I have a grayscale image, I would like to split it into pixels and determine the grayscale in each pixel of the image.
The array is needed in the following form: (pixel by X, pixel by Y, gray shade 0-255).
1,1,25;
1,2,36;
1,3,50;
.
.
.
50,60,96;
.
.
.
if the image is 500 by 600 dots, then at the end it should get - (500,600, grayscale).
Could you tell me please, how can I get such an array of data from an image? What do I need to do? Which libraries should I use? If there is someone who has solved such a problem, please give an example. Thank you very much!
If you already have an image file, you can read it like this:
from PIL import Image
img = Image.open('/path/to/image.png')
To get this an an array:
import numpy as np
ima = np.asarray(img)
If it's really an 8-bit greyscale image, you might be able to use Image.open('image.png', mode='L'), but in any case you can always just get the red channel with ima[:, :, 0]. If it's greyscale, all the channels will be equal.
Now you can stack these grey levels with the coordinates:
h, w, _ = ima.shape
x, y = np.meshgrid(np.arange(w), np.arange(h))
np.dstack([x, y, ima[..., 0]])
I would do this:
# random data
np.random.seed(10)
img = np.random.randint(0,256, (500,600))
# coordinates
# np.meshgrid is also a good (better) choice
x, y = np.where(np.ones_like(img))
# put them together
out = np.stack([x,y, img.ravel()], axis=1)
Output:
array([[ 0, 0, 9],
[ 0, 1, 125],
[ 0, 2, 228],
...,
[499, 597, 111],
[499, 598, 128],
[499, 599, 8]])
Related
I am using python 3.8.5 and opencv 4.5.1 on windows 7
I am using the following code to rotate images.
def pad_rotate(image, ang, pad, pad_value=0):
(h, w) = image.shape[:2]
#create larger image and paste original image at the center.
# this is done to avoid any cropping during rotation
nH, nW = h + 2*pad, w + 2*pad #new height and width
cY, cX = nW//2, nH//2 #center of the new image
#create new image with pad_values
newImg = np.zeros((h+2*pad, w+2*pad), dtype=image.dtype)
newImg[:,:] = pad_value
#paste new image at the center
newImg[pad:pad+h, pad:pad+w] = image
#rotate CCW (for positive angles)
M = cv2.getRotationMatrix2D(center=(cX, cY), angle=ang, scale=1.0)
rotImg = cv2.warpAffine(newImg, M, (nW, nH), cv2.INTER_CUBIC,
borderMode=cv2.BORDER_CONSTANT, borderValue=pad_value)
return rotImg
My issue is that after the rotation, image intensity distribution is different than original.
Following part of the question is edited to clarify the issue
img = np.random.rand(500,500)
Rimg = pad_rotate(img, 15, 300, np.nan)
Here is what these images look like:
Their intensities have clearly shifted:
np.percentile(img, [20, 50, 80])
# prints array([0.20061218, 0.50015415, 0.79989986])
np.nanpercentile(Rimg, [20, 50, 80])
# prints array([0.32420028, 0.50031483, 0.67656537])
Can someone please tell me how to avoid this normalization?
The averaging effect of the interpolation changes the distribution...
Note:
There is a mistake in your code sample (not related to the percentiles).
The 4'th argument of warpAffine is dst.
replace cv2.warpAffine(newImg, M, (nW, nH), cv2.INTER_CUBIC with:
cv2.warpAffine(newImg, M, (nW, nH), flags=cv2.INTER_CUBIC
I tried to simplify the code sample that reproduces the problem.
The code sample uses linear interpolation, 1 degree rotation, and no NaN values.
import numpy as np
import cv2
img = np.random.rand(1000, 1000)
M = cv2.getRotationMatrix2D((img.shape[1]//2, img.shape[0]//2), 1, 1) # Rotate by 1 degree
Rimg = cv2.warpAffine(img, M, (img.shape[1], img.shape[0]), flags=cv2.INTER_LINEAR) # Use Linear interpolation
Rimg = Rimg[20:-20, 20:-20] # Crop the part without the margins.
print(np.percentile(img, [20, 50, 80])) #[0.20005696 0.49990526 0.79954818]
print(np.percentile(Rimg, [20, 50, 80])) #[0.32244747 0.4998595 0.67698961]
cv2.imshow('img', img)
cv2.imshow('Rimg', Rimg)
cv2.waitKey()
cv2.destroyAllWindows()
When we disable the interpolation,
Rimg = cv2.warpAffine(img, M, (img.shape[1], img.shape[0]), flags=cv2.INTER_NEAREST)
The percentiles are: [0.19943713 0.50004768 0.7995525 ].
