toughcookie97

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笔记本没有安装java，所以用记事本提交

awesome-hand-pose-estimation

Awesome work on hand pose estimation/tracking

Conv3D_CLSTM

Multimodal Gesture Recognition Using 3D Convolution and Convolutional LSTM

convolutional-pose-machines-tensorflow

CPlusPlusThings

C++那些事

DeepHandGestureRecognition

Trying to classify the 20BN-JESTER hand gesture data set using a few architectures.

Detect-Hand

使用Retinaface来检测手部分，使用mobilenet作为手势提取网络，写了test_pic和test_video的两个接口，实现了实时的手部检测，目前没有关键点

HOF

using HOF to extract features of videos

ncnn-srgan

使用腾讯ncnn框架实现图片的超分辨率处理，esrgan也可以实现，写了前向的代码，效果嘛 不如pytorch 、模型是TensorFlow转来的

yolofstst-dpsort

using yolo-fastestv1 && deepsort using shufflenetv2 as its backbone

toughcookie97/ncnn-srgan

使用腾讯ncnn框架实现图片的超分辨率处理，esrgan也可以实现，写了前向的代码，效果嘛 不如pytorch 、模型是TensorFlow转来的

toughcookie97/yolofstst-dpsort

using yolo-fastestv1 && deepsort using shufflenetv2 as its backbone

toughcookie97/-

笔记本没有安装java，所以用记事本提交

toughcookie97/Detect-Hand

使用Retinaface来检测手部分，使用mobilenet作为手势提取网络，写了test_pic和test_video的两个接口，实现了实时的手部检测，目前没有关键点

toughcookie97/HOF

using HOF to extract features of videos

toughcookie97/awesome-hand-pose-estimation

Awesome work on hand pose estimation/tracking

toughcookie97/Conv3D_CLSTM

Multimodal Gesture Recognition Using 3D Convolution and Convolutional LSTM

toughcookie97/convolutional-pose-machines-tensorflow

toughcookie97/CPlusPlusThings

C++那些事

toughcookie97/dynamic-gesture-recognition-on-RGB-video

toughcookie97/Emotion-Detection-in-Videos

The aim of this work is to recognize the six emotions (happiness, sadness, disgust, surprise, fear and anger) based on human facial expressions extracted from videos. To achieve this, we are considering people of different ethnicity, age and gender where each one of them reacts very different when they express their emotions. We collected a data set of 149 videos that included short videos from both, females and males, expressing each of the the emotions described before. The data set was built by students and each of them recorded a video expressing all the emotions with no directions or instructions at all. Some videos included more body parts than others. In other cases, videos have objects in the background an even different light setups. We wanted this to be as general as possible with no restrictions at all, so it could be a very good indicator of our main goal. The code detect_faces.py just detects faces from the video and we saved this video in the dimension 240x320. Using this algorithm creates shaky videos. Thus we then stabilized all videos. This can be done via a code or online free stabilizers are also available. After which we used the stabilized videos and ran it through code emotion_classification_videos_faces.py. in the code we developed a method to extract features based on histogram of dense optical flows (HOF) and we used a support vector machine (SVM) classifier to tackle the recognition problem. For each video at each frame we extracted optical flows. Optical flows measure the motion relative to an observer between two frames at each point of them. Therefore, at each point in the image you will have two values that describes the vector representing the motion between the two frames: the magnitude and the angle. In our case, since videos have a resolution of 240x320, each frame will have a feature descriptor of dimensions 240x320x2. So, the final video descriptor will have a dimension of #framesx240x320x2. In order to make a video comparable to other inputs (because inputs of different length will not be comparable with each other), we need to somehow find a way to summarize the video into a single descriptor. We achieve this by calculating a histogram of the optical flows. This is, separate the extracted flows into categories and count the number of flows for each category. In more details, we split the scene into a grid of s by s bins (10 in this case) in order to record the location of each feature, and then categorized the direction of the flow as one of the 8 different motion directions considered in this problem. After this, we count for each direction the number of flows occurring in each direction bin. Finally, we end up with an s by s by 8 bins descriptor per each frame. Now, the summarizing step for each video could be the average of the histograms in each grid (average pooling method) or we could just pick the maximum value of the histograms by grid throughout all the frames on a video (max pooling For the classification process, we used support vector machine (SVM) with a non linear kernel classifier, discussed in class, to recognize the new facial expressions. We also considered a Naïve Bayes classifier, but it is widely known that svm outperforms the last method in the computer vision field. A confusion matrix can be made to plot results better.

toughcookie97/ESRGAN

ECCV18 Workshops - Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution (Third Region)

toughcookie97/face-detector-recognition

toughcookie97/GestureVolumeControl

实现手势控制音量

toughcookie97/interview_experience

2021年最新整理，200位校招面经分享，包含微软，华为，腾讯，字节，阿里，360，tplink，好未来，网易，搜狗，wps等，研发岗位，C++研发岗位，安全岗位。

toughcookie97/Markdown-Resume-Template

BAT程序员自己的简历模板分享出来了 。技术简历追求简单明了，避免没有必要的花哨修饰，大家可以fork到自己仓库中，基于这个模板进行修改。

toughcookie97/Minimal-Hand-pytorch

PyTorch reimplementation of minimal-hand (CVPR2020)

toughcookie97/poker

toughcookie97/PRNet-Train

PRNet train code

toughcookie97/readme

第一次作业 readme

toughcookie97/Real-time-GesRec

Real-time Hand Gesture Recognition with PyTorch on EgoGesture, NvGesture, Jester, Kinetics and UCF101

toughcookie97/test-e2e

toughcookie97/todo

toughcookie97/todolist

toughcookie97/todolist_final

toughcookie97/todolist_homework

react

toughcookie97/v4l2-framebuffer

Map frame from USB camera to Linux framebuffer

toughcookie97/YOLOV3-SORT

基于Opencv和Filterpy实现YOLOV3-SORT车辆跟踪与车流统计算法

toughcookie97/Yolov5_DeepSort_Pytorch

Real-time multi-object tracker using YOLO v5 and deep sort

toughcookie97/yolov5forFLIRdataset

Tutorial to train YOLOv5 for FLIRdataset