I have a Python code that identifies dark images:

import os import glob import cv2 import numpy as np def isbright(image, dim=10, thresh=0.16): # Resize image to 10x10 image = cv2.resize(image, (dim, dim)) # Convert color space to LAB format and extract L channel L, A, B = cv2.split(cv2.cvtColor(image, cv2.COLOR_BGR2LAB)) # Normalize L channel by dividing all pixel values with maximum pixel value L = L/np.max(L) # Return True if mean is greater than thresh else False return np.mean(L) > thresh # create output directories if not exists os.makedirs("output/bright", exist_ok=True) os.makedirs("output/dark", exist_ok=True) # iterate through images directory for i, path in enumerate(os.listdir(os.path.abspath(''))): # load image from path image = cv2.imread(path) # find if image is bright or dark path = os.path.basename(path) text = "bright" if isbright(image) else "dark" # save image to disk cv2.imwrite("output/{}/{}".format(text, path), image) print(path, "=>", text) 

I'd like to also identify, for example, mostly red images, mostly yellow images and so on. Basically mostly uniform colors in pictures keeping the same code structure, more or less. How would you guys do it?

I have a Python code that identifies dark images:

import os import glob import cv2 import numpy as np def isbright(image, dim=10, thresh=0.16): # Resize image to 10x10 image = cv2.resize(image, (dim, dim)) # Convert color space to LAB format and extract L channel L, A, B = cv2.split(cv2.cvtColor(image, cv2.COLOR_BGR2LAB)) # Normalize L channel by dividing all pixel values with maximum pixel value L = L/np.max(L) # Return True if mean is greater than thresh else False return np.mean(L) > thresh # create output directories if not exists os.makedirs("output/bright", exist_ok=True) os.makedirs("output/dark", exist_ok=True) # iterate through images directory for i, path in enumerate(os.listdir(os.path.abspath(''))): # load image from path image = cv2.imread(path) # find if image is bright or dark path = os.path.basename(path) text = "bright" if isbright(image) else "dark" # save image to disk cv2.imwrite("output/{}/{}".format(text, path), image) print(path, "=>", text) 

I'd like to also identify, for example, mostly red images, mostly yellow images and so on. Basically mostly uniform colors in pictures keeping the same code structure, more or less. How would you guys do it?

Samples:

I have a Python code that identifies dark images:

import os import glob import cv2 import numpy as np def isbright(image, dim=10, thresh=0.16): # Resize image to 10x10 image = cv2.resize(image, (dim, dim)) # Convert color space to LAB format and extract L channel L, A, B = cv2.split(cv2.cvtColor(image, cv2.COLOR_BGR2LAB)) # Normalize L channel by dividing all pixel values with maximum pixel value L = L/np.max(L) # Return True if mean is greater than thresh else False return np.mean(L) > thresh # create output directories if not exists os.makedirs("output/bright", exist_ok=True) os.makedirs("output/dark", exist_ok=True) # iterate through images directory for i, path in enumerate(os.listdir(os.path.abspath(''))): # load image from path image = cv2.imread(path) # find if image is bright or dark path = os.path.basename(path) text = "bright" if isbright(image) else "dark" # save image to disk cv2.imwrite("output/{}/{}".format(text, path), image) print(path, "=>", text) 

I'd like to also identify, for example, mostly red images, mostly yellow images and so on. Basically mostly uniform colors in pictures keeping the same code structure, more or less. How you guys do it?

I have a Python code that identifies dark images:

import os import glob import cv2 import numpy as np def isbright(image, dim=10, thresh=0.16): # Resize image to 10x10 image = cv2.resize(image, (dim, dim)) # Convert color space to LAB format and extract L channel L, A, B = cv2.split(cv2.cvtColor(image, cv2.COLOR_BGR2LAB)) # Normalize L channel by dividing all pixel values with maximum pixel value L = L/np.max(L) # Return True if mean is greater than thresh else False return np.mean(L) > thresh # create output directories if not exists os.makedirs("output/bright", exist_ok=True) os.makedirs("output/dark", exist_ok=True) # iterate through images directory for i, path in enumerate(os.listdir(os.path.abspath(''))): # load image from path image = cv2.imread(path) # find if image is bright or dark path = os.path.basename(path) text = "bright" if isbright(image) else "dark" # save image to disk cv2.imwrite("output/{}/{}".format(text, path), image) print(path, "=>", text) 

I'd like to also identify, for example, mostly red images, mostly yellow images and so on. Basically mostly uniform colors in pictures keeping the same code structure, more or less. How would you guys do it?