Simpler example for showing that averaging elements changes the distribution:
A = np.random.rand(10000000)
B = (A[0:-1:2] + A[1::2])/2 # Averaging every two elements.
print(np.percentile(A, [20, 50, 80])) # [0.19995436 0.49999472 0.80007232]
print(np.percentile(B, [20, 50, 80])) # [0.31617922 0.50000145 0.68377251]
Why does interpolation skews the distribution towered the median?
I am not a mathematician.
I am sure you can get a better explanation...
Here is an intuitive example:
Assume there is list of values with uniform distribution in range [0, 1].
Assume there is a zero value in the list:
[0.2, 0.7, 0, 0.5... ]
After averaging every two sequential elements, the probability for getting a zero element in the output list is very small (only two sequential zeros result a zero).
The example shows that averaging pushes the extreme values towered the center.
I'm new with OpenCV and the thing is that i need to get all the contour points. This is easy setting the cv2.RETR_TREE mode in findContours method. The thing is that in this way, returns redundant coordinates. So, for example, in this polygon, i don't want to get the contour points like this:
But like this:
So according to the first image, green color are the contours detected with RETR_TREE mode, and points 1-2, 3-5, 4-6, ... are redundant, because they are so close to each other. I need to put together those redundant points into one, and append it in the customContours array.
For the moment, i only have the code according for the first picture, setting up the distance between the points and the points coordinates:
def getContours(img, minArea=20000, cThr=[100, 100]):
font = cv2.FONT_HERSHEY_COMPLEX
imgColor = img
imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
imgBlur = cv2.GaussianBlur(imgGray, (5, 5), 1)
imgCanny = cv2.Canny(imgBlur, cThr[0], cThr[1])
kernel = np.ones((5, 5))
imgDial = cv2.dilate(imgCanny, kernel, iterations=3)
imgThre = cv2.erode(imgDial, kernel, iterations=2)
cv2.imshow('threshold', imgThre)
contours, hierachy = cv2.findContours(imgThre, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
customContours = []
for cnt in contours:
area = cv2.contourArea(cnt)
if area > minArea:
peri = cv2.arcLength(cnt, True)
approx = cv2.approxPolyDP(cnt, 0.009*peri, True)
bbox = cv2.boundingRect(approx)
customContours.append([len(approx), area, approx, bbox, cnt])
print('points: ', len(approx))
n = approx.ravel()
i = 0
for j in n:
if i % 2 == 0:
x = n[i]
y = n[i + 1]
string = str(x)+" " + str(y)
cv2.putText(imgColor, str(i//2+1) + ': ' + string, (x, y), font, 2, (0, 0, 0), 2)
i = i + 1
customContours = sorted(customContours, key=lambda x: x[1], reverse=True)
for cnt in customContours:
cv2.drawContours(imgColor, [cnt[2]], 0, (0, 0, 255), 5)
return imgColor, customContours
Could you help me to get the real points regarding to i.e. the second picture?
(EDIT 01/07/21)
I want a generic solution, because the image could be more complex, such as the following picture:
NOTE: notice that the middle arrow (points 17 and 18) doesn't have a closed area, so isn't a polygon to study. Then, that region is not interested to obtain his points. Also, notice that the order of the points aren't important, but if the entry is the hole image, it should know that there are 4 polygons, so for each polygon points starts with 0, then 1, etc.
Here's my approach. It is mainly morphological-based. It involves convolving the image with a special kernel. This convolution identifies the end-points of the triangle as well as the intersection points where the middle line is present. This will result in a points mask containing the pixel that matches the points you are looking for. After that, we can apply a little bit of morphology to join possible duplicated points. What remains is to get a list of the coordinate of these points for further processing.
