- How to convert gray to rgb python implementation
- Convert grayscale to rgb python
- PYTHON : How does one convert a grayscale image to RGB in
- Convert RGB image to grayscale in python opencv (2019)
- Convert RGB Images to Grayscale image from scratch using python
- How to turn grayscale image into RGB?
- Grayscale to RGB
- Greyscale Image python Implementation
- Convert grayscale to rgb python
- Answer by Harmony Lloyd
- Answer by Addison Villalobos
- Answer by Kayla Golden
- View aliases
- Answer by Guadalupe Kline
- Answer by Deandre Roth
- Answer by Roy Fuentes
- Example: opencv grayscale to rgb
How to convert gray to rgb python implementation
Observe the output from code below: Output: As you can see, with the image of a red, green and blue shape (each a specific shade of its color) , converting it into grayscale results in the three colors turning into one; . Using this grayscale conversion formula: Method #1: : Before After Method #2: You could use OpenCV directly and read in the image as grayscale with by passing in the or flag to load the image as grayscale.
Convert grayscale to rgb python
import cv2 image = cv2.imread('C:/Users/N/Desktop/Test.jpg') gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) cv2.imshow('Original image',image) cv2.imshow('Gray image', gray) cv2.waitKey(0) cv2.destroyAllWindows()
backtorgb = cv2.cvtColor(gray,cv2.COLOR_GRAY2RGB)
from PIL import Image img = Image.open('image.png').convert('LA') img.save('greyscale.png')
Python — How to convert a RGB image (3 channel) to grayscale (1, Your first code block: import matplotlib.pyplot as plt plt.imsave(‘image.png’, image, format=’png’, cmap=’gray’). This is saving the image
PYTHON : How does one convert a grayscale image to RGB in
Convert RGB image to grayscale in python opencv (2019)
Topic: python tutorials: convert RGB image to grayscale in python OpenCV (2019).In this
Duration: 7:51
Convert RGB Images to Grayscale image from scratch using python
How to turn grayscale image into RGB?
When you reduce a color image to grayscale, you’re discarding information. There’s no way to get color back. If you want to get an acceptable frame rate, you’re going to have to choose some other approach.
When you convert an RGB image to grayscale, color data gets thrown away, hence you won’t be able to get the original image back. Observe the output from code below:
import cv2 import numpy as np # Create image img = np.full((500, 500, 3), 255, 'uint8') cv2.rectangle(img, (50, 100), (250, 300), (0, 0, 96), -1) cv2.circle(img, (300, 350), 100, (0, 50, 0), -1) cv2.drawContours(img, [np.array([(300, 50), (200, 250), (400, 250)])], 0, (255, 0, 0), -1) # Convert to grayscale img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) print(np.unique(img_gray)) # Show images cv2.imshow("BGR", img) cv2.imshow("Gray", img_gray) cv2.waitKey(0)
As you can see, with the image of a red, green and blue shape (each a specific shade of its color) , converting it into grayscale results in the three colors turning into one; (29, 29, 29) . There is no way the computer will be able to tell that the three shapes used to be different colors.
PYTHON : How does one convert a grayscale image to RGB in, PYTHON : How does one convert a grayscale image to RGB in OpenCV (Python)? [ Gift Duration: 1:21
Grayscale to RGB
I think tat problem is that your image is not real grayscale. It is RGB, but visible as grayscale. So need to bring one channel from image and then run the code:
backtorgb = cv2.cvtColor(img[. 0], cv2.COLOR_GRAY2RGB)
By default, imread opens the image as a 3-channel BGR image so you don’t need to convert it, maybe just to RGB if that’s what you’re looking for.
Check out the documentation here
The problem was on the application of the CLAHE filter and its grayscale output, actually the output kept the 3 channels but at the sight it looked like a grayscale, documentation here.
I simply found here a method that kept the RGB form to solve the problem, thanks to everyone for the answers.
