I want to measure the distance between two points using scikit-image. Here is the image:
In the above photo I want to measure the distance between the red point and black point. The unit of measurement does not matter to me as I want to normalize the distance by the end of the day. Any idea how I can do it?
Thanks
You could get the job done through the following stepwise procedure:
Compute distance from each pixel to red and black colors.
Binarize using an appropriate threshold.
Perform morphological closing.
Determine the centroid coordinates of the resulting blobs.
Calculate the distance between centroids.
Hopefully the code below will put you on the right track:
import numpy as np
from skimage import io
from skimage.morphology import closing
from skimage.measure import regionprops
import matplotlib.pyplot as plt
from matplotlib.patches import ConnectionPatch
img = io.imread('https://i.stack.imgur.com/vbOmy.jpg')
red = [255, 0, 0]
black = [0, 0, 0]
threshold = 10
dist_from_red = np.linalg.norm(img - red, axis=-1)
dist_from_black = np.linalg.norm(img - black, axis=-1)
red_blob = closing(dist_from_red < threshold)
black_blob = closing(dist_from_black < threshold)
labels = np.zeros(shape=img.shape[:2], dtype=np.ubyte)
labels[black_blob] = 1
labels[red_blob] = 2
blobs = regionprops(labels)
center_0 = np.asarray(blobs[0].centroid[::-1])
center_1 = np.asarray(blobs[1].centroid[::-1])
dist = np.linalg.norm(center_0 - center_1)
Demo
fig, ax = plt.subplots(1, 1)
ax.imshow(img)
con = ConnectionPatch(xyA=center_0, xyB=center_1,
coordsA='data', arrowstyle="-|>", ec='yellow')
ax.add_artist(con)
plt.annotate('Distance = {:.2f}'.format(dist),
xy=(center_0 + center_1)/2, xycoords='data',
xytext=(0.5, 0.7), textcoords='figure fraction', color='blue',
arrowprops=dict(arrowstyle="->", color='blue'))
plt.show(fig)
Related
I have been working on a code where an image is given as shown
I have to place this knife onto some other image. The condition is that I have to crop the knife along its outline and not in a rectangular box.
import numpy as np
import cv2
from matplotlib import pyplot as plt
img = cv2.imread('2.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.imshow(img)
img_blur = cv2.bilateralFilter(img, d = 7,
sigmaSpace = 75, sigmaColor =75)
img_gray = cv2.cvtColor(img_blur, cv2.COLOR_RGB2GRAY)
a = img_gray.max()
_, thresh = cv2.threshold(img_gray, a/2+60, a,cv2.THRESH_BINARY_INV)
plt.imshow(thresh, cmap = 'gray')
contours, hierarchy = cv2.findContours(
image = thresh,
mode = cv2.RETR_TREE,
method = cv2.CHAIN_APPROX_SIMPLE)
contours = sorted(contours, key = cv2.contourArea, reverse = True)
img_copy = img.copy()
final = cv2.drawContours(img_copy, contours, contourIdx = -1,
color = (255, 0, 0), thickness = 2)
plt.imshow(img_copy)
This is what I have tried but it doesn't seem to work well.
Input
Output
You can do it starting with bounding box using snake algorithm with balloon force added.
Snake's algo is defined such that it minimizes 3 energies - Continuity, Curvature and Gradient. The first two (together called internal energy) get minimized when points (on curve) are pulled closer and closer i.e. contract. If they expand then energy increases which is not allowed by snake algorithm.
But this initial algo proposed in 1987 has a few problems. One of the problem is that in flat areas (where gradient is zero) algo fails to converge and does nothing. There are several modifications proposed to solve this problem. The solution of interest here is - Balloon Force proposed by LD Cohen in 1989.
Balloon force guides the contour in non-informative areas of the image, i.e., areas where the gradient of the image is too small to push the contour towards a border. A negative value will shrink the contour, while a positive value will expand the contour in these areas. Setting this to zero will disable the balloon force.
Another improvement is - Morphological Snakes which use morphological operators (such as dilation or erosion) over a binary array instead of solving PDEs over a floating point array, which is the standard approach for active contours. This makes Morphological Snakes faster and numerically more stable than their traditional counterpart.
