smeschke

import pandas as pd
import numpy as np
import cv2, os
import csv

input_source = "/home/stephen/Desktop/me.MP4"
cap = cv2.VideoCapture(input_source)
frame_number = 0
font, scale, colorText, thick = cv2.FONT_HERSHEY_SIMPLEX, .5, (234,234,234), 1
size, color, thickness = 5, (255,255,255), 5

smeschke

import cv2, numpy as np, csv

#https://github.com/opencv/opencv/blob/master/samples/dnn/openpose.py
outfile_path = '/home/stephen/Desktop/workout.csv'

protoFile = "/home/stephen/pose/mpi/pose_deploy_linevec_faster_4_stages.prototxt"
weightsFile = "/home/stephen/pose/mpi/pose_iter_160000.caffemodel"
net = cv2.dnn.readNetFromCaffe(protoFile, weightsFile)

data, input_width, input_height, threshold, frame_number = [], 368, 386, 0.1, 0

smeschke

import pandas as pd
df = pd.read_csv('/home/stephen/Desktop/records.csv')

#get list of jugglers
jugglers = []
for record in df.values:
    jugglers.append(record[1])
jugglers = list(set(jugglers))

#get list of comparitive pr's

smeschke

import cv2
import numpy as np
import numpy as np
from matplotlib import pyplot as plt
import pandas as pd
from scipy import signal
import cv2
from matplotlib import pyplot as plt
import pandas as pd
from mpl_toolkits.mplot3d import Axes3D

smeschke

import cv2, math, numpy as np

#h,s,v range of the object to be tracked
#h,s,v,h1,s1,v1 = 27,0,0,82,190,255 #GREEN
color_values = 168,154,99,177,255,255 #RED
threshold_value = 0

output_path = '/home/stephen/Desktop/1.csv'

cap = cv2.VideoCapture('/home/stephen/Desktop/531.avi')

smeschke

#import cv2 for computer vision
#import numpy to make blank images
import cv2, numpy as np

#capture source video
cap = cv2.VideoCapture('/home/acer/Desktop/colorspace/bxx.MP4')
#define

#define orange
orange = 0,145,155,255,255,255

smeschke

import cv2, numpy as np

#path to source video
source_path = '/home/acer/Desktop/744.MP4'
#out path to save tracking data
output_path = '/home/acer/Desktop/light_blue.csv'

#parameter of lk optical flow
window_size = 18
lk_params = dict(winSize = (window_size,window_size),

smeschke

import cv2, numpy as np, os

#parameters
working_dir = '/home/stephen/Desktop/keras_demo/'
cap = cv2.VideoCapture(0)

org, font, scale, color, thickness, linetype = (50,50), cv2.FONT_HERSHEY_SIMPLEX, 1.2, (234,12,123), 2, cv2.LINE_AA
#chromakey values
h,s,v,h1,s1,v1 = 16,0,64,123,111,187 #green
h,s,v,h1,s1,v1 = 0,74,53,68,181,157 #skin tone

smeschke

import cv2, numpy as np, os

#parameters
working_dir = '/home/stephen/Desktop/keras_demo/'
cap = cv2.VideoCapture(1)

org, font, scale, color, thickness, linetype = (50,50), cv2.FONT_HERSHEY_SIMPLEX, 1.2, (234,12,123), 2, cv2.LINE_AA
#chromakey values
h,s,v,h1,s1,v1 = 16,0,64,123,111,187
#amount of data to use

smeschke

import numpy as np, os, cv2
from keras.utils import to_categorical
from keras.models import load_model

#reads images from folder (images must be labeled 0.png, 1.png, etc...)
def read_from_folder(folder, pattern, image_number, stop):
    images = []
    while image_number < stop:
        path = folder + pattern + '/' + str(image_number) + '.png'
        img = cv2.imread(path, 0)