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Image Reconstruction (Erosion and Dilation) both gives black image as output

I am trying to remove the black spots from a face of this image using the erosion methods.
I have implemented:
img = skimage.io.imread('blemish.jpeg')
img = skimage.color.rgb2gray(img)
img_inten = skimage.exposure.rescale_intensity(img,in_range=(50,100))
diliation_seed = img_inten.copy()
diliation_seed[1:-1,1:-1] = img_inten.min()
mask = img_inten
eroded_img = skimage.morphology.reconstruction(diliation_seed,mask,method='dilation')
matplotlib.pyplot.imshow(eroded_img,cmap='gray')
My output is always a black image in both the cases. What is going wrong here?

rgb2gray is outputting an image as a matrix of floats, with values in [0;1]
So the rescale_intensity is just outputting a matrix of 0, since you ask for values between 50 and 100 and there is none in the gray img.
you can fix it like this :
import skimage
from skimage import data, exposure, img_as_float
from skimage.morphology import reconstruction
import matplotlib.pyplot as plt
img = skimage.io.imread('blemish.jpeg')
gray_img = 255*skimage.color.rgb2gray(img) # multiply by 255 to get back in the [0;255] range
img_inten = exposure.rescale_intensity(gray_img,in_range=(50,100))
diliation_seed = img_inten.copy()
diliation_seed[1:-1,1:-1] = img_inten.min()
mask = img_inten
eroded_img = reconstruction(diliation_seed,mask,method='dilation')
plt.imshow(eroded_img,cmap='gray')
plt.show()

Convert 3 channel image to 1 channel python

I am using Unet for segmentation in python and my unet's output is a mask with this shape [512,512,1].
After predicted a mask I want to do f1 score between the predicted mask and the real mask of the test image. I need to convert the real mask from [512,512,3] to [512,512,1] and I just can convert to [512,512].
Can anyone help me?
Image with my outputs

You can use Pillow
from PIL import Image
img = Image.open('image.png').convert('LA')
img.save('greyscale.png')
OR
Using matplotlib and the formula
Y' = 0.2989 R + 0.5870 G + 0.1140 B
you could do:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
def rgb2gray(rgb):
return np.dot(rgb[...,:3], [0.2989, 0.5870, 0.1140])
img = mpimg.imread('image.png')
gray = rgb2gray(img)
plt.imshow(gray, cmap=plt.get_cmap('gray'), vmin=0, vmax=1)
plt.show()
You can refer this answer for more info

I discover the answer. I had to remove one axis
predicted.shape
[512,512,1]
predicted = predicted[:, :,:, 0]
predicted.shape
[512,512]

Speed up image manipulation calculation with numpy

I have a very simple method that converts an RGB image to HSL and adjusts the Hue. This works relatively quickly with small image files, but large image files require minutes to process. I am converting the imgdata to a numpy array, but this does not seem to speed it up at all. Do I have to use numpy functions exclusively inside of the loop to speed this up? I can't find exactly where the bottleneck is inside of the loop as it is just fairly simple math calculations.
from colorsys import rgb_to_hls, hls_to_rgb
from numpy import array
def reload_img():
global img, sizew, sizeh, maxsize, imgdata
img = Image.open(IMAGE_SRC)
sizew, sizeh = img.size
maxsize = ((sizew/2)**2 + (sizeh/2)**2)**0.5
imgdata = list(img.getdata())
# Convert to numpy array
imgdata = array(imgdata)
IMAGE_SRC = "test.jpg"
reload_img()
# Adjust Hue
for i in range(0,len(imgdata)):
r,g,b = imgdata[i]
r /= 255.0
g /= 255.0
b /= 255.0
(h, l, s) = rgb_to_hls(r,g,b)
h = .50
imgdata[i] = hls2rgb((h,l,s))

Here's a fast but not super precise method:
import numpy as np
from PIL import Image
def set_hue(img, hue):
"""
img - a PIL (pillow) image
hue - an integer in the range [0, 255]
Returns a new PIL (pillow) image in HSV mode
"""
hsv = img.convert('HSV')
hsv_ar = np.array(hsv)
hsv_ar[...,0] = hue
out = Image.fromarray(hsv_ar, mode='HSV')
return out
For this to work a somewhat recent version of Pillow (PIL fork) is probably required. It's fast because it uses the buffer protocol to convert between PIL format and Numpy array and vice versa. But the precision of the hue modification is not perfect, because there's an intermediate result with only 24 bits per pixel.
Note that Pillow doesn't have a HSL mode, so I used HSV.