Scikit-image's implementation using the above two improvements is morphological_geodesic_active_contour. It has a parameter balloon
Using your image
import numpy as np
import matplotlib.pyplot as plt
from skimage.segmentation import morphological_geodesic_active_contour, inverse_gaussian_gradient
from skimage.color import rgb2gray
from skimage.util import img_as_float
from PIL import Image, ImageDraw
im = Image.open('knife.jpg')
im = np.array(im)
im = rgb2gray(im)
im = img_as_float(im)
plt.imshow(im, cmap='gray')
Now let us create a function which will help us to store iterations:
def store_evolution_in(lst):
"""Returns a callback function to store the evolution of the level sets in
the given list.
"""
def _store(x):
lst.append(np.copy(x))
return _store
This method needs image to be preprocessed to highlight the contours. This can be done using the function inverse_gaussian_gradient, although the user might want to define their own version. The quality of the MorphGAC segmentation depends greatly on this preprocessing step.
gimage = inverse_gaussian_gradient(im)
Below we define our starting point - a bounding box.
init_ls = np.zeros(im.shape, dtype=np.int8)
init_ls[200:-400, 20:-30] = 1
List with intermediate results for plotting the evolution
evolution = []
callback = store_evolution_in(evolution)
Now required magic line for morphological_geodesic_active_contour with balloon contraction is below:
ls = morphological_geodesic_active_contour(gimage, 200, init_ls,
smoothing=1, balloon=-0.75,
threshold=0.7,
iter_callback=callback)
Now let us plot the results:
fig, axes = plt.subplots(1, 2, figsize=(8, 8))
ax = axes.flatten()
ax[0].imshow(im, cmap="gray")
ax[0].set_axis_off()
ax[0].contour(ls, [0.5], colors='b')
ax[0].set_title("Morphological GAC segmentation", fontsize=12)
ax[1].imshow(ls, cmap="gray")
ax[1].set_axis_off()
contour = ax[1].contour(evolution[0], [0.5], colors='r')
contour.collections[0].set_label("Starting Contour")
contour = ax[1].contour(evolution[25], [0.5], colors='g')
contour.collections[0].set_label("Iteration 25")
contour = ax[1].contour(evolution[-1], [0.5], colors='b')
contour.collections[0].set_label("Last Iteration")
ax[1].legend(loc="upper right")
title = "Morphological GAC Curve evolution"
ax[1].set_title(title, fontsize=12)
plt.show()
With more balloon force you can get only the blade of knife as well.
ls = morphological_geodesic_active_contour(gimage, 100, init_ls,
smoothing=1, balloon=-1,
threshold=0.7,
iter_callback=callback)
Play with these parameters - smoothing, balloon, threshold to get your perfect curve
This is my first experience with image processing. In the jupiter notebook, using scipy , I am trying to convert a gray scale line art image into SVG vector representation. So far I was able to convert the gray scale image to binary (monochrome image) and use sobel filter in x and y axis to get the edges of the drawing. I am getting double lines as edges to account for the both sides of the lines (as shown in below picture and also the code i have used)
I want to replace these double lines with a single one. After that to detect the lines and curves in the drawing and convert them to svg lines and bezier curves. Searching online, i am getting a bit overwhelmed and confused about the proper way forward. It would be of great help if i can get some pointers about how to proceed from here. If possible i want to do this in scipy only and not with opencv.
Rather than simply using the existing scipy functions and algorithms, I also want to learn about the underlying theory so that i can use them efficiently. So please kindly share any helpful theoretical resources.
Thanks in advance
%matplotlib inline
import numpy as np
from scipy import ndimage as nd
import matplotlib.pyplot as plt
from skimage import io
def apply_gradient_threshold(d,thres):
d2 = np.copy(d)
d2[d2 == -thres] = thres
d2[d2 != thres] = 0
return d2
def plot_images(imgs, names):
fig, axes_list = plt.subplots(1, len(imgs), figsize=(20, 20))
for name,axes in zip(names, axes_list):
axes.set_title(name)
for img, axes in zip(imgs, axes_list):
axes.imshow(img, cmap='Greys_r')
plt.show()
img_file = <file_url>
img = plt.imread(img_file)
gray_img = io.imread(img_file, as_gray=True)
if(np.max(gray_img) > 1) :
gray_img = gray_img/255 #normalize
threshold = 0.2
binary = (gray_img > threshold)*1 # convert the grayscale image to binary (monochrome)
im = binary.astype('int32')
dx = nd.sobel(im,1)
dy = nd.sobel(im,0)
dx = apply_gradient_threshold(dx, 4)
dy = apply_gradient_threshold(dy, 4)
mag = np.hypot(dx,dy) #sqrt(dx^2 + dy^2)
mag *= 255.0/np.max(mag)
plot_images([binary, mag ], ['Binary - ' + str(threshold), 'Sobel Filter Result'])
Your image is virtually already made of edges. Use thinning, not an edge filter.