Python | Grayscaling of Images using OpenCV, Grayscaling is the process of converting an image from other color spaces e.g. RGB, CMYK, HSV, etc. to shades of gray. It varies between complete black and
Greyscale Image python Implementation
You should avoid using for loops when performing image processing since it is very slow. Instead you can use Numpy which is highly optimized for vector operations. Using this grayscale conversion formula:
gray = R * .299 + G * .587 + B * .114
Method #1: apply_along_axis :
import cv2 import numpy as np def grayscale(colors): r, g, b = colors return 0.299 * r + 0.587 * g + 0.114 * b # Create image of size 100x100 of random pixels # Convert to grayscale image = np.random.randint(255, size=(100,100,3),dtype=np.uint8) gray = np.apply_along_axis(grayscale, 2, image) # Display cv2.imshow('image', image) cv2.imshow('gray', gray) cv2.waitKey()
Method #2: cv2.cvtColor
You could use OpenCV directly and read in the image as grayscale with cv2.imread by passing in the cv2.IMREAD_GRAYSCALE or 0 flag to load the image as grayscale.
image = cv2.imread('img.png', cv2.IMREAD_GRAYSCALE) # OR # image = cv2.imread('img.png', 0)
If you already have the image loaded, you can convert the RGB or BGR image to grayscale using cv2.cvtColor
image = cv2.imread('img.png') gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
Assuming you are using a for loop, because you intent to solve it «manually» (like C code), there are number of issues with your implementation:
- The assignment grayimg = img in Python does not create a copy of img (the result is that grayimg referencing img ).
You meant to use: grayimg = img.copy() . - img has 3 dimensions, so when using grayimg = img , grayimg also has 3 dimensions.
You need to create grayimg with two dimensions.
Example for creating grayimg and initialize to zeros:
grayimg = np.zeros((height, width), img.dtype)
Here is a corrected version of RGBtoGRAY :
def RGBtoGRAY(img): height, width, channels = img.shape #grayimg = img # Create height x width array with same type of img, and initialize with zeros. grayimg = np.zeros((height, width), img.dtype) for i in range(height): for j in range(width): grayimg[i,j] = 0.3 * img[i,j][0] + 0.59 * img[i,j][1] + 0.11 * img[i,j][2] return grayimg
Mahotas — RGB to Gray Conversion, Mahotas – RGB to Gray Conversion In this article we will see how we can convert rgb image to gray in Below is the implementation.
Convert grayscale to rgb python
You could draw in the original ‘frame’ itself instead of using gray image., 4 You could draw in the original ‘frame’ itself instead of using gray image – Anoop K. Prabhu Feb 6 ’14 at 7:43 ,I am promoting my comment to an answer:,Next we convert it to grayscale and create another image using cv2.merge() with three gray channels
This is because you’re trying to display three channels on a single channel image. To fix this, you can simply merge the three single channels
image = cv2.imread('image.png') gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) gray_three = cv2.merge([gray,gray,gray])
image = (np.random.standard_normal([200,200,3]) * 255).astype(np.uint8)
Next we convert it to grayscale and create another image using cv2.merge() with three gray channels
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) gray_three = cv2.merge([gray,gray,gray])
contour = np.array([[10,10], [190, 10], [190, 80], [10, 80]]) cv2.fillPoly(gray, [contour], [36,255,12]) cv2.fillPoly(gray_three, [contour], [36,255,12])
import cv2 import numpy as np # Create random color image image = (np.random.standard_normal([200,200,3]) * 255).astype(np.uint8) # Convert to grayscale (1 channel) gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Merge channels to create color image (3 channels) gray_three = cv2.merge([gray,gray,gray]) # Fill a contour on both the single channel and three channel image contour = np.array([[10,10], [190, 10], [190, 80], [10, 80]]) cv2.fillPoly(gray, [contour], [36,255,12]) cv2.fillPoly(gray_three, [contour], [36,255,12]) cv2.imshow('image', image) cv2.imshow('gray', gray) cv2.imshow('gray_three', gray_three) cv2.waitKey()
Answer by Harmony Lloyd
Data pre-processing is critical for computer vision applications, and properly converting grayscale images to the RGB format expected by current deep learning frameworks is an essential technique. What does that mean?,So trying to ingest your grayscale with many computer vision / deep learning pipelines relying on transfer learning from a standard commodity model such as Resnet18 or -34 will result in a variety of errors.,Former NSA analyst working on compliance & security automation in the cloud. Background in applied mathematics, student of machine learning & neural nets.,Quoting the Pytorch documentation:¹ All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB images of shape (3 x H x W).