These are the steps:
Get a binary image of the input via Otsu's thresholding
Get the skeleton of the binary image
Define the special kernel and convolve the skeleton image
Apply a morphological dilate to join possible duplicated points
Get the centroids of the points and store them in a list
Here's the code:
# Imports:
import numpy as np
import cv2
# image path
path = "D://opencvImages//"
fileName = "triangle.png"
# Reading an image in default mode:
inputImage = cv2.imread(path + fileName)
# Prepare a deep copy for results:
inputImageCopy = inputImage.copy()
# Convert BGR to Grayscale
grayImage = cv2.cvtColor(inputImage, cv2.COLOR_BGR2GRAY)
# Threshold via Otsu:
_, binaryImage = cv2.threshold(grayImage, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
The first bit computes the binary image. Very straightforward. I'm using this image as base, which is just a cleaned-up version of what you posted without the annotations. This is the resulting binary image:
Now, to perform the convolution we must first get the image "skeleton". The skeleton is a version of the binary image where lines have been normalized to have a width of 1 pixel. This is useful because we can then convolve the image with a 3 x 3 kernel and look for specific pixel patterns. Let's compute the skeleton using OpenCV's extended image processing module:
# Get image skeleton:
skeleton = cv2.ximgproc.thinning(binaryImage, None, 1)
This is the image obtained:
We can now apply the convolution. The approach is based on Mark Setchell's info on this post. The post mainly shows the method for finding end-points of a shape, but I extended it to also identify line intersections, such as the middle portion of the triangle. The main idea is that the convolution yields a very specific value where patterns of black and white pixels are found in the input image. Refer to the post for the theory behind this idea, but here, we are looking for two values: 110 and 40. The first one occurs when an end-point has been found. The second one when a line intersections is found. Let's setup the convolution:
# Threshold the image so that white pixels get a value of 0 and
# black pixels a value of 10:
_, binaryImage = cv2.threshold(skeleton, 128, 10, cv2.THRESH_BINARY)
# Set the convolution kernel:
h = np.array([[1, 1, 1],
[1, 10, 1],
[1, 1, 1]])
# Convolve the image with the kernel:
imgFiltered = cv2.filter2D(binaryImage, -1, h)
# Create list of thresholds:
thresh = [110, 40]
The first part is done. We are going to detect end-points and intersections in two separated steps. Each step will produce a partial result, we can OR both results to get a final mask:
# Prepare the final mask of points:
(height, width) = binaryImage.shape
pointsMask = np.zeros((height, width, 1), np.uint8)
# Perform convolution and create points mask:
for t in range(len(thresh)):
# Get current threshold:
currentThresh = thresh[t]
# Locate the threshold in the filtered image:
tempMat = np.where(imgFiltered == currentThresh, 255, 0)
# Convert and shape the image to a uint8 height x width x channels
# numpy array:
tempMat = tempMat.astype(np.uint8)
tempMat = tempMat.reshape(height,width,1)
# Accumulate mask:
pointsMask = cv2.bitwise_or(pointsMask, tempMat)
This is the final mask of points:
Note that the white pixels are the locations that matched our target patterns. Those are the points we are looking for. As the shape is not a perfect triangle, some points could be duplicated. We can "merge" neighboring blobs by applying a morphological dilation:
# Set kernel (structuring element) size:
kernelSize = 7
# Set operation iterations:
opIterations = 3
# Get the structuring element:
morphKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernelSize, kernelSize))
# Perform Dilate:
morphoImage = cv2.morphologyEx(pointsMask, cv2.MORPH_DILATE, morphKernel, None, None, opIterations, cv2.BORDER_REFLECT101)
This is the result:
Very nice, we have now big clusters of pixels (or blobs). To get their coordinates, one possible approach would be to get the bounding rectangles of these contours and compute their centroids:
# Look for the outer contours (no children):
contours, _ = cv2.findContours(morphoImage, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# Store the points here:
