I am newbie to OpenCV,now I am making a senior project related Image processing. I have a question: Can I make a horizontal or vertical histogram with some functions of OpenCV?
Thanks,
Truong
The most efficient way to do this is by using the cvReduce function. There's a parameter to allow to select if you want an horizontal or vertical projection.
You can also do it by hand with the functions cvGetCol and cvGetRow combined with cvSum.
Based on the link you provided in a comment, this is what I believe you're trying to do.
You want to create an array with n elements, where n is the number of columns in the input image. The value of the nth element of the array is the sum of all the pixels in the nth column.
You can calculate this array by looping over the columns of the input image, using cvGetSubRect to access the pixels in that column, and cvSum to sum those pixels.
Here is some Python code that does that, assuming a grayscale image:
import cv
def verticalProjection(img):
"Return a list containing the sum of the pixels in each column"
(w,h) = cv.GetSize(img)
sumCols = []
for j in range(w):
col = cv.GetSubRect(img, (j,0,1,h))
sumCols.append(cv.Sum(col)[0])
return sumCols
Updating carnieri answer (some cv functions are not working today)
import numpy as np
import cv2
def verticalProjection(img):
"Return a list containing the sum of the pixels in each column"
(h, w) = img.shape[:2]
sumCols = []
for j in range(w):
col = img[0:h, j:j+1] # y1:y2, x1:x2
sumCols.append(np.sum(col))
return sumCols
Regards.
An example of using cv2.reduce with OpenCV 3 in Python :
import numpy as np
import cv2
img = cv2.imread("test_1.png")
x_sum = cv2.reduce(img, 0, cv2.REDUCE_SUM, dtype=cv2.CV_32S)
y_sum = cv2.reduce(img, 1, cv2.REDUCE_SUM, dtype=cv2.CV_32S)
Related
My dataset is :enter image description here. First seven columns are for input metric. And the last five columns are for outputs. Output is an array of 5 numbers consist of zero or one. I am using Keras functional API for that. Whenever I try to to resample my data with individual columns, I got shape issues in merging, even if I I try to slice the rows.
Basically there's no "easy" approach to doing this. The only logical way is to maybe use Label Powerset over your design matrix, and resample based on the created column off that - though in that scenario it might be easier to "handcraft" such a transformation.
Here is one approach
import numpy as np
from sklearn.datasets import make_multilabel_classification
from sklearn.datasets import make_classification
from imblearn.over_sampling import RandomOverSampler
import pandas as pd
X0, y = make_classification()
_, X1 = make_multilabel_classification(n_classes=5, random_state=0)
# transform X1 by creating a powerset...
df_x1 = pd.DataFrame(X1, columns=[f'c{x}' for x in range(X1.shape[1])])
df_x1 = pd.merge(df_x1, df_x1.drop_duplicates().reset_index()).rename(columns={"index":"dummy"})
print(df_x1['dummy'].value_counts()) # shows imbalance
df_x1 = df_x1.reset_index() # so that we know which rows are resampled
df_y1 = df_x1['dummy']
df_x1 = df_x1[[x for x in df_x1.columns if x != 'dummy']]
ros = RandomOverSampler()
X_sample, _ = ros.fit_resample(df_x1, df_y1) # this is the resampled index
X = np.hstack([X0, X1])
X_res, y_res = X[X_sample['index'], :], y[X_sample['index']]
Where the secret sauce really is this bit:
df_x1 = pd.merge(df_x1, df_x1.drop_duplicates().reset_index()).rename(columns={"index":"dummy"})
Which re-indexes based on the selected 5 columns
df_x1 = df_x1.reset_index()
Which is then used in the RandomOverSampler, and would guarantee the 5 columns would be balanced.
Finally, we can select the indices of the sampling, to generate a dataset and labels which has been successfully resampled across both X0, X1, y
X = np.hstack([X0, X1])
X_res, y_res = X[X_sample['index'], :], y[X_sample['index']]
What is the best way to iterate da.linalg.inv over a multi-dimensional dask array?
