(0, .1)# random position
y = np.random.uniform(0, .5)
size = np.random.uniform(w / dpi * 72 / 6, w / dpi * 72 / 3.3)
text = ax.text(x, y, number, fontdict={'size': size})
bbox = text.get_window_extent(fig.canvas.get_renderer())# !!! get the extent of the text
if (bbox.x1 < w) & (bbox.y1 < h):# !!! make sure the extent is within bounds before save
plt.savefig(f'{number}.jpg'), pad_inches=0, dpi=dpi)
count += 1
plt.close()# remember to close else bad for memory(?)
Hello I am beginner in OpenCv.
I have a maze image. I wrote maze solver code. I need to get the photo like the picture for this code to work.
I want to choose the contours of the white area using ROI but I could not
When I try the ROI method I get a smooth rectangle with a black area selected.
https://i.stack.imgur.com/Ty5BX.png -----> this is my code result
https://i.stack.imgur.com/S7zuJ.png --------> I want to this result
import cv2
import numpy as np
#import image
image = cv2.imread('rt4.png')
#grayscaleqq
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
#cv2.imshow('gray', gray)
#qcv2.waitKey(0)
#binary
#ret,thresh = cv2.threshold(gray,127,255,cv2.THRESH_BINARY_INV)
threshold = 150
thresh = cv2.threshold(gray, threshold, 255, cv2.THRESH_BINARY)[1]
cv2.namedWindow('second', cv2.WINDOW_NORMAL)
cv2.imshow('second', thresh)
cv2.waitKey(0)
cv2.destroyAllWindows()
#dilation
kernel = np.ones((1,1), np.uint8)
img_dilation = cv2.dilate(thresh, kernel, iterations=1)
cv2.namedWindow('dilated', cv2.WINDOW_NORMAL)
cv2.imshow('dilated', img_dilation)
cv2.waitKey(0)
cv2.destroyAllWindows()
#find contours
im2,ctrs, hier = cv2.findContours(img_dilation.copy(),
cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
#sort contours
sorted_ctrs = sorted(ctrs, key=lambda ctr: cv2.boundingRect(ctr)
[0])
list = []
for i, ctr in enumerate(sorted_ctrs):
# Get bounding box
x, y, w, h = cv2.boundingRect(ctr)
# Getting ROI
roi = image[y:y+h, x:x+w]
a = w-x
b = h-y
list.append((a,b,x,y,w,h))
# show ROI
#cv2.imshow('segment no:'+str(i),roi)
cv2.rectangle(image,(x,y),( x + w, y + h ),(0,255,0),2)
#cv2.waitKey(0)
if w > 15 and h > 15:
cv2.imwrite('home/Desktop/output/{}.png'.format(i), roi)
cv2.namedWindow('marked areas', cv2.WINDOW_NORMAL)
cv2.imshow('marked areas',image)
cv2.waitKey(0)
cv2.destroyAllWindows()
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
gray = np.float32(gray)
dst = cv2.cornerHarris(gray,2,3,0.04)
#result is dilated for marking the corners, not important
dst = cv2.dilate(dst,None)
image[dst>0.01*dst.max()]=[0,0,255]
cv2.imshow('dst',image)
if cv2.waitKey(0) & 0xff == 27:
cv2.destroyAllWindows()
list.sort()
print(list[len(list)-1])
I misunderstood your question earlier. So, I'm rewriting.
As #Silencer has already stated, you could use the drawContours method. You can do it as follows:
import cv2
import numpy as np
#import image
im = cv2.imread('Maze2.png')
gaus = cv2.GaussianBlur(im, (5, 5), 1)
# mask1 = cv2.dilate(gaus, np.ones((15, 15), np.uint8, 3))
mask2 = cv2.erode(gaus, np.ones((5, 5), np.uint8, 1))
imgray = cv2.cvtColor(mask2, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 127, 255, 0)
im2, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
maxArea1=0
maxI1=0
for i in range(len(contours)):
area = cv2.contourArea(contours[i])
epsilon = 0.01 * cv2.arcLength(contours[i], True)
approx = cv2.approxPolyDP(contours[i], epsilon, True)
if area > maxArea1 :
maxArea1 = area
print(maxArea1)
print(maxI1)
cv2.drawContours(im, contours, maxI1, (0,255,255), 3)
cv2.imshow("yay",im)
cv2.imshow("gray",imgray)
cv2.waitKey(0)
cv2.destroyAllWindows()
I used it on the following image:
And I got the right answer. You can add additional filters, or you could decrease the area using an ROI, to decrese the discrepancy, but it wasn't required
Hope it helps!