pointsList = []
# Loop through the contours:
for i, c in enumerate(contours):
# Get the contours bounding rectangle:
boundRect = cv2.boundingRect(c)
# Get the centroid of the rectangle:
cx = int(boundRect[0] + 0.5 * boundRect[2])
cy = int(boundRect[1] + 0.5 * boundRect[3])
# Store centroid into list:
pointsList.append( (cx,cy) )
# Set centroid circle and text:
color = (0, 0, 255)
cv2.circle(inputImageCopy, (cx, cy), 3, color, -1)
font = cv2.FONT_HERSHEY_COMPLEX
string = str(cx) + ", " + str(cy)
cv2.putText(inputImageCopy, str(i) + ':' + string, (cx, cy), font, 0.5, (255, 0, 0), 1)
# Show image:
cv2.imshow("Circles", inputImageCopy)
cv2.waitKey(0)
These are the points located in the original input:
Note also that I've stored their coordinates in the pointsList list:
# Print the list of points:
print(pointsList)
This prints the centroids as the tuple (centroidX, centroidY):
[(717, 971), (22, 960), (183, 587), (568, 586), (388, 98)]
I have a matrix, e.g., generated as follows
x = np.random.randint(10,size=(20,20))
How to visualize the matrix with respect to the distribution of a given value, i.e., 6
In other words, how to show the matrix as an image, where the pixels with corresponding matrix entries being equivalent to 6 will be shown as white, while other pixels will be shown as black.
The simplest way to display the distribution of a given value through a black and white image is using a boolean array like x == 6. If you wish to improve visualization by replacing black and white with custom colors, NumPy's where will come in handy:
import numpy as np
import matplotlib.pyplot as plt
x = np.random.randint(10, size=(20, 20))
value = 6
foreground = [255, 0, 0] # red
background = [0, 0, 255] # blue
bw = x == value
rgb = np.where(bw[:, :, None], foreground, background)
fig, ax = plt.subplots(1, 2)
ax[0].imshow(bw, cmap='gray')
ax[0].set_title('Black & white')
ax[1].imshow(rgb)
ax[1].set_title('RGB')
plt.show(fig)
I think you want this:
from PIL import Image
import numpy as np
# Initialise data
x = np.random.randint(10,size=(20,20),dtype=np.uint8)
# Make all pixels with value 6 into white and all else black
x[x==6] = 255
x[x!=255] = 0
# Make PIL Image from Numpy array
pi = Image.fromarray(x)
# Display image
pi.show()
# Save PIL Image
pi.save('result.png')
I am new to image processing so i am really confused regarding the coordinate system with images. I have a sample image and i am trying to rotate it 45 clockwise. My transformation matrix is T = [ [cos45 sin45] [-sin45 cos45] ]
Here is the code:
import numpy as np
from matplotlib import pyplot as plt
from skimage import io
image = io.imread('sample_image')
img_transformed = np.zeros((image.shape), dtype=np.uint8)
trans_matrix = np.array([[np.cos(45), np.sin(45)], [-np.sin(45), np.cos(45)]])
for i, row in enumerate(image):
for j,col in enumerate(row):
pixel_data = image[i,j] #get the value of pixel at corresponding location
input_coord = np.array([i, j]) #this will be my [x,y] matrix
result = trans_matrix # input_coord
i_out, j_out = result #store the resulting coordinate location
#make sure the the i and j values remain within the index range
if (0 < int(i_out) < image.shape[0]) and (0 < int(j_out) < image.shape[1]):
img_transformed[int(i_out)][int(j_out)] = pixel_data
plt.imshow(img_transformed, cmap='gray')
The image comes out distorted and doesn't seems right. I know that in pixel coordinate, the origin is at the top left corner (row, column). is the rotation happening with respect to origin from the top left corner? is there a way to shift origin to center or any other given point?
Thank you all!
Yes, as you suspect, the rotation is happening with respect to the top left corner, which has coordinates (0, 0). (Also: the NumPy trigonometric functions use radians rather than degrees, so you need to convert your angle.) To compute a rotation with respect to the center, you do a little hack: you compute the transformation for moving the image so that it is centered on (0, 0), then you rotate it, then you move the result back. You need to combine these transformations in a sequence because if you do it one after the other, you'll lose everything in negative coordinates.
It's much, much easier to do this using Homogeneous coordinates, which add an extra "dummy" dimension to your image. Here's what your code would look like in homogeneous coordinates:
import numpy as np
from matplotlib import pyplot as plt
from skimage import io
image = io.imread('sample_image')
img_transformed = np.zeros((image.shape), dtype=np.uint8)
c, s = np.cos(np.radians(45)), np.sin(np.radians(45))
rot_matrix = np.array([[c, s, 0], [-s, c, 0], [0, 0, 1]])
x, y = np.array(image.shape) // 2
# move center to (0, 0)
translate1 = np.array([[1, 0, -x], [0, 1, -y], [0, 0, 1]])
# move center back to (x, y)
translate2 = np.array([[1, 0, x], [0, 1, y], [0, 0, 1]])
# compose all three transformations together
trans_matrix = translate2 # rot_matrix # translate1
for i, row in enumerate(image):
for j,col in enumerate(row):
pixel_data = image[i,j] #get the value of pixel at corresponding location
input_coord = np.array([i, j, 1]) #this will be my [x,y] matrix
result = trans_matrix # input_coord
i_out, j_out, _ = result #store the resulting coordinate location
#make sure the the i and j values remain within the index range
if (0 < int(i_out) < image.shape[0]) and (0 < int(j_out) < image.shape[1]):
img_transformed[int(i_out)][int(j_out)] = pixel_data
plt.imshow(img_transformed, cmap='gray')
The above should work ok, but you will probably get some black spots due to aliasing. What can happen is that no coordinates i, j from the input land exactly on an output pixel, so that pixel never gets updated. Instead, what you need to do is iterate over the pixels of the output image, then use the inverse transform to find which pixel in the input image maps closest to that output pixel. Something like:
inverse_tform = np.linalg.inv(trans_matrix)
for i, j in np.ndindex(img_transformed.shape):
i_orig, j_orig, _ = np.round(inverse_tform # [i, j, 1]).astype(int)
if i_orig in range(image.shape[0]) and j_orig in range(image.shape[1]):
img_transformed[i, j] = image[i_orig, j_orig]
Hope this helps!
I have an MxN ndarray that contains True and False values inside those arrays and want to draw those as an image.
The goal is to convert the array to a pillow image with each True value as a constant color. I was able to get it working by looping through each pixel and changing them individually by a comparison and drawing the pixel on a blank image, but that method is way too slow.
# img is a PIL image result
# image is the MxN ndarray
pix = img.load()
for x in range(image.shape[0]):
for y in range(image.shape[1]):
if image[x, y]:
pix[y, x] = (255, 0, 0)
Is there a way to change the ndarray to a MxNx3 by replacing the tuples directly to the True values?
If you have your True/False 2D array and the label for the color, for example [255,255,255], the following will work:
colored = np.expand_dims(bool_array_2d,axis=-1)*np.array([255,255,255])
To illustrate it with a dummy example: in the following code I have created a random matrix of 0s and 1s and then have turned the 1s to white ([255,255,255]).
import numpy as np
import matplotlib.pyplot as plt
array = np.random.randint(0,2, (100,100))
colors = np.array([255,255,255])
colored = np.expand_dims(array, axis=-1)*colors
plt.imshow(colored)
Hope this has helped
Did find another solution, converted to an image first, then converted to RGB, then converted back to separate to 3 channels. When I was trying to combine multiple boolean arrays together, this way was a lot faster.
img = Image.fromarray(image * 1, 'L').convert('RGB')
data = np.array(img)
red, green, blue = data.T
area = (red == 1)
data[...][area.T] = (255, 255, 255)
img = Image.fromarray(data)
I think you can do this quite simply and fast like this:
# Make a 2 row by 3 column image of True/False values
im = np.random.choice((True,False),(2,3))
Mine looks like this:
array([[False, False, True],
[ True, True, True]])
Now add a new axis it make it 3 channel and multiply the truth values by your new "colour":
result = im[..., np.newaxis]*[255,255,255]
which gives you this:
array([[[ 0, 0, 0],
[ 0, 0, 0],
[255, 255, 255]],
[[255, 255, 255],
[255, 255, 255],
[255, 255, 255]]])