I have a dask array of shape (4, 4, 8, 8), and need to compute the inverse of the last two dimensions. With numpy, np.linalg.inv loops over all dimensions except the last two, so in the following example, I can just call np.linalg.inv(A).
I have chosen to use a for loop, but I have read about gufuncs in dask (the documentation seems a little outdated). However, I'm not sure how to implement the it, particularly the "signature" bit,
import dask.array as da
import numpy as np
A = da.random.random((4,4,8,8))
A2 = A.reshape((-1,) + A.shape[-2:])
B = [da.linalg.inv(a) for a in A2]
B2 = da.asarray(B)
B3 = B2.reshape(A.shape)
np.testing.assert_array_almost_equal(
np.linalg.inv(A.compute()),
B3
)
My attempt at a gufunc leads to an error:
def foo(x):
return da.linalg.inv(x)
gufoo = da.gufunc(foo, signature="()->()", output_dtypes=float, vectorize=True)
gufoo(A2).compute() # IndexError: tuple index out of range
I think that you want to apply the numpy function np.linalg.inv over your Dask array rather than the dask array function.
If np.linalg.inv is already a gufunc then it might work as expected today
np.linalg.inv(A)
I have a bunch of images like
What would be the good way to extract just the table structure from the image? I'm only interested extracting the straight lines.
I have been toying around with OpenCV Finding Contours code sample and the results are quite promising. I'm just wondering if there is maybe a better way?
OpenCV has a nice way to detect line segments. Here is a code snippet in python:
import math
import numpy as np
import cv2
img = cv2.imread('page2.png')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
lsd = cv2.createLineSegmentDetector(0)
dlines = lsd.detect(gray)
for dline in dlines[0]:
x0 = int(round(dline[0][0]))
y0 = int(round(dline[0][1]))
x1 = int(round(dline[0][2]))
y1 = int(round(dline[0][3]))
cv2.line(img, (x0, y0), (x1,y1), 255, 1, cv2.LINE_AA)
# print line segment length
a = (x0-x1) * (x0-x1)
b = (y0-y1) * (y0-y1)
c = a + b
print(math.sqrt(c))
cv2.imwrite('page2_lines.png', img)
Kindly go through my Github repository Code for table extraction
The developed code detect table and extract out information by keeping the spatial coordinates intact.
The code detects lines from tables as shown in an image below. I hope it solves your problem.
The extracted output in terms of a table is shown below.
I am getting different shapes for my PCA using sklearn. Why isn't my transformation resulting in an array of the same dimensions like the docs say?
fit_transform(X, y=None)
Fit the model with X and apply the dimensionality reduction on X.
Parameters:
X : array-like, shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features.
Returns:
X_new : array-like, shape (n_samples, n_components)
Check this out with the iris dataset which is (150, 4) where I'm making 4 PCs:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler
from sklearn import decomposition
import seaborn as sns; sns.set_style("whitegrid", {'axes.grid' : False})
%matplotlib inline
np.random.seed(0)
# Iris dataset
DF_data = pd.DataFrame(load_iris().data,
index = ["iris_%d" % i for i in range(load_iris().data.shape[0])],
columns = load_iris().feature_names)
Se_targets = pd.Series(load_iris().target,
index = ["iris_%d" % i for i in range(load_iris().data.shape[0])],
name = "Species")
# Scaling mean = 0, var = 1
DF_standard = pd.DataFrame(StandardScaler().fit_transform(DF_data),
index = DF_data.index,
columns = DF_data.columns)
# Sklearn for Principal Componenet Analysis
# Dims
m = DF_standard.shape[1]
K = m
# PCA (How I tend to set it up)
M_PCA = decomposition.PCA()
A_components = M_PCA.fit_transform(DF_standard)
#DF_standard.shape, A_components.shape
#((150, 4), (150, 4))
but then when I use the same exact approach on my actual dataset (76, 1989) as in 76 samples and 1989 attributes/dimensions I get a (76, 76) array instead of (76, 1989)
DF_centered = normalize(DF_mydata, method="center", axis=0)
m = DF_centered.shape[1]
# print(m)
# 1989
M_PCA = decomposition.PCA(n_components=m)
A_components = M_PCA.fit_transform(DF_centered)
DF_centered.shape, A_components.shape
# ((76, 1989), (76, 76))
normalize is just a wrapper I made that subtracts the mean from each dimension.