histogram equalization for colored image give error using python opencv

Error : Assertion failed (0 < cn && cn <= CV_CN_MAX) in merge
In the merge function
cv2.merge(channels,img2)
if the arguments are replaced as shown:
cv2.merge(img2,channels)
it will not give an error, but the histograms will be the same before and after equalization. What can I do in this piece of code.
Code:
import cv2,cv
import cv2.cv as cv
import numpy as np
from matplotlib import pyplot as plt
capture = cv.CaptureFromCAM(0)
img = cv.QueryFrame(capture)
img_size = cv.GetSize(img)
width,height = img_size
size = width,height,3
channels = np.zeros(size , np.uint8)
while (1):
img = cv.QueryFrame(capture)
img = np.asarray(img[:,:])
cv2.imshow("original",img)
hist = cv2.calcHist([img],[2],None,[256],[0,256])
#convert img to YCR_CB
img2 = cv2.cvtColor(img,cv2.COLOR_BGR2YCR_CB)
#split image to Y, CR, CB
cv2.split(img2,channels)
#histogram equalization to Y-MATRIX
cv2.equalizeHist(channels[0],channels[0])
#merge this matrix to reconstruct our colored image
cv2.merge(channels,img2)
#convert this output image to rgb
rgb = cv2.cvtColor(img2,cv2.COLOR_YCR_CB2BGR)
hist2 = cv2.calcHist([rgb],[2],None,[256],[0,256])
plt.plot(hist)
plt.plot(hist2)
plt.show()

Instead of using split and merge, take advantage of numpy slicing.
img2[:, :, 0] = cv2.equalizeHist(img2[:, :, 0])
# or run a small loop over each channel

you got the split() wrong here. it returns the channels.
since you don't catch the return values, your channels are not initialized
>>> import cv2
>>> help(cv2.split)
Help on built-in function split in module cv2:
split(...)
split(m[, mv]) -> mv
so it should look like:
channels = cv2.split(img2)
and please, avoid the old cv api, instead stick with cv2 consistently. (use cv2.VideoCapture, not cv.CaptureFromCAM)

how to convert an RGB image to numpy array?

I have an RGB image. I want to convert it to numpy array. I did the following
im = cv.LoadImage("abc.tiff")
a = numpy.asarray(im)
It creates an array with no shape. I assume it is a iplimage object.

You can use newer OpenCV python interface (if I'm not mistaken it is available since OpenCV 2.2). It natively uses numpy arrays:
import cv2
im = cv2.imread("abc.tiff",mode='RGB')
print(type(im))
result:
<type 'numpy.ndarray'>

PIL (Python Imaging Library) and Numpy work well together.
I use the following functions.
from PIL import Image
import numpy as np
def load_image( infilename ) :
img = Image.open( infilename )
img.load()
data = np.asarray( img, dtype="int32" )
return data
def save_image( npdata, outfilename ) :
img = Image.fromarray( np.asarray( np.clip(npdata,0,255), dtype="uint8"), "L" )
img.save( outfilename )
The 'Image.fromarray' is a little ugly because I clip incoming data to [0,255], convert to bytes, then create a grayscale image. I mostly work in gray.
An RGB image would be something like:
out_img = Image.fromarray( ycc_uint8, "RGB" )
out_img.save( "ycc.tif" )

You can also use matplotlib for this.
from matplotlib.image import imread
img = imread('abc.tiff')
print(type(img))
output:
<class 'numpy.ndarray'>

As of today, your best bet is to use:
img = cv2.imread(image_path) # reads an image in the BGR format
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # BGR -> RGB
You'll see img will be a numpy array of type:
<class 'numpy.ndarray'>

Late answer, but I've come to prefer the imageio module to the other alternatives
import imageio
im = imageio.imread('abc.tiff')
Similar to cv2.imread(), it produces a numpy array by default, but in RGB form.

