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, 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

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, 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
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

#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, 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

import cv2
import numpy as np


src = 255 - cv2.imread('/home/stephen/Desktop/I7Ykpbs.jpg',0)
scores = []

h,w = src.shape
small_dimention = min(h,w)
src = src[:small_dimention, :small_dimention]

smeschke

# This script will only work if the post it
# notes are on a really white background.
#
# You will have to change the paths to:
#    1. Path to source image
#    2. Path to desktop (or folder to save images)
# https://stackoverflow.com/questions/55832414/extract-a-fixed-number-of-squares-from-an-image-with-python-opencv/55834299?noredirect=1#comment98366082_55834299

import cv2
import numpy as np

smeschke

import numpy as np
import cv2
from matplotlib import pyplot as plt

# Load image and mask
img = cv2.imread('/home/stephen/Downloads/bird.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
h,w,_ = img.shape
deep_mask = cv2.imread('/home/stephen/Downloads/bird_mask.png',0)
deep_mask = cv2.resize(deep_mask, (w,h))