A simple solution to just draw a slanted rectangle would be to use cv2.polylines. Based on your result, I'm assuming you have the coordinates of the vertices of the area already, lets call them [x1,y1], [x2,y2], [x3,y3], [x4,y4]. The polylines function draws a line from vertex to vertex to create a closed polygon.
import cv2
import numpy as np
#List coordinates of vertices as an array
pts = np.array([[x1,y1],[x2,y2],[x3,y3],[x4,y4]], np.int32)
pts = pts.reshape((-1,1,2))
#Draw lines from vertex to vertex
cv2.polylines(image, [pts], True, (255,0,0))
I have a single-band binary image (consisting of only 0 and 1 pixel values) as shown in the figure below.
I have to convert all the black pixels inside the outer white boundaries into whites.
The black pixels outside the outer white boundaries should remain black.
How would you do it?
The code below yields the following result:
I've commented the code inline to explain what I've done.
from skimage import io, img_as_bool, measure, morphology
from scipy import ndimage
import numpy as np
import matplotlib.pyplot as plt
# Read the image, convert the values to True or False;
# discard all but the red channel (since it's a black and
# white image, they're all the same)
image = img_as_bool(io.imread('borders.png'))[..., 0]
# Compute connected regions in the image; we're going to use this
# to find centroids for our watershed segmentation
labels = measure.label(image)
regions = measure.regionprops(labels)
# Marker locations for the watershed segmentation; we choose these to
# be the centroids of the different connected regions in the image
markers = np.array([r.centroid for r in regions]).astype(np.uint16)
marker_image = np.zeros_like(image, dtype=np.int64)
marker_image[markers[:, 0], markers[:, 1]] = np.arange(len(markers)) + 1
# Compute the distance map, which provides a "landscape" for our watershed
# segmentation
distance_map = ndimage.distance_transform_edt(1 - image)
# Compute the watershed segmentation; it will over-segment the image
filled = morphology.watershed(1 - distance_map, markers=marker_image)
# In the over-segmented image, combine touching regions
filled_connected = measure.label(filled != 1, background=0) + 1
# In this optional step, filter out all regions that are < 25% the size
# of the mean region area found
filled_regions = measure.regionprops(filled_connected)
mean_area = np.mean([r.area for r in filled_regions])
filled_filtered = filled_connected.copy()
for r in filled_regions:
if r.area < 0.25 * mean_area:
coords = np.array(r.coords).astype(int)
filled_filtered[coords[:, 0], coords[:, 1]] = 0
# And display!
f, (ax0, ax1, ax2) = plt.subplots(1, 3)
ax0.imshow(image, cmap='gray')
ax1.imshow(filled_filtered, cmap='spectral')
ax2.imshow(distance_map, cmap='gray')
plt.savefig('/tmp/labeled_filled_regions.png', bbox_inches='tight')
I have a masked array which is used by matplotlib.plt.contourf to project a temperature contour on a glabal map. I was trying to smooth the contour, but unfortunately none of the proposed solutions seems to be able to handle masked array. I tested these solutions:
-scipy.ndimage.gaussian_filter - moving averages
scipy.ndimage.zoom
none of them works(they count in the masked values also). Is there any way I can smooth my contour on maskedArray
I have added this part after trying the proposed 'inpaint' solution and the results were unchanged. here is the code (if it helps)
import Scientific.IO.NetCDF as S
import mpl_toolkits.basemap as bm
import numpy.ma as MA
import numpy as np
import matplotlib.pyplot as plt
import inpaint
def main():
fileobj = S.NetCDFFile('Bias.ANN.tas_A1_1.nc', mode='r')
# take the values
set1 = {'time', 'lat', 'lon'}
set2 = set(fileobj.variables.keys())
set3 = set2 - set1
datadim = set3.pop()
print "******************datadim: "+datadim
data = fileobj.variables[datadim].getValue()[0,:,:]
lon = fileobj.variables['lon'].getValue()
lat = fileobj.variables['lat'].getValue()
fileobj.close()
data, lon = bm.shiftgrid(180.,data, lon,start=False)
data = MA.masked_equal(data, 1.0e20)
#data2 = inpaint.replace_nans(data, 10, 0.25, 2, 'idw')
#- Make 2-D longitude and latitude arrays:
[lon2d, lat2d] =np.meshgrid(lon, lat)
#- Set up map:
mapproj = bm.Basemap(projection='cyl',
llcrnrlat=-90.0, llcrnrlon=-180.00,
urcrnrlat=90.0, urcrnrlon=180.0)
