基于Opencv实现眼睛控制鼠标

# For monitoring web camera and performing image minipulationsimport cv2# For performing array operationsimport numpy as np# For creating and removing directoriesimport osimport shutil# For recognizing and performing actions on mouse pressesfrom pynput.mouse import Listener

from pynput.mouse import Listenerdef on_click(x, y, button, pressed):"""  Args:    x: the x-coordinate of the mouse    y: the y-coordinate of the mouse    button: 1 or 0, depending on right-click or left-click    pressed: 1 or 0, whether the mouse was pressed or released  """if pressed:print (x, y)with Listener(on_click = on_click) as listener:  listener.join()

import cv2# Load the cascade classifier detection objectcascade = cv2.CascadeClassifier("haarcascade_eye.xml")# Turn on the web cameravideo_capture = cv2.VideoCapture(0)# Read data from the web camera (get the frame)_, frame = video_capture.read()# Convert the image to grayscalegray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)# Predict the bounding box of the eyesboxes = cascade.detectMultiScale(gray, 1.3, 10)# Filter out images taken from a bad angle with errors# We want to make sure both eyes were detected, and nothing elseif len(boxes) == 2:eyes = []for box in boxes:    # Get the rectangle parameters for the detected eyex, y, w, h = box    # Crop the bounding box from the frameeye = frame[y:y + h, x:x + w]    # Resize the crop to 32x32eye = cv2.resize(eye, (32, 32))    # Normalizeeye = (eye - eye.min()) / (eye.max() - eye.min())    # Further crop to just around the eyeballeye = eye[10:-10, 5:-5]    # Scale between [0, 255] and convert to int datatypeeye = (eye * 255).astype(np.uint8)    # Add the current eye to the list of 2 eyeseyes.append(eye)  # Concatenate the two eye images into oneeyes = np.hstack(eyes)

def normalize(x):  minn, maxx = x.min(), x.max()return (x - minn) / (maxx - minn)

def scan(image_size=(32, 32)):_, frame = video_capture.read()gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)boxes = cascade.detectMultiScale(gray, 1.3, 10)if len(boxes) == 2:eyes = []for box in boxes:x, y, w, h = boxeye = frame[y:y + h, x:x + w]eye = cv2.resize(eye, image_size)eye = normalize(eye)eye = eye[10:-10, 5:-5]eyes.append(eye)return (np.hstack(eyes) * 255).astype(np.uint8)else:return None

def on_click(x, y, button, pressed):# If the action was a mouse PRESS (not a RELEASE)if pressed:# Crop the eyes    eyes = scan()# If the function returned None, something went wrongif not eyes is None:# Save the image      filename = root + "{} {} {}.jpeg".format(x, y, button)      cv2.imwrite(filename, eyes)

import cv2import numpy as npimport osimport shutilfrom pynput.mouse import Listener
root = input("Enter the directory to store the images: ")if os.path.isdir(root):  resp = ""while not resp in ["Y", "N"]:    resp = input("This directory already exists. If you continue, the contents of the existing directory will be deleted. If you would still like to proceed, enter [Y]. Otherwise, enter [N]: ")if resp == "Y":     shutil.rmtree(root)else:    exit()os.mkdir(root)
# Normalization helper functiondef normalize(x):  minn, maxx = x.min(), x.max()return (x - minn) / (maxx - minn)
# Eye cropping functiondef scan(image_size=(32, 32)):  _, frame = video_capture.read()  gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)  boxes = cascade.detectMultiScale(gray, 1.3, 10)if len(boxes) == 2:    eyes = []for box in boxes:      x, y, w, h = box      eye = frame[y:y + h, x:x + w]      eye = cv2.resize(eye, image_size)      eye = normalize(eye)      eye = eye[10:-10, 5:-5]      eyes.append(eye)return (np.hstack(eyes) * 255).astype(np.uint8)else:return None
def on_click(x, y, button, pressed):# If the action was a mouse PRESS (not a RELEASE)if pressed:# Crop the eyes    eyes = scan()# If the function returned None, something went wrongif not eyes is None:# Save the image      filename = root + "{} {} {}.jpeg".format(x, y, button)      cv2.imwrite(filename, eyes)
cascade = cv2.CascadeClassifier("haarcascade_eye.xml")video_capture = cv2.VideoCapture(0)
with Listener(on_click = on_click) as listener:  listener.join()

import numpy as npimport osimport cv2import pyautoguifrom tensorflow.keras.models import *from tensorflow.keras.layers import *from tensorflow.keras.optimizers import *

cascade = cv2.CascadeClassifier("haarcascade_eye.xml")video_capture = cv2.VideoCapture(0)

def normalize(x):  minn, maxx = x.min(), x.max()return (x - minn) / (maxx - minn)

def scan(image_size=(32, 32)):_, frame = video_capture.read()gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)boxes = cascade.detectMultiScale(gray, 1.3, 10)if len(boxes) == 2:eyes = []for box in boxes:x, y, w, h = boxeye = frame[y:y + h, x:x + w]eye = cv2.resize(eye, image_size)eye = normalize(eye)eye = eye[10:-10, 5:-5]eyes.append(eye)return (np.hstack(eyes) * 255).astype(np.uint8)else:return None

# Note that there are actually 2560x1440 pixels on my screen# I am simply recording one less, so that when we divide by these# numbers, we will normalize between 0 and 1. Note that mouse# coordinates are reported starting at (0, 0), not (1, 1)width, height = 2559, 1439

filepaths = os.listdir(root)X, Y = [], []for filepath in filepaths:x, y, _ = filepath.split(' ')x = float(x) / widthy = float(y) / heightX.append(cv2.imread(root + filepath))Y.append([x, y])X = np.array(X) / 255.0Y = np.array(Y)print (X.shape, Y.shape)

model = Sequential()model.add(Conv2D(32, 3, 2, activation = 'relu', input_shape = (12, 44, 3)))model.add(Conv2D(64, 2, 2, activation = 'relu'))model.add(Flatten())model.add(Dense(32, activation = 'relu'))model.add(Dense(2, activation = 'sigmoid'))model.compile(optimizer = "adam", loss = "mean_squared_error")model.summary()

epochs = 200for epoch in range(epochs):  model.fit(X, Y, batch_size = 32)

while True:  eyes = scan()if not eyes is None:      eyes = np.expand_dims(eyes / 255.0, axis = 0)      x, y = model.predict(eyes)[0]      pyautogui.moveTo(x * width, y * height)