How can I use G.L.C.M. features to locate dark spots in a grayscale image?
If I use a distance greater than one, i.e. I try to to make a matrix for larger distances, will I be able to detect large patches of dark pixels?
You can use features like contrast, energy, homogeneity, Correlation or dissimilarity.
Python code for these features -
import cv2
import numpy as np
from skimage.feature import greycomatrix, greycoprops
img = cv2.imread("spot.jpg")
gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
properties = ['contrast','energy', 'homogeneity','correlation','dissimilarity']
distances = [1, 2, 3]
angles = [0, np.pi/4, np.pi/2, 3*np.pi/4]
glcm = greycomatrix(gray_image,
distances=distances,
angles=angles,
symmetric=True,
normed=True)
contrast = greycoprops(glcm,properties[0])
energy = greycoprops(glcm,properties[1])
homogeneity = greycoprops(glcm,properties[2])
correlation = greycoprops(glcm,properties[3])
dissimilarity = greycoprops(glcm,properties[4])
#.......................Display.........................
print(contrast)
print(energy)
print(homogeneity)
print(correlation)
print(dissimilarity)
Please look at this github page. I want to generate heat maps in this way using Python PIL,open cv or matplotlib library. Can somebody help me figure it out?
I could create a heat map for my network at the same size as the input, but I am not able superimpose them. The heatmap shape is (800,800) and the base image shape is (800,800,3)
Updated Answer -- 29th April, 2022.
After the repeated comments I have decided to update this post with a better visualization.
Consider the following image:
img = cv2.imread('image_path')
I obtained a binary image after performing binary threshold on the a-channel of the LAB converted image:
lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
a_component = lab[:,:,1]
th = cv2.threshold(a_component,140,255,cv2.THRESH_BINARY)[1]
Applying Gaussian blur:
blur = cv2.GaussianBlur(th,(13,13), 11)
The resulting heatmap:
heatmap_img = cv2.applyColorMap(blur, cv2.COLORMAP_JET)
Finally, superimposing the heatmap over the original image:
super_imposed_img = cv2.addWeighted(heatmap_img, 0.5, img, 0.5, 0)
Note: You can vary the weight parameters in the function cv2.addWeighted and observe the differences.
My code starts from a heatmap matrix (224,224) called cam, which is applied to the original image called frame, via opencv;
and it seems to work pretty well:
import numpy as np
from cv2 import cv2
from skimage import exposure
...
capture = cv2.VideoCapture(...)
while True:
ret, frame = capture.read()
if ret:
#resize original frame
frame = cv2.resize(frame, (224, 224))
#get color map
cam = getMap(frame)
map_img = exposure.rescale_intensity(cam, out_range=(0, 255))
map_img = np.uint8(map_img)
heatmap_img = cv2.applyColorMap(map_img, cv2.COLORMAP_JET)
#merge map and frame
fin = cv2.addWeighted(heatmap_img, 0.5, frame, 0.5, 0)
#show result
cv2.imshow('frame', fin)
the getMap() function gets the headmap given the frame;
I found some interesting free videos about this topic:
https://www.youtube.com/watch?v=vTY58-51XZA&t=14s
https://www.youtube.com/watch?v=4v9usdvGU50&t=208s
I had some problems with grayscale images at this line
super_imposed_img = cv2.addWeighted(heatmap_img, 0.5, img, 0.5, 0)
but this one worked for me
plt.imshow(binary_classification_result * 0.99 + original_gray_image * 0.01)
I'm studying Image Processing on the famous Gonzales "Digital Image Processing" and talking about image restoration a lot of examples are done with computer-generated noise (gaussian, salt and pepper, etc). In MATLAB there are some built-in functions to do it. What about OpenCV?
As far as I know there are no convenient built in functions like in Matlab. But with only a few lines of code you can create those images yourself.
