hugozanini

print("Hugo")

hugozanini

x = np.array([1, 2, 4, 3, 5])
y = np.array([1, 3, 3, 2, 5])
n = len(x)

hugozanini

B1 = (np.sum((x - np.mean(x))*(y - np.mean(y))))/(np.sum(((x - np.mean(x))**2)))

hugozanini

B0 = (np.mean(y)) - (B1*np.mean(x))

hugozanini

y_pred = B0 + B1*x

hugozanini

+----+--------------------+
| ID |     Descrição      |
+----+--------------------+
|  0 | Nariz              |
|  1 | Olho Esquerdo      |
|  2 | Olho Direito       |
|  3 | Orelha esquerda    |
|  4 | Orelha direita     |
|  5 | Ombro esquerdo     |
|  6 | Ombro direito      |

hugozanini

var delta_rx = keypoints[10].position.x - keypoints[8].position.x;
var delta_ry = keypoints[10].position.y - keypoints[8].position.y;
var right_teta = Math.atan2(delta_ry, delta_rx) * 180 / Math.PI;

var delta_lx = keypoints[9].position.x - keypoints[7].position.x;
var delta_ly = keypoints[9].position.y - keypoints[7].position.y;
var left_teta = Math.atan2(delta_ly, delta_lx) * 180 / Math.PI;

hugozanini

GET /model.json
GET /group1-shard1of4
GET /group1-shard2of4
GET /group1-shard3of4
GET /group1-shard4of4

hugozanini

async function load_model() {
  const model = await tf.loadLayersModel("http://127.0.0.1:8080/model.json");
  return model;
}

hugozanini

<annotation>
    <folder>images</folder>
    <filename>kangaroo-0.jpg</filename>
    <path>/home/hugo/Documents/projects/tfjs/dataset/images/kangaroo-0.jpg</path>
  <source>
    <database>Unknown</database>
  </source>
  <size>
    <width>3872</width>
    <height>2592</height>