python批量写入图片到excel中

基于opencv使用python实现人脸识别与视频生成

概述

OpenCV是一个基于Apache2.0许可（开源）发行的跨平台计算机视觉和机器学习软件库，可以运行在Linux、Windows、Android和Mac OS操作系统上。

OpenCV由一系列C函数和少量C++类构成，同时提供了Python、Ruby、MATLAB等语言的接口，实现了图像处理和计算机视觉方面的很多通用算法，主要倾向于实时视觉应用。

基础用法

• cv2.imread()：读入图像。第一个参数为图像路径，第二个参数指定读入图像的颜色，例如cv2.IMREAD_COLOR表示读入彩色图像，cv2.IMREAD_GRAYSCALE表示读入灰度图像。
• cv2.imshow()：显示图像。第一个参数是窗口的名字，第二个参数是要显示的图像。
• cv2.VideoCapture()：获取摄像头视频或者读取视频文件。参数可以是设备的索引号，也可以是视频文件的路径。
• cv2.imwrite()：保存图像。第一个参数是文件名，包括文件路径和文件扩展名，第二个参数是要保存的图像。
• cv2.cvtColor()：转换图像颜色空间。例如将彩色图像转换为灰度图像。

实际案例

图像转视频

图像尺寸统一化

@logger.catch
def image_fill_black(target_dir, image_path):
    """
    循环处理输入图像：如果输入尺寸大于输出尺寸则：缩小；如果输入尺寸小于输出尺寸，则用黑边填充。尺寸相等 输出图像
    :param target_dir:输出图像路径
    :param image_path:输入图像路径
    :return:是否转换成功
    """
    # Step 1： import opencv lib
    import cv2
    try:
        # Step 2: Define the target size
        target_size = (output_video_width, output_video_height)
        # 读取图片
        img = cv2.imread(image_path)
        # 检查图片尺寸
        height, width = img.shape[:2]
        border_width = 0
        border_height = 0
        grap_width = 0
        grap_height = 0
        while True:
            if width < target_size[0] and height < target_size[1]:
                if width < target_size[0]:
                    border_width = abs(target_size[0] - width) // 2
                    grap_width = output_video_width - (border_width * 2) - width
                elif width == target_size[0]:
                    border_width = 0
                    grap_width = 0
                if height < target_size[1]:
                    border_height = abs(target_size[1] - height) // 2
                    grap_height = output_video_height - (border_height * 2) - height
                elif height == target_size[1]:
                    border_height = 0
                    grap_height = 0
                border = border_width, border_height
                img = cv2.copyMakeBorder(img, border[1], border[1] + grap_height, border[0], border[0] + grap_width,
                                         cv2.BORDER_CONSTANT, value=[0, 0, 0])
                out_height, out_width = img.shape[:2]
                if out_height == output_video_height and out_width == output_video_width:
                    break
                else:
                    continue
            elif width > target_size[0] or height > target_size[1]:
                img = cv2.resize(img, target_size, interpolation=cv2.INTER_LINEAR)
                out_height, out_width = img.shape[:2]
                if out_height == output_video_height and out_width == output_video_width:
                    break
                else:
                    continue
            else:
                logger.info(f"Image size matches the target size: {image_path}")
                break
        file_path, file_name = os.path.split(image_path)
        cv2.imwrite(os.path.join(target_dir, "result_" + file_name), img)
        cv2.waitKey(0)
        cv2.destroyAllWindows()
    except AttributeError as uie:
        logger.error("error, AttributeError: 'NoneType' object has no attribute 'shape'! detail: " + str(uie) +
                     ", ""file_name or path: " + str(image_path))
        return False
    except Exception as e:
        logger.error("error, unknown error! detail: " + str(e) + ", file_name or path: " + str(image_path))
        return False
    return True

