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所在平台: Udemy |
课程主页: https://www.udemy.com/course/learn-computer-vision-with-opencv-and-python/
课程评论：没有评论
课程名称：使用OpenCV和Python学习计算机视觉 课程概述： 本课程将带您从零开始学习计算机视觉和图像处理。我们将专注于实践，并提供丰富的真实世界案例和详细的实现解释，帮助您轻松理解和掌握核心概念。 **课程亮点:** * **循序渐进的学习路径:** 课程内容经过精心组织，引导您系统地学习计算机视觉基础和OpenCV的各项功能，避免在海量信息中迷失。 * **丰富的实践案例:** 除了基础知识，课程还包含大量特别的应用示例，例如： * **搜索团队Logo:** 学习图像对比和相似图像查找。 * **遗失与遗弃物品检测:** 开发用于检测遗失或遗弃物品的应用。 * **人脸特征点与特殊应用:** 实现实时的睡眠和微笑检测。 * **差异化特殊应用:** 持续更新包括“足球运动员检测”和“基于深度学习的目标检测API”等多种主题的特殊应用。 * **易于理解的讲解:** 课程避免了过于复杂的数学理论，侧重于实际操作和实现。 * **Python与OpenCV的结合:** 使用Python作为开发语言，能够高效地专注于解决实际问题，减少在编程语法上的时间投入。 * **互动式学习:** 鼓励在问答区积极交流，共同学习和分享信息。 **您将学到的内容:** 1. **计算机视觉与OpenCV核心概念** 2. **基本图像处理操作:** 直方图均衡化、阈值处理、卷积、边缘检测、图像锐化、形态学操作、图像金字塔。 3. **关键点与关键点匹配:** 特征点检测与匹配。 4. **特别应用: 迷你游戏** 5. **图像分割:** 分割与轮廓、轮廓属性、直线检测、圆检测、斑块检测、分水岭算法。 6. **特别应用: 人数统计** 7. **目标跟踪:** 跟踪API、基于颜色的滤波。 8. **特别应用: 移动目标跟踪** 9. **目标检测:** Haar级联实现人脸和眼睛检测、HOG实现行人检测。 10. **基于深度学习的目标检测** 11. **额外章节: 如何准备数据集和训练您的深度学习模型** 12. **额外章节: 特殊应用 - 遗失与遗弃物品检测** 13. **额外章节: 人脸特征点与特殊应用 (实时睡眠和微笑检测)** 14. **额外章节: 差异化特殊应用 (将持续更新)**
Note: You will find real world examples (not only using implemented functions in OpenCV) and i'll add more by the time. It means that course content will expand with new special examples!. ***New Chapter***: "How to Prepare dataset and Train Your Deep Learning Model" was added to the course. You will learn how to prepare a simple dataset, label the objects and train your own deep learning model.***New Special App***: "Search team logos" was added to the course. You will learn how you can compare images and find similar image/object in your dataset.***New Chapter***: "Special Apps - Missing and Abandoned Object Detection" was added to the course. You will learn how to do an application for missing object detection and abandoned object detection***New Chapter***: Facial Landmarks and Special Applications (real time sleep and smile detection) videos was added to the course!***Different Special Applications Chapter***: new videos in different topics will be shared under this chapter. You can look at "Soccer players detection" and "deep learning based API for object detection" examples. In this course, you are going to learn computer vision & image processing from scratch. You will reach all resources, have many examples and explanations of these examples.The explanations are easy to understand and also you can ask the points you need.I have shared key concepts with you without the heavily mathematical theory, so we can focus the implementation.Maybe you can find some other resources, videos or blogs to learn about some of these topics explained in my course, but the advantage of this course is that, you will learn computer vision from scratch by following an order, so that you will not loss yourself between many different sources.You will also find many special examples beside the fundamental topics.I preferred to use OpenCV which is an open source computer vision library used and supported by many people!. I have used OpenCV with Python, because Python allows us to focus on the problem easily without spending time for programming syntax/complex codes.I wish this course to be useful for you to learn computer vision, and Actively we can use 'questions and answers' area to share information...You will learn the topics:The key concepts of computer Vision & OpenCVBasic operations: histogram equalization,thresholding, convolution, edge detection, sharpening ,morphological operations, image pyramids.Keypoints and keypoint matchingSpecial App: mini game by using key pointsImage segmentation: segmentation and contours, contour properties, line detection, circle detection, blob detection, watershed segmentation.Special App: People counter Object tracking:Tracking APIs, Filtering by Color.Special App: Tracking of moving object Object detection: haarcascade face and eye detection, HOG pedestrian detectionObject detection with Deep LearningExtra Chapter: How to Prepare dataset and Train Your Deep Learning ModelExtra Chapter: Special Apps - Missing and Abandoned Object DetectionExtra Chapter: Facial Landmarks and Special Applications (real time sleep and smile detection)Extra Chapter: Different Special Applications ( will be updated with special examples in different topics )