Using the matplotlib library, let’s look at a color (RGB) image:
img = plt.imread('whales/547b59eec.jpg')plt.imshow(img)print(img.shape)(525, 1050, 3)
The img object is , so let’s look at the shape and values of each layer:
#valuesprint(img[524][1049][2])198print(img[524][1049])[155 177 198]print(img[524])[[ 68 107 140] [ 76 115 148] [ 76 115 148] [ 75 114 147] . [171 196 216] [171 193 214] [171 193 214] [155 177 198]]
The obvious (and less-than-correct) way is to add two arrays of zeros of the same size:
dim = np.zeros((28,28))R = np.stack((O,dim, dim), axis=2)
Answer by Addison Villalobos
backtorgb = cv2.cvtColor(gray,cv2.COLOR_GRAY2RGB)
Answer by Kayla Golden
TensorFlow Core v2.6.0 , TensorFlow Core v2.6.0 ,tf.compat.v1.image.grayscale_to_rgb, Why TensorFlow More
View aliases
Compat aliases for migration
tf.image.grayscale_to_rgb( images, name=None )
Outputs a tensor of the same DType and rank as images . The size of the last dimension of the output is 3, containing the RGB value of the pixels. The input images’ last dimension must be size 1.
original = tf.constant([[[1.0], [2.0], [3.0]]]) converted = tf.image.grayscale_to_rgb(original) print(converted.numpy()) [[[1. 1. 1.] [2. 2. 2.] [3. 3. 3.]]]
Answer by Guadalupe Kline
Early in the program I used gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) to convert from RGB to grayscale, but to go back I’m confused, and the function backtorgb = cv2.cvtColor(gray,cv2.CV_GRAY2RGB) is giving:,The code below does not appear to be drawing contours in green. Is this because it’s a grayscale image? If so, can I convert the grayscale image back to RGB to visualize the contours in green?,Next we convert it to grayscale and create another image using cv2.merge() with three gray channels,backtorgb = cv2.cvtColor(gray,cv2.COLOR_GRAY2RGB) is the correct syntax.
The code below does not appear to be drawing contours in green. Is this because it’s a grayscale image? If so, can I convert the grayscale image back to RGB to visualize the contours in green?
import numpy as np import cv2 import time cap = cv2.VideoCapture(0) while(cap.isOpened()): ret, frame = cap.read() gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) ret, gb = cv2.threshold(gray,128,255,cv2.THRESH_BINARY) gb = cv2.bitwise_not(gb) contour,hier = cv2.findContours(gb,cv2.RETR_CCOMP,cv2.CHAIN_APPROX_SIMPLE) for cnt in contour: cv2.drawContours(gb,[cnt],0,255,-1) gray = cv2.bitwise_not(gb) cv2.drawContours(gray,contour,-1,(0,255,0),3) cv2.imshow('test', gray) if cv2.waitKey(1) & 0xFF == ord('q'): break cap.release() cv2.destroyAllWindows()
Answer by Deandre Roth
Dimension reduction: For example, In RGB images there are three color channels and has three dimensions while grayscale images are single-dimensional.,Let’s learn the different image processing methods to convert a colored image into a grayscale image.,Hope you have understood the above discussed image processing techniques to convert a colored image into a grayscale image in Python!,Python program to convert a list to string
Answer by Roy Fuentes
Example: opencv grayscale to rgb
backtorgb = cv2.cvtColor(gray,cv2.COLOR_GRAY2RGB)