mapproj.drawcoastlines(linewidth=.5)
mapproj.drawmapboundary(fill_color='.8')
#mapproj.drawparallels(N.array([-90, -45, 0, 45, 90]), labels=[1,0,0,0])
#mapproj.drawmeridians(N.array([0, 90, 180, 270, 360]), labels=[0,0,0,1])
lonall, latall = mapproj(lon2d, lat2d)
cmap=plt.cm.Spectral
#- Make a contour plot of the temperature:
mymapf = plt.contourf(lonall, latall, data, 20, cmap=cmap)
#plt.clabel(mymapf, fontsize=12)
plt.title(cmap.name)
plt.colorbar(mymapf, orientation='horizontal')
plt.savefig('sample2.png', dpi=150, edgecolor='red', format='png', bbox_inches='tight', pad_inches=.2)
plt.close()
if __name__ == "__main__":
main()
I am comparing the output from this code (the first figure), with output of the same datafile from Panoply. Zoomin in and looking more precisely it seems like it is not the smoothness problem, but the pyplot model provides one stripe slimmer, or the contours are cut earlier (the outer boundaries shows this clearly, and inner contours are different due to this fact). It makes it to look like that the pyplot model is not as smooth as the Panoply one. how can I get (nearly) the same model? Am I distinguishing it right?
I had similar problem and google pointed me to this: blog post. Basically he's using inpaint algorithm to interpolate missing values and produce valid array for filtering.
The code is at the end of the post, and you can save it to site-packages (or else) and load it as module (i.e. inpaint.py):
import inpaint
filled = inpaint.replace_nans(NANMask, 5, 0.5, 2, 'idw')
I'm happy with the result, and I guess it will suite missing temperature values just fine. There is also next version here: github but code will need some cleaning for general usage as it's part of a project.
For reference, easy use and preservation sake I'll post the code (of initial version) here:
# -*- coding: utf-8 -*-
"""A module for various utilities and helper functions"""
import numpy as np
#cimport numpy as np
#cimport cython
DTYPEf = np.float64
#ctypedef np.float64_t DTYPEf_t
DTYPEi = np.int32
#ctypedef np.int32_t DTYPEi_t
##cython.boundscheck(False) # turn of bounds-checking for entire function
##cython.wraparound(False) # turn of bounds-checking for entire function
def replace_nans(array, max_iter, tol,kernel_size=1,method='localmean'):
"""Replace NaN elements in an array using an iterative image inpainting algorithm.
The algorithm is the following:
1) For each element in the input array, replace it by a weighted average
of the neighbouring elements which are not NaN themselves. The weights depends
of the method type. If ``method=localmean`` weight are equal to 1/( (2*kernel_size+1)**2 -1 )
2) Several iterations are needed if there are adjacent NaN elements.
If this is the case, information is "spread" from the edges of the missing
regions iteratively, until the variation is below a certain threshold.
Parameters
----------
array : 2d np.ndarray
an array containing NaN elements that have to be replaced
max_iter : int
the number of iterations
kernel_size : int
the size of the kernel, default is 1
method : str
the method used to replace invalid values. Valid options are
`localmean`, 'idw'.
Returns
-------
filled : 2d np.ndarray
a copy of the input array, where NaN elements have been replaced.
"""
# cdef int i, j, I, J, it, n, k, l
# cdef int n_invalids
filled = np.empty( [array.shape[0], array.shape[1]], dtype=DTYPEf)
kernel = np.empty( (2*kernel_size+1, 2*kernel_size+1), dtype=DTYPEf )
# cdef np.ndarray[np.int_t, ndim=1] inans
# cdef np.ndarray[np.int_t, ndim=1] jnans
# indices where array is NaN
inans, jnans = np.nonzero( np.isnan(array) )
# number of NaN elements
n_nans = len(inans)
# arrays which contain replaced values to check for convergence
replaced_new = np.zeros( n_nans, dtype=DTYPEf)
replaced_old = np.zeros( n_nans, dtype=DTYPEf)
# depending on kernel type, fill kernel array
if method == 'localmean':
print 'kernel_size', kernel_size
for i in range(2*kernel_size+1):
for j in range(2*kernel_size+1):
kernel[i,j] = 1
print kernel, 'kernel'
elif method == 'idw':
kernel = np.array([[0, 0.5, 0.5, 0.5,0],
[0.5,0.75,0.75,0.75,0.5],
[0.5,0.75,1,0.75,0.5],
[0.5,0.75,0.75,0.5,1],
[0, 0.5, 0.5 ,0.5 ,0]])
print kernel, 'kernel'
else:
raise ValueError( 'method not valid. Should be one of `localmean`.')
