python屏幕识别训练

要实现Python屏幕识别训练，可以使用Python的图像处理库和机器学习库。以下是一个简单的示例代码，可以识别屏幕上的数字：

import numpy as np
import cv2
import os

# 读取数字图片数据集
dataset_path = 'digits_dataset'
dataset = []
for filename in os.listdir(dataset_path):
    if filename.endswith('.png'):
        img = cv2.imread(os.path.join(dataset_path, filename), cv2.IMREAD_GRAYSCALE)
        img = cv2.resize(img, (20, 20))
        dataset.append((img, int(filename[0])))

# 拆分数据集为训练集和测试集
np.random.shuffle(dataset)
train_set = dataset[:800]
test_set = dataset[800:]

# 训练模型
X_train = np.array([img.flatten() for img, label in train_set])
y_train = np.array([label for img, label in train_set])
X_test = np.array([img.flatten() for img, label in test_set])
y_test = np.array([label for img, label in test_set])

model = cv2.ml.KNearest_create()
model.train(X_train, cv2.ml.ROW_SAMPLE, y_train)

# 测试模型
test_results = model.predict(X_test)
accuracy = np.mean(test_results[1] == y_test)
print('Accuracy:', accuracy)

# 使用模型识别屏幕上的数字
while True:
    screenshot = np.array(pyautogui.screenshot())
    screenshot_gray = cv2.cvtColor(screenshot, cv2.COLOR_BGR2GRAY)
    screenshot_resized = cv2.resize(screenshot_gray, (200, 200))
    _, screenshot_binary = cv2.threshold(screenshot_resized, 127, 255, cv2.THRESH_BINARY)

    for x in range(0, 180, 20):
        for y in range(0, 180, 20):
            digit = screenshot_binary[y:y+20, x:x+20]
            digit_flattened = digit.flatten()
            _, result, _, _ = model.findNearest(digit_flattened.reshape(1, -1), 1)
            print(int(result[0][0]), end=' ')
        print()

    cv2.imshow('Screenshot', screenshot_binary)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cv2.destroyAllWindows()

需要注意的是，这个示例代码只是一个简单的示例，实际情况可能会更复杂。例如，数字图片数据集可能需要自己手动准备，模型的训练参数可能需要调整，等等。具体实现方式需要根据具体情况进行调整。