smeschke

import cv2, numpy as np, random, math

# Find contour edges
# Find the edge that is torn
#     use the hough line transform
#     create a mask image where the lines and white on a black background
#     check if the point is in a white or black region
# Rotate the torn edges
# Measure how much they overlap
# The rotation with the maximum overlap will be how they should align

smeschke

#Takes an integer and returns a siteswap character
def get_char(height):
    # If the height is under 10, return the string on the height
    if height < 10: return str(height)
    # If the height is over 10, return 'a' for 10, 'b' for 11, etc...
    else: return chr(height + 87)
# One ball goes high and the others low
for num_balls in range(3,6):
    for height in range(6):
        siteswap = get_char(height + num_balls)

smeschke

import cv2, numpy as np, math
raw_image = cv2.imread('/home/stephen/Desktop/gear6.jpg')
bilateral_filtered_image = cv2.bilateralFilter(raw_image, 5, 175, 175)
# Added median blurring to improve edge detection
median_blurred_images = cv2.medianBlur(bilateral_filtered_image, 5)
edge_detected_image = cv2.Canny(median_blurred_images, 75, 200)
# Switched from RETR_TREE to RETR_EXTERNAL to only extract most outer contours
_, contours, _ = cv2.findContours(edge_detected_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contour_list = []
for contour in contours:

smeschke

import cv2, numpy as np, math

# Parameters
num_dots, hw, num_bins = 10, 400, 400
# Read image that has two lines in it
lines = np.zeros((hw,hw), np.uint8)

# Create a row of diagonal dots
space = int(hw / (num_dots+1))
for i in range(num_dots): cv2.circle(lines, (space*(i+1), space*(i+1)), 0, 255, 1)

smeschke

import cv2, numpy as np, math, random

# create a list for the particles
num_particles = 25000
particles = np.zeros((num_particles,5), np.float32)
# x, y, angle, size
for i in range(particles.shape[0]): particles[i] = -234,-1234,0,0,i
       
# define parameters
hw = 1080

smeschke

import cv2, numpy as np

# Read image
img = cv2.imread('/home/stephen/Desktop/vids/me.png')
h,w,_ = img.shape
sf = 1
img = cv2.resize(img, (int(w/sf), int(h/sf)))
img_pristine = img.copy()

# Get clicks from user

smeschke

from midiutil import MIDIFile
#if module error, try: sudo-apt get install python-scipy
import cv2
import numpy as np
from matplotlib import pyplot as plt
import pandas as pd
import scipy.signal as signal

#path to source data
data_source = '/home/stephen/Desktop/sound1/sound.csv'

smeschke

import cv2, math
import numpy as np
import pandas as pd
import scipy
from sklearn import preprocessing
from scipy import signal
import matplotlib.pyplot as plt

smeschke

import cv2, numpy as np

# Click on one corner of the image,
# then, click on the other corner on the image.
# The image will be cropped and saved into the folder (see below)

# Press 'esc' to quit

# Before you begin, change the path to you own video:
cap = cv2.VideoCapture('/home/stephen/Desktop/track.MP4')

smeschke

import pandas as pd
import numpy as np
import cv2, os
import scipy
from scipy import signal
import csv

circle_color, line_color = (255,255,0), (0,0,255)
window_length, polyorder = 13, 2
sd = "workout"