# fill new array with input elements
for i in range(array.shape[0]):
for j in range(array.shape[1]):
filled[i,j] = array[i,j]
# make several passes
# until we reach convergence
for it in range(max_iter):
print 'iteration', it
# for each NaN element
for k in range(n_nans):
i = inans[k]
j = jnans[k]
# initialize to zero
filled[i,j] = 0.0
n = 0
# loop over the kernel
for I in range(2*kernel_size+1):
for J in range(2*kernel_size+1):
# if we are not out of the boundaries
if i+I-kernel_size < array.shape[0] and i+I-kernel_size >= 0:
if j+J-kernel_size < array.shape[1] and j+J-kernel_size >= 0:
# if the neighbour element is not NaN itself.
if filled[i+I-kernel_size, j+J-kernel_size] == filled[i+I-kernel_size, j+J-kernel_size] :
# do not sum itself
if I-kernel_size != 0 and J-kernel_size != 0:
# convolve kernel with original array
filled[i,j] = filled[i,j] + filled[i+I-kernel_size, j+J-kernel_size]*kernel[I, J]
n = n + 1*kernel[I,J]
# divide value by effective number of added elements
if n != 0:
filled[i,j] = filled[i,j] / n
replaced_new[k] = filled[i,j]
else:
filled[i,j] = np.nan
# check if mean square difference between values of replaced
#elements is below a certain tolerance
print 'tolerance', np.mean( (replaced_new-replaced_old)**2 )
if np.mean( (replaced_new-replaced_old)**2 ) < tol:
break
else:
for l in range(n_nans):
replaced_old[l] = replaced_new[l]
return filled
def sincinterp(image, x, y, kernel_size=3 ):
"""Re-sample an image at intermediate positions between pixels.
This function uses a cardinal interpolation formula which limits
the loss of information in the resampling process. It uses a limited
number of neighbouring pixels.
The new image :math:`im^+` at fractional locations :math:`x` and :math:`y` is computed as:
.. math::
im^+(x,y) = \sum_{i=-\mathtt{kernel\_size}}^{i=\mathtt{kernel\_size}} \sum_{j=-\mathtt{kernel\_size}}^{j=\mathtt{kernel\_size}} \mathtt{image}(i,j) sin[\pi(i-\mathtt{x})] sin[\pi(j-\mathtt{y})] / \pi(i-\mathtt{x}) / \pi(j-\mathtt{y})
Parameters
----------
image : np.ndarray, dtype np.int32
the image array.
x : two dimensions np.ndarray of floats
an array containing fractional pixel row
positions at which to interpolate the image
y : two dimensions np.ndarray of floats
an array containing fractional pixel column
positions at which to interpolate the image
kernel_size : int
interpolation is performed over a ``(2*kernel_size+1)*(2*kernel_size+1)``
submatrix in the neighbourhood of each interpolation point.
Returns
-------
im : np.ndarray, dtype np.float64
the interpolated value of ``image`` at the points specified
by ``x`` and ``y``
"""
# indices
# cdef int i, j, I, J
# the output array
r = np.zeros( [x.shape[0], x.shape[1]], dtype=DTYPEf)
# fast pi
pi = 3.1419
# for each point of the output array
for I in range(x.shape[0]):
for J in range(x.shape[1]):
#loop over all neighbouring grid points
for i in range( int(x[I,J])-kernel_size, int(x[I,J])+kernel_size+1 ):
for j in range( int(y[I,J])-kernel_size, int(y[I,J])+kernel_size+1 ):
# check that we are in the boundaries
if i >= 0 and i <= image.shape[0] and j >= 0 and j <= image.shape[1]:
if (i-x[I,J]) == 0.0 and (j-y[I,J]) == 0.0:
r[I,J] = r[I,J] + image[i,j]
elif (i-x[I,J]) == 0.0:
r[I,J] = r[I,J] + image[i,j] * np.sin( pi*(j-y[I,J]) )/( pi*(j-y[I,J]) )
elif (j-y[I,J]) == 0.0:
r[I,J] = r[I,J] + image[i,j] * np.sin( pi*(i-x[I,J]) )/( pi*(i-x[I,J]) )
else:
r[I,J] = r[I,J] + image[i,j] * np.sin( pi*(i-x[I,J]) )*np.sin( pi*(j-y[I,J]) )/( pi*pi*(i-x[I,J])*(j-y[I,J]))
return r
#cdef extern from "math.h":
# double sin(double)
A simple smoothing function that works with masked data will solve this. One can then avoid the approaches that involve making up data (ie, interpolating, inpainting, etc); and making up data should always be avoided.
