yanfengliu

...
"scripts": {
  "start": "serve -p 5000",
  "cy:run:chrome": "cypress run --browser=chrome",
  "test:chrome": "start-server-and-test start http://localhost:5000 cy:run:chrome"
},
...

yanfengliu

FROM cypress/included:8.0.0
WORKDIR /app
COPY . .
RUN npm install
ENTRYPOINT ["npm", "run", "test:chrome"]

yanfengliu

...
jobs:
  cypress-run:
    runs-on: ubuntu-latest
    container: cypress/included:8.0.0 # this line specifies the docker image we want
...

yanfengliu

- uses: actions/upload-artifact@v1
  if: failure()
  with:
    name: cypress-screenshots
    path: cypress/screenshots
- uses: actions/upload-artifact@v1
  if: always()
  with:
    name: cypress-videos
    path: cypress/videos

yanfengliu

name: Turkey end-to-end tests chrome headless
on: [push]
jobs:
  cypress-run:
    runs-on: ubuntu-20.04
    steps:
      - uses: actions/checkout@v2
      - uses: cypress-io/github-action@v2
        with:
          start: npm start

yanfengliu

describe("buttons", () => {
  it("can change class", () => {
    cy.getById("label_class").select("class1");
    dot_mode_coordinates_2.forEach((coordinate) => {
      cy.get("canvas").click(coordinate.x, coordinate.y);
    });
    cy.get("canvas").matchImageSnapshot("multi_class");
  });
}

yanfengliu

import keras 
import numpy as np
from keras.models import Sequential, Model
from keras.layers import Dense, Activation, LeakyReLU, Input, Lambda, Concatenate
from keras.losses import mean_absolute_error, mean_squared_error
import os
import sys
%matplotlib inline
import matplotlib.pyplot as plt
from keras import optimizers

yanfengliu

def transform_matrix_tensor(theta, d, a, alpha):
    # tensor version of transform matrix
    matrix = [[tf.cos(theta), tf.multiply(-tf.sin(theta), tf.cos(alpha)), tf.multiply(tf.sin(theta), tf.sin(alpha)), tf.multiply(a, tf.cos(theta))], 
              [tf.sin(theta), tf.multiply(tf.cos(theta), tf.cos(alpha)), tf.multiply(-tf.cos(theta), tf.sin(alpha)), tf.multiply(a, tf.sin(theta))], 
              [tf.zeros_like(theta), tf.sin(alpha), tf.cos(alpha), d], 
              [tf.zeros_like(theta), tf.zeros_like(theta), tf.zeros_like(theta), tf.ones_like(theta)]]
    return matrix
  
def batch_matmul(location_v, batch_theta_v):
    # perform matrix multiplication between the location vector and the transform matrix, 

yanfengliu

model = Sequential([
    Dense(256, input_shape=(2,)),
    LeakyReLU(),
    Dense(256),
    LeakyReLU(),
    Dense(256),
    LeakyReLU(),
    Dense(256),
    LeakyReLU(),
    Dense(256),

yanfengliu

def transformMatrix(theta, d, a, alpha):
    return np.array([[np.cos(theta), -np.sin(theta)*np.cos(alpha), np.sin(theta)*np.sin(alpha), a*np.cos(theta)], 
                     [np.sin(theta), np.cos(theta)*np.cos(alpha), -np.cos(theta)*np.sin(alpha), a*np.sin(theta)], 
                     [0, np.sin(alpha), np.cos(alpha), d], 
                     [0, 0, 0, 1]])

def forwardKinematics_2(theta1, theta2):
    T00 = transformMatrix(theta1,0,1,0)
    T01 = transformMatrix(theta2,0,1,0)
    pos = [0, 0, 0, 1]