上述代码讲不同尺寸图像转换为统一目标尺寸，过小用黑边填充，过大进行缩放。

图像转为视频

@logger.catch
def generate_video_from_images(images_input_path, video_out_path):
    """
    generate video from images list
    :param images_input_path:
    :param video_out_path:
    :return:
    """
    image_paths = []
    for root, dirs, files in os.walk(images_input_path):
        for file in files:
            if "square" in file or "custom" in file or "error_images" in root or "small_images" in root \
                    or "gif_unzip" in root:
                logger.warning(f"skip file, because images error or small, name: {file}")
                continue
            elif file.endswith('.jpg') or file.endswith('.png'):  # 仅处理jpg和png图片文件
                image_paths.append(os.path.join(root, file))
    logger.debug(
        "scan image coule use image length: " + str(len(image_paths)) + ", scan dir: " + str(constants.data_path))
    if len(image_paths) <= 0:
        return False
    width = 0
    height = 0

    for image_path in image_paths:
        try:
            image = cv2.imread(image_path)
            if width == 0:
                width = image.shape[1]
            height = image.shape[0]
        except Exception as e:
            logger.error("error! detail: " + "file name or path: " + image_path + ", error detail: " + str(e))
            continue

        # 检查输出路径是否存在，如果不存在则创建目录
    if not os.path.exists(video_out_path):
        os.makedirs(video_out_path)

    fourcc = cv2.VideoWriter.fourcc(*'MJPG')
    # 创建VideoWriter对象
    video_name = video_out_path + id_generate_time() + "test.mp4"
    video = cv2.VideoWriter(video_name, fourcc, int(output_video_fps), (width, height))  # 设置视频帧率、输出视频大小
    if not video.isOpened():
        logger.error("not open video watch!")
        return False

    try:
        export_index = 0
        image_size_len = len(image_paths)
        for image_path in os.listdir(images_input_path):
            export_index += 1
            percent_cur = int((export_index / image_size_len) * 100)
            image = cv2.imread(os.path.join(images_input_path, image_path))
            if image is None:  # 增加对图像是否正确读取的检查
                logger.error("Image not loaded: " + image_path)
                continue
            resized_image = cv2.resize(image, (width, height))  # 将图像的宽度和高度设置为适合MPEG-4的尺寸
            if image is not None:
                video.write(resized_image)
            if percent_cur % 100 == 0:
                logger.info(f"export process to export_index / image_size_len: {export_index} / {image_size_len} * "
                            f"100%100: {percent_cur}%")
    finally:
        video.release()
    return video_name

上述代码从list中读取图像，使用width = image.shape[1]获取图像宽度或高度；使用 fourcc = cv2.VideoWriter.fourcc(*'MJPG') # 创建VideoWriter对象 video_name = video_out_path + id_generate_time() + "test.mp4" video = cv2.VideoWriter(video_name, fourcc, int(output_video_fps), (width, height))创建videowriter对象,其中output_video_fps规定了视频输出帧率，(width, height)规定了视频输出宽高度；后边使用 image = cv2.imread(os.path.join(images_input_path, image_path))读取传入图片对象；使用resized_image = cv2.resize(image, (width, height))再次调整图片大小至合适尺寸；最后使用video.write(resized_image)写入图像到视频中。写入完成后，使用video.release()释放对象。

人脸特征检测

分类器下载与使用

• 下载地址

https://github.com/opencv/opencv/tree/master/data/haarcascades。

• 读取与使用

@logger.catch
def get_data_file(filename):
    """

    获取数据文件的路径，无论是直接运行还是通过 PyInstaller 打包
    :param filename
    :return
    """
    if getattr(sys, 'frozen', False):
        # 如果程序是“冷冻的”，即打包后的 exe
        basedir = sys._MEIPASS
    else:
        # 如果程序是直接运行的，即没有打包
        basedir = os.path.dirname(__file__)

    return os.path.join(basedir, filename)

def main():
    face_xml_path = get_data_file("xml_data/haarcascade_frontalface_default.xml")
    eye_xml_path = get_data_file('xml_data/haarcascade_eye.xml')
    # 加载分类器
    face_cascade = cv2.CascadeClassifier(face_xml_path)
    eye_cascade = cv2.CascadeClassifier(eye_xml_path)

实际案例

• 图像读取与人脸特征分割

@logger.catch
def face_detect(path, image_path):
    """
    使用OpenCV进行人脸和眼睛检测。

    :param path:
    :param image_path: 图像文件的路径。
    :return: None
    """
    file_path, file_name = os.path.split(image_path)
    folder_name = find_img_result(image_path)

    if folder_name is None:
        folder_name = 'unknown_name'
        logger.warning('folder_name is None, default use unknown_name.')

