python毕业设计-基于mediapipe实现的运动计数项目源码+文档说明

# facial-landmarks-35-adas-0002 ## Use Case and High-Level Description This is a custom-architecture convolutional neural network for 35 facial landmarks estimation. ## Example and Landmarks Definition  [Left Eye] **p0, p1**: corners of the eye, located on the boundary of the eyeball and the eyelid. [Right Eye] **p2, p3**: corners of the eye, located on the boundary of the eyeball and the eyelid. [Nose] **p4**: nose-tip point; **p5**: lowest point of the nasal septum; **p6, p7**: right-bottom and left-bottom of the nose wing. [Mouth] **p8, p9**: mouth corners on the outer boundary of the lip; **p10, p11**: center points along the outer boundary of the lip. [Left Eyebrow] **p12**: starting point of the upper boundary of the eyebrow; **p13**: mid-point of the upper arc of the eyebrow; **p14**: ending point of the upper boundary of the eyebrow. [Right Eyebrow] **p15**: starting point of the upper boundary of the eyebrow; **p16**: mid-point of the upper arc of the eyebrow; **p17**: ending point of the upper boundary of the eyebrow. [Face Contour] **p26**: chin center; **p18, p34**: upper points of the face contour aligned with the outer corners of the eyes; **p19~p25**: boundary points, evenly distributed along the curve p18-p26; **p27~p33**: boundary points, evenly distributed along the curve p26-p34. ## Specification | Metric | Value | |-----------------------|---------------------------------------------| | GFlops | 0.042 | | MParams | 4.595 | | Source framework | Caffe\* | ## Validation Dataset A 1000-sample random subset of a large internal dataset containing images of 300 people with different facial expressions. ## Validation Results The quality of landmarks' positions prediction is evaluated through the use of Normed Error (NE). The error for the i^th sample has the form:  where N is the number of landmarks, _p_-hat and _p_ are, correspondingly, the prediction and ground truth vectors of the k^th landmark of the i^th sample, and d_i is the interocular distance for the i^th sample. | Dataset | Mean NE | 90^th [Percentile](https://en.wikipedia.org/wiki/Percentile) NE |[Standard deviation](https://en.wikipedia.org/wiki/Standard_deviation) of NE | |------------------|---------|---------------------------------------------------------------------------|-----------------------------------------------------------------------------| | Internal dataset | 0.106 | 0.143 | 0.038 | ## Inputs Image, name: `data`, shape: `1, 3, 60, 60` in the format `B, C, H, W`, where: - `B` - batch size - `C` - number of channels - `H` - image height - `W` - image width ## Outputs The net outputs a blob `align_fc3` with the shape: `1, 70`, containing row-vector of 70 floating point values for 35 landmarks' normed coordinates in the form (x0, y0, x1, y1, ..., x34, y34). ## Legal Information [*] Other names and brands may be claimed as the property of others.