The main issue that arises when smoothing masked data is that for each point, smoothing uses the neighboring values to calculate a new value at a center point, but when those neighbors are masked, the new value for the center point will also become masked due to the rules of masked arrays. Therefore, one needs to do the calculation with unmasked data, and explicitly account for the mask. That's easy to do, and is not in the function smooth below.
from numpy import *
import pylab as plt
# make a grid and a striped mask as test data
N = 100
x = linspace(0, 5, N, endpoint=True)
grid = 2. + 1.*(sin(2*pi*x)[:,newaxis]*sin(2*pi*x)>0.)
m = resize((sin(pi*x)>0), (N,N))
plt.imshow(grid.copy(), cmap='jet', interpolation='nearest')
plt.colorbar()
plt.title('original data')
def smooth(u, mask):
m = ~mask
r = u*m # set all 'masked' points to 0. so they aren't used in the smoothing
a = 4*r[1:-1,1:-1] + r[2:,1:-1] + r[:-2,1:-1] + r[1:-1,2:] + r[1:-1,:-2]
b = 4*m[1:-1,1:-1] + m[2:,1:-1] + m[:-2,1:-1] + m[1:-1,2:] + m[1:-1,:-2] # a divisor that accounts for masked points
b[b==0] = 1. # for avoiding divide by 0 error (region is masked so value doesn't matter)
u[1:-1,1:-1] = a/b
# run the data through the smoothing filter a few times
for i in range(10):
smooth(grid, m)
mg = ma.array(grid, mask=m) # put together the mask and the data
plt.figure()
plt.imshow(mg, cmap='jet', interpolation='nearest')
plt.colorbar()
plt.title('smoothed with mask')
plt.show()
The main point is that at the boundary of the mask, the masked values are not used in the smoothing. (This is also where the grid squares switch values, so it would be clear in the figure if the masked neighboring values were being used.)
We also just had this problem and the astropy package has us covered:
import numpy as np
import matplotlib.pyplot as plt
# Some Axes
x = np.arange(100)
y = np.arange(100)
#Some Interesting Shape
z = np.array(np.outer(np.sin((x+y)/10),np.sin(y/3)),dtype=float)
# some mask
mask = np.outer(np.sin((x+y)/20),np.sin(y/5))**2>.9
# masked data represent noise, so lets put in some trash into the masked points
z[mask] = (np.random.random(size = (100,100))*10)[mask]
# masked data
z_masked = np.ma.masked_array(z, mask)
# "Conventional" filter
filter_kernelsize = 2
import scipy.ndimage
z_filtered_bad = scipy.ndimage.gaussian_filter(z_masked,filter_kernelsize)
# Lets filter it
import astropy.convolution.convolve
from astropy.convolution import Gaussian2DKernel
k = Gaussian2DKernel(1.5)
z_filtered = astropy.convolution.convolve(z_masked, k, boundary='extend')
### Plots:
fig, axes = plt.subplots(2,2)
plt.sca(axes[0,0])
plt.title('Raw Data')
plt.imshow(z)
plt.colorbar()
plt.sca(axes[0,1])
plt.title('Raw Data Masked')
plt.imshow(z_masked)
plt.colorbar()
plt.sca(axes[1,0])
plt.title('ndimage filter (ignores mask)')
plt.imshow(z_filtered_bad)
plt.colorbar()
plt.sca(axes[1,1])
plt.title('astropy filter (uses mask)')
plt.imshow(z_filtered)
plt.colorbar()
plt.tight_layout()
Output plot of the code