For example additive gaussian noise:
Mat gaussian_noise = img.clone();
randn(gaussian_noise,128,30);
Salt and pepper noise:
Mat saltpepper_noise = Mat::zeros(img.rows, img.cols,CV_8U);
randu(saltpepper_noise,0,255);
Mat black = saltpepper_noise < 30;
Mat white = saltpepper_noise > 225;
Mat saltpepper_img = img.clone();
saltpepper_img.setTo(255,white);
saltpepper_img.setTo(0,black);
There is function random_noise() from the scikit-image package. It has several builtin noise patterns, such as gaussian, s&p (for salt and pepper noise), possion and speckle.
Below I show an example of how to use this method
from PIL import Image
import numpy as np
from skimage.util import random_noise
im = Image.open("test.jpg")
# convert PIL Image to ndarray
im_arr = np.asarray(im)
# random_noise() method will convert image in [0, 255] to [0, 1.0],
# inherently it use np.random.normal() to create normal distribution
# and adds the generated noised back to image
noise_img = random_noise(im_arr, mode='gaussian', var=0.05**2)
noise_img = (255*noise_img).astype(np.uint8)
img = Image.fromarray(noise_img)
img.show()
There is also a package called imgaug which are dedicated to augment images in various ways. It provides gaussian, poissan and salt&pepper noise augmenter. Here is how you can use it to add noise to image:
from PIL import Image
import numpy as np
from imgaug import augmenters as iaa
def main():
im = Image.open("bg_img.jpg")
im_arr = np.asarray(im)
# gaussian noise
# aug = iaa.AdditiveGaussianNoise(loc=0, scale=0.1*255)
# poisson noise
# aug = iaa.AdditivePoissonNoise(lam=10.0, per_channel=True)
# salt and pepper noise
aug = iaa.SaltAndPepper(p=0.05)
im_arr = aug.augment_image(im_arr)
im = Image.fromarray(im_arr).convert('RGB')
im.show()
if __name__ == "__main__":
main()
Simple Function to add Gaussian, Salt-pepper speckle and poisson noise to an image
Parameters
----------
image : ndarray
Input image data. Will be converted to float.
mode : str
One of the following strings, selecting the type of noise to add:
'gauss' Gaussian-distributed additive noise.
'poisson' Poisson-distributed noise generated from the data.
's&p' Replaces random pixels with 0 or 1.
'speckle' Multiplicative noise using out = image + n*image,where
n,is uniform noise with specified mean & variance.
import numpy as np
import os
import cv2
def noisy(noise_typ,image):
if noise_typ == "gauss":
row,col,ch= image.shape
mean = 0
#var = 0.1
#sigma = var**0.5
gauss = np.random.normal(mean,1,(row,col,ch))
gauss = gauss.reshape(row,col,ch)
noisy = image + gauss
return noisy
elif noise_typ == "s&p":
row,col,ch = image.shape
s_vs_p = 0.5
amount = 0.004
out = image
# Salt mode
num_salt = np.ceil(amount * image.size * s_vs_p)
coords = [np.random.randint(0, i - 1, int(num_salt))
for i in image.shape]
out[coords] = 1
# Pepper mode
num_pepper = np.ceil(amount* image.size * (1. - s_vs_p))
coords = [np.random.randint(0, i - 1, int(num_pepper))
for i in image.shape]
out[coords] = 0
return out
elif noise_typ == "poisson":
vals = len(np.unique(image))
vals = 2 ** np.ceil(np.log2(vals))
noisy = np.random.poisson(image * vals) / float(vals)
return noisy
elif noise_typ =="speckle":
row,col,ch = image.shape
gauss = np.random.randn(row,col,ch)
gauss = gauss.reshape(row,col,ch)
noisy = image + image * gauss
return noisy
"Salt & Pepper" noise can be added in a quite simple fashion using NumPy matrix operations.
def add_salt_and_pepper(gb, prob):
'''Adds "Salt & Pepper" noise to an image.
gb: should be one-channel image with pixels in [0, 1] range
prob: probability (threshold) that controls level of noise'''
rnd = np.random.rand(gb.shape[0], gb.shape[1])
noisy = gb.copy()
noisy[rnd < prob] = 0
noisy[rnd > 1 - prob] = 1
return noisy
# Adding noise to the image
import cv2
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
img = cv2.imread('./fruit.png',0)
im = np.zeros(img.shape, np.uint8) # do not use original image it overwrites the image
mean = 0
sigma = 10
cv2.randn(im,mean,sigma) # create the random distribution
Fruit_Noise = cv2.add(img, im) # add the noise to the original image
plt.imshow(Fruit_Noise, cmap='gray')
The values of mean and sigma can be altered to bring about a specific change in noise like gaussian or pepper-salt noise etc.
