Seagor

normalize = lambda x: (255 / (x.max() - x.min())) * (x - x.min())
smooth = lambda x: ndimage.convolve(x, np.ones([3,3])/9)

def threshold_mask(img, threshold=150):
    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    mask = np.ones(gray.shape).astype(np.uint8)
    mask[gray < threshold] = 0
    return mask

def scanline_mask(imask):

Seagor

import cv2
from skimage.morphology import binary_opening

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
matplotlib.style.use('ggplot')
%matplotlib inline

Seagor

#!/bin/sh
"exec" "twxec" "-e" "filter_stream()" "$0" "$@"

import oauth2 as oauth
from trickle import twitter as tw

{{docstring "Make sure you have registered an application with Twitter in order to obtain your credentials. Once you have them, start streaming tweets using one of the filtering parameteres below"}}

app_key = {{string Twitter_App_Key}}
app_secret = {{string Twitter_App_Secret}}

Seagor

import base64 as b64
import numpy as np
import cv2
import matplotlib.pyplot as plt
from ipywidgets import interact, interactive, fixed
from IPython.display import Image, display
import ipywidgets as widgets
from itertools import tee, izip

WINDOW_SIZE = 41 # size of window to use when computing averages. Endpoint images outside 

Seagor

#!/bin/sh
"exec" "twxec" "-e" "filter_stream()" "$0" "$@"

import oauth2 as oauth
from trickle import twitter as tw

{{docstring "Make sure you have registered an application with Twitter in order to obtain your credentials. Once you have them, start streaming tweets using one of the filtering parameteres below"}}

app_key = {{string Twitter_App_Key}}
app_secret = {{string Twitter_App_Secret}}

Seagor

import cv2
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt


def buffer_coastline(img, threshold=135):
    arr = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    mask = np.ndarray(arr.shape + (3,)).astype(np.uint8)
    below = arr < threshold

Seagor

import pandas as pd

areas = []
for i, img in enumerate(found_objects):
    for r, