使用OpenCV自动去除背景色

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

import cv2import osimport stringimport randomfrom os import listdirfrom os.path import isfile, join, splitextimport timeimport sysimport numpy as npimport 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))