from PIL import Image, ImageEnhance def increase_contrast(image_path, contrast_factor): """ 增加图像的对比度。 :param image_path: 图像文件的路径。 :param contrast_factor: 对比度因子(大于1.0表示增加对比度)。 :return: 对比度增加后的图像对象。 """ # 打开图像 img = Image.open(image_path) ...
import cv2 import os def increase_brightness(input_folder, output_folder, brightness_factor): # 创建输出文件夹 os.makedirs(output_folder, exist_ok=True) # 遍历输入文件夹中的所有图像文件 for filename in os.listdir(input_folder): if filename.endswith(('.jpg', '.jpeg', '.png')): # 读...
接下来,我们将使用以下代码示例来增加图像的对比度: importcv2importnumpyasnpdefincrease_contrast(image,alpha,beta):new_image=np.zeros(image.shape,image.dtype)foryinrange(image.shape[0]):forxinrange(image.shape[1]):forcinrange(image.shape[2]):new_image[y,x,c]=np.clip(alpha*image[y,x,c]...
Args: response (requests.Response): The response object containing the image data. Returns: dict: A dict of image quality metrics including brightness, sharpness, contrast, and colorfulness. """ image_array = np.frombuffer(response.content, np.uint8) image = cv2.imdecode(image_array, cv2....
In this tutorial, you'll learn how to use NumPy reshape() to rearrange the data in an array. You'll learn to increase and decrease the number of dimensions and to configure the data in the new array to suit your requirements.
Args: response (requests.Response): The response object containing the image data. Returns: dict: A dict of image quality metrics including brightness, sharpness, contrast, and colorfulness. """ image_array = np.frombuffer(response.content, np.uint8) image = cv2.imdecode(image_array, cv2....
The result (with auto-contrast applied) shows that the noise is gone, a very large improvement from the original image. However, we still notice some strange artifacts, such as the greenish frame and the gridlike pattern. This time, it’s not a random noise, but a fixed pattern noise. ...
Future versions of the image may come with slightly different system packages, potentially causing problems. The -it option in the docker run command instructs Docker to run your new container in an interactive mode. This lets you type commands as if you logged in to a remote server. The -...
This transform flattens the graylevel histogram of an image so that all intensities are as equally common as possible. This is often a good way to normalize image intensity before further processing and also a way to increase image contrast. The transform function is, in this case, a ...
count() collector.increase() assert collector.is_counting_2()DiagramsAdditional Keywords:title (optional): Sets the title of the generated image. show_conditions (default False): Shows conditions at transition edges show_auto_transitions (default False): Shows auto transitions in graph show_state_...