使用OpenCV自动去除背景色

opencv_python==4.1.0.25
pip install opencv-python
numpy==1.16.4
pip install numpy

import cv2
import os
import string
import random
from os import listdir
from os.path import isfile, join, splitext
import time
import sys
import numpy as np
import argparse

watch_folder = ‘toprocess’
processed_folder = ‘processed’
poll_time = 1

before = dict([(f, None) for f in os.listdir(watch_folder)])
while 1:
  time.sleep(poll_time)
  after = dict([(f, None) for f in os.listdir(watch_folder)])
  added = [f for f in after if not f in before]
  removed = [f for f in before if not f in after]
  if added:
    print(“Added “, “, “.join(added))
  if added[0] is not None:
    processImage(added[0])
  if removed:
    print(“Removed “, “, “.join(removed))
  before = after

def processImage(fileName):
  # Load in the image using the typical imread function using our watch_folder path, and the fileName passed in, then set the final output image to our current image for now
  image = cv2.imread(watch_folder + ‘/’ + fileName)
  output = image
  # Set thresholds. Here, we are using the Hue, Saturation, Value color space model. We will be using these values to decide what values to show in the ranges using a minimum and maximum value. 
THESE VALUES CAN BE PLAYED AROUND FOR DIFFERENT COLORS
  hMin = 29  # Hue minimum
  sMin = 30  # Saturation minimum
  vMin = 0   # Value minimum (Also referred to as brightness)
  hMax = 179 # Hue maximum
  sMax = 255 # Saturation maximum
  vMax = 255 # Value maximum
  # Set the minimum and max HSV values to display in the output image using numpys' array function. We need the numpy array since OpenCVs' inRange function will use those.
  lower = np.array([hMin, sMin, vMin])
  upper = np.array([hMax, sMax, vMax])
  # Create HSV Image and threshold it into the proper range.
  hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) # Converting color space from BGR to HSV
  mask = cv2.inRange(hsv, lower, upper) # Create a mask based on the lower and upper range, using the new HSV image
  # Create the output image, using the mask created above. This will perform the removal of all unneeded colors, but will keep a black background.
  output = cv2.bitwise_and(image, image, mask=mask)
  # Add an alpha channel, and update the output image variable
  *_, alpha = cv2.split(output)
  dst = cv2.merge((output, alpha))
  output = dst
  # Resize the image to 512, 512 (This can be put into a variable for more flexibility), and update the output image variable.
  dim = (512, 512)
  output = cv2.resize(output, dim)
  # Generate a random file name using a mini helper function called randomString to write the image data to, and then save it in the processed_folder path, using the generated filename.
  file_name = randomString(5) + ‘.png’
  cv2.imwrite(processed_folder + ‘/’ + file_name, output)

def randomString(length):
  letters = string.ascii_lowercase
  return ‘’.join(random.choice(letters) for i in range(length))