You need to use cv.LoadImageM instead of cv.LoadImage:
In [1]: import cv
In [2]: import numpy as np
In [3]: x = cv.LoadImageM('im.tif')
In [4]: im = np.asarray(x)
In [5]: im.shape
Out[5]: (487, 650, 3)

You can get numpy array of rgb image easily by using numpy and Image from PIL
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
im = Image.open('*image_name*') #These two lines
im_arr = np.array(im) #are all you need
plt.imshow(im_arr) #Just to verify that image array has been constructed properly

When using the answer from David Poole I get a SystemError with gray scale PNGs and maybe other files. My solution is:
import numpy as np
from PIL import Image
img = Image.open( filename )
try:
data = np.asarray( img, dtype='uint8' )
except SystemError:
data = np.asarray( img.getdata(), dtype='uint8' )
Actually img.getdata() would work for all files, but it's slower, so I use it only when the other method fails.

load the image by using following syntax:-
from keras.preprocessing import image
X_test=image.load_img('four.png',target_size=(28,28),color_mode="grayscale"); #loading image and then convert it into grayscale and with it's target size
X_test=image.img_to_array(X_test); #convert image into array

OpenCV image format supports the numpy array interface. A helper function can be made to support either grayscale or color images. This means the BGR -> RGB conversion can be conveniently done with a numpy slice, not a full copy of image data.
Note: this is a stride trick, so modifying the output array will also change the OpenCV image data. If you want a copy, use .copy() method on the array!
import numpy as np
def img_as_array(im):
"""OpenCV's native format to a numpy array view"""
w, h, n = im.width, im.height, im.channels
modes = {1: "L", 3: "RGB", 4: "RGBA"}
if n not in modes:
raise Exception('unsupported number of channels: {0}'.format(n))
out = np.asarray(im)
if n != 1:
out = out[:, :, ::-1] # BGR -> RGB conversion
return out

I also adopted imageio, but I found the following machinery useful for pre- and post-processing:
import imageio
import numpy as np
def imload(*a, **k):
i = imageio.imread(*a, **k)
i = i.transpose((1, 0, 2)) # x and y are mixed up for some reason...
i = np.flip(i, 1) # make coordinate system right-handed!!!!!!
return i/255
def imsave(i, url, *a, **k):
# Original order of arguments was counterintuitive. It should
# read verbally "Save the image to the URL" — not "Save to the
# URL the image."
i = np.flip(i, 1)
i = i.transpose((1, 0, 2))
i *= 255
i = i.round()
i = np.maximum(i, 0)
i = np.minimum(i, 255)
i = np.asarray(i, dtype=np.uint8)
imageio.imwrite(url, i, *a, **k)
The rationale is that I am using numpy for image processing, not just image displaying. For this purpose, uint8s are awkward, so I convert to floating point values ranging from 0 to 1.
When saving images, I noticed I had to cut the out-of-range values myself, or else I ended up with a really gray output. (The gray output was the result of imageio compressing the full range, which was outside of [0, 256), to values that were inside the range.)
There were a couple other oddities, too, which I mentioned in the comments.

We can use following function of open CV2 to convert BGR 2 RGB format.
RBG_Image = cv2.cvtColor(Image, cv.COLOR_BGR2RGB)

Using Keras:
from keras.preprocessing import image
img = image.load_img('path_to_image', target_size=(300, 300))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
images = np.vstack([x])

Try timing the options to load an image to numpy array, they are quite similar. Go for plt.imread for simplicity and speed.
def time_this(function, times=100):
cum_time = 0
for t in range(times):
st = time.time()
function()
cum_time += time.time() - st
return cum_time / times
import matplotlib.pyplot as plt
def load_img_matplotlib(img_path):
return plt.imread(img_path)
import cv2
def load_img_cv2(img_path):
return cv2.cvtColor(cv2.imread(img_path), cv2.COLOR_BGR2RGB)
from PIL import Image
import numpy as np
def load_img_pil(img_path):
img = Image.open(img_path)
img.load()
return np.asarray( img, dtype="int32" )
if __name__=='__main__':
img_path = 'your_image_path'
for load_fn in [load_img_pil, load_img_cv2, load_img_matplotlib]:
print('-'*20)
print(time_this(lambda: load_fn(img_path)), 10000)
Result:
--------------------
0.0065201687812805175 10000 PIL, as in [the second answer][1]https://stackoverflow.com/a/7769424/16083419)
--------------------
0.0053211402893066405 10000 CV2
--------------------
0.005320906639099121 10000 matplotlib

You can try the following method. Here is a link to the docs.
tf.keras.preprocessing.image.img_to_array(img, data_format=None, dtype=None)
from PIL import Image
img_data = np.random.random(size=(100, 100, 3))
img = tf.keras.preprocessing.image.array_to_img(img_data)
array = tf.keras.preprocessing.image.img_to_array(img)