    base_path = os.path.join(path, "face_detect_result")
    base_path = os.path.join(base_path, folder_name)
    img_file_path = os.path.join(base_path, "split_face")
    img_file_path_line = os.path.join(base_path, "red_line")
    img_file_name = os.path.join(img_file_path, file_name)
    img_file_name_line = os.path.join(img_file_path_line, file_name)

    if not os.path.exists(img_file_path):
        os.makedirs(img_file_path)
        logger.warning("face detect split_face dir not exists, create it.")
    if not os.path.exists(img_file_path_line):
        os.makedirs(img_file_path_line)
        logger.warning(f"face detect red_line dir not exists, create it: {img_file_path}.")

    # 只生成没有的
    if os.path.exists(img_file_name) or os.path.exists(img_file_name_line):
        logger.warning(f"{img_file_name} already exists, will skip!")
        return False
    try:
        face_xml_path = get_data_file("xml_data/haarcascade_frontalface_default.xml")
        eye_xml_path = get_data_file('xml_data/haarcascade_eye.xml')
        # 加载分类器
        face_cascade = cv2.CascadeClassifier(face_xml_path)
        eye_cascade = cv2.CascadeClassifier(eye_xml_path)

        # 读取图像
        img = cv2.imread(image_path)
        if img is None:
            logger.error(f"Error: Unable to load image at {image_path}")
            return False

        # 转换为灰度图像
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

        # 检测人脸
        faces = face_cascade.detectMultiScale(gray, 1.1, 5)
        # eye_cascade.detectMultiScale(gray, 1.1, 5)
        if len(faces) == 0:
            # logger.warning(f"No faces detected:{image_path}")
            return False
        else:
            save_face(img_file_name, img_file_name_line, img, faces, gray, eye_cascade)
        # 显示结果
        cv2.waitKey(0)
        cv2.destroyAllWindows()

    except Exception as e:
        logger.error(f"An error occurred: {e}")
        return False

• 识别结果保存

@logger.catch
def save_face(img_file_name, img_file_name_line, img, faces, gray, eye_cascade):
    """
    
    :param eye_cascade:
    :param gray:
    :param img_file_name_line:
    :param img_file_name:
    :param img:
    :param faces:
    :return:
    """

    target_size = (200, 200)

    # Calculate the dimensions of the faces_image based on the number of faces and target size
    num_faces = len(faces)
    faces_image_height = num_faces * target_size[0]
    faces_image_width = target_size[1]  # Assuming we want to fit only one face horizontally for simplicity

    try:
        # Create a blank image to hold the resized faces
        faces_image = np.zeros((faces_image_height, faces_image_width, 3), dtype=np.uint8)

        for (x, y, w, h) in faces:
            cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
            roi_gray = gray[y:y + h, x:x + w]
            roi_color = img[y:y + h, x:x + w]

            # 在人脸区域内检测眼睛
            eyes = eye_cascade.detectMultiScale(roi_gray)
            for (ex, ey, ew, eh) in eyes:
                cv2.rectangle(roi_color, (ex, ey), (ex + ew, ey + eh), (0, 255, 0), 2)

            # 将绘制了眼睛矩形框的人脸区域放回原图的正确位置
            img[y:y + h, x:x + w] = roi_color

        # Iterate over the detected faces
        for i, (x, y, w, h) in enumerate(faces):
            # Resize the face to the target size
            face_resized = cv2.resize(img[y:y + h, x:x + w], target_size)

            row = i
            col = 0

            # Place the resized face in the faces_image array
            faces_image[row * target_size[0]:(row + 1) * target_size[0],
            col * target_size[1]:(col + 1) * target_size[1]] = face_resized

        cv2.waitKey(0)
        cv2.destroyAllWindows()

        cv2.imwrite(img_file_name, faces_image)
        cv2.imwrite(img_file_name_line, img)
    except Exception as e:
        logger.error(f"unknown error, detail: {e}, name: {img_file_name[-27:]}")
        return False
    logger.success(f"save success, name: {img_file_name[-27:]}")
    return True

上述代码，在人脸中检测了眼睛、脸部特征点，并使用对应颜色框框选出来，最终保存至目标路径。