You can use either randn or randu according to the need. Have a look at the documentation: https://docs.opencv.org/2.4/modules/core/doc/operations_on_arrays.html#cv2.randu
I made some change of #Shubham Pachori 's code. When reading a image into numpy arrary, the default dtype is uint8, which can cause wrapping when adding noise onto the image.
import numpy as np
from PIL import Image
"""
image: read through PIL.Image.open('path')
sigma: variance of gaussian noise
factor: the bigger this value is, the more noisy is the poisson_noised image
##IMPORTANT: when reading a image into numpy arrary, the default dtype is uint8,
which can cause wrapping when adding noise onto the image.
E.g, example = np.array([128,240,255], dtype='uint8')
example + 50 = np.array([178,44,49], dtype='uint8')
Transfer np.array to dtype='int16' can solve this problem.
"""
def gaussian_noise(image, sigma):
img = np.array(image)
noise = np.random.randn(img.shape[0], img.shape[1], img.shape[2])
img = img.astype('int16')
img_noise = img + noise * sigma
img_noise = np.clip(img_noise, 0, 255)
img_noise = img_noise.astype('uint8')
return Image.fromarray(img_noise)
def poisson_noise(image, factor):
factor = 1 / factor
img = np.array(image)
img = img.astype('int16')
img_noise = np.random.poisson(img * factor) / float(factor)
np.clip(img_noise, 0, 255, img_noise)
img_noise = img_noise.astype('uint8')
return Image.fromarray(img_noise)
http://scikit-image.org/docs/dev/api/skimage.util.html#skimage.util.random_noise
skimage.util.random_noise(image, mode='gaussian', seed=None, clip=True, **kwargs)
#Adding noise
[m,n]=img.shape
saltpepper_noise=zeros((m, n));
saltpepper_noise=rand(m,n); #creates a uniform random variable from 0 to 1
for i in range(0,m):
for j in range(0,n):
if saltpepper_noise[i,j]<=0.5:
saltpepper_noise[i,j]=0
else:
saltpepper_noise[i,j]=255
def add_salt_noise(src, ratio: float = 0.05, noise: list = [0, 0, 0]):
dst = src.copy()
import random
shuffle_dict = {}
i = 0
while i < (int(dst.shape[0]*dst.shape[1] * ratio)):
x, y = random.randint(0, dst.shape[0] - 1), random.randint(0, dst.shape[1] - 1)
if (x, y) in shuffle_dict:
continue
else:
dst[x, y] = noise
shuffle_dict[(x, y)] = 0
i += 1
return dst
although there is no built-in functions like in matlab
imnoise(image,noiseType,NoiseLevel) but we can easily add required amount random
valued impulse noise or salt and pepper into an image manually.
to add random valued impulse noise.
import random as r
def addRvinGray(image,n): # add random valued impulse noise in grayscale
'''parameters:
image: type=numpy array. input image in which you want add noise.
n: noise level (in percentage)'''
k=0 # counter variable
ih=image.shape[0]
iw=image.shape[1]
noisypixels=(ih*iw*n)/100 # here we calculate the number of pixels to be altered.
for i in range(ih*iw):
if k<noisypixels:
image[r.randrange(0,ih)][r.randrange(0,iw)]=r.randrange(0,256) #access random pixel in the image gives random intensity (0-255)
k+=1
else:
break
return image
to add salt and pepper noise
def addSaltGray(image,n): #add salt-&-pepper noise in grayscale image
k=0
salt=True
ih=image.shape[0]
iw=image.shape[1]
noisypixels=(ih*iw*n)/100
for i in range(ih*iw):
if k<noisypixels: #keep track of noise level
if salt==True:
image[r.randrange(0,ih)][r.randrange(0,iw)]=255
salt=False
else:
image[r.randrange(0,ih)][r.randrange(0,iw)]=0
salt=True
k+=1
else:
break
return image
Note: for color images: first split image in to three or four channels depending on the input image using opencv function:
(B, G, R) = cv2.split(image)
(B, G, R, A) = cv2.split(image)
after spliting perform the same operations on all channels.
at the end merge all the channels:
merged = cv2.merge([B, G, R])
return merged