smeschke

import cv2
import numpy as np
import random

numSnowflakes = 2000
xSize, ySize = 1080, 1920

#cv2.namedWindow("window", cv2.WND_PROP_FULLSCREEN)
#cv2.setWindowProperty("window",cv2.WND_PROP_FULLSCREEN,cv2.WINDOW_FULLSCREEN)

smeschke

import cv2
import numpy as np
import math

imgSize = 600,600, 3
coreCenter = 300,300
coreSize = 50
beltThickness = .045
beltLength = 5025
segmentStart = coreCenter[0], coreCenter[1] + coreSize

smeschke

import numpy as np
import cv2
import pandas as pd
import numpy.polynomial.polynomial as poly
import math

# Read Source Data
cap = cv2.VideoCapture('/home/stephen/Desktop/ss5_id_412.MP4')
df = pd.read_csv('/home/stephen/Desktop/ss5_id_412.csv')
#Write Video Out

smeschke

import cv2
import numpy as  np

# Define the multiverse parameter
multiverse_parameter = 3

# Define the temporal seperation in frames
temporal_seperation = 24

# Define path to source video

smeschke

import cv2
import numpy as np

# Read in image
img = cv2.imread('/home/stephen/Desktop/test.png',0)
img = 255 - img
img = cv2.resize(img, (6000,6000))
h,w = img.shape
cutter_size = 20
buffer = int(cutter_size * 1.25)

smeschke

from mrcnn.config import Config
from mrcnn import model as modellib
from mrcnn import visualize
import cv2, os, numpy as np

# Make Model: https://github.com/matterport/Mask_RCNN/blob/master/samples/demo.ipynb
class Config(Config):
	NAME = "deep_segment"
	GPU_COUNT = 1
	IMAGES_PER_GPU = 1

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))

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 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

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