(Note: this answer is adapted from my answer on Cross Validated here: Why are there only n−1 principal components for n data points if the number of dimensions is larger or equal than n?)
PCA (as most typically run) creates a new coordinate system by:
shifting the origin to the centroid of your data,
squeezes and/or stretches the axes to make them equal in length, and
rotates your axes into a new orientation.
(For more details, see this excellent CV thread: Making sense of principal component analysis, eigenvectors & eigenvalues.) However, step 3 rotates your axes in a very specific way. Your new X1 (now called "PC1", i.e., the first principal component) is oriented in your data's direction of maximal variation. The second principal component is oriented in the direction of the next greatest amount of variation that is orthogonal to the first principal component. The remaining principal components are formed likewise.
With this in mind, let's examine a simple example (suggested by #amoeba in a comment). Here is a data matrix with two points in a three dimensional space:
X = [ 1 1 1
2 2 2 ]
Let's view these points in a (pseudo) three dimensional scatterplot:
So let's follow the steps listed above. (1) The origin of the new coordinate system will be located at (1.5,1.5,1.5). (2) The axes are already equal. (3) The first principal component will go diagonally from what used to be (0,0,0) to what was originally (3,3,3), which is the direction of greatest variation for these data. Now, the second principal component must be orthogonal to the first, and should go in the direction of the greatest remaining variation. But what direction is that? Is it from (0,0,3) to (3,3,0), or from (0,3,0) to (3,0,3), or something else? There is no remaining variation, so there cannot be any more principal components.
With N=2 data, we can fit (at most) N−1=1 principal components.
I am trying to find the number of objects in a given image using watershed segmentation. Consider for example the coins image. Here I would like to know the number of coins in the image. I implemented the code available at Scikit-image documentation and tweaked with it a little and got results similar to those displayed on the documentation page.
After looking at functions used in the code in detail I found out that ndimage.label() also returns number of unique objects found in the image (mentioned in it's documentation), but when I print that value I am getting 53 which is very high as compared to the number of coins in the actual image.
Can somebody suggest some method to find the number of objects in an image.
Here is a version of your code that counts the coins in one of two ways: a) by directly segmenting the distance image and b) by doing watershed first and rejecting tiny intersecting regions.
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
from skimage import io, color, filter as filters
from scipy import ndimage
from skimage.morphology import watershed
from skimage.feature import peak_local_max
from skimage.measure import regionprops, label
image = color.rgb2gray(io.imread('water_coins.jpg', plugin='freeimage'))
image = image < filters.threshold_otsu(image)
distance = ndimage.distance_transform_edt(image)
# Here's one way to measure the number of coins directly
# from the distance map
coin_centres = (distance > 0.8 * distance.max())
print('Number of coins (method 1):', np.max(label(coin_centres)))
# Or you can proceed with the watershed labeling
local_maxi = peak_local_max(distance, indices=False, footprint=np.ones((3, 3)),
labels=image)
markers, num_features = ndimage.label(local_maxi)
labels = watershed(-distance, markers, mask=image)
# ...but then you have to clean up the tiny intersections between coins
regions = regionprops(labels)
regions = [r for r in regions if r.area > 50]
print('Number of coins (method 2):', len(regions) - 1)
fig, axes = plt.subplots(ncols=3, figsize=(8, 2.7))
ax0, ax1, ax2 = axes
ax0.imshow(image, cmap=plt.cm.gray, interpolation='nearest')
ax0.set_title('Overlapping objects')
ax1.imshow(-distance, cmap=plt.cm.jet, interpolation='nearest')
ax1.set_title('Distances')
ax2.imshow(labels, cmap=plt.cm.spectral, interpolation='nearest')
ax2.set_title('Separated objects')
for ax in axes:
ax.axis('off')
fig.subplots_adjust(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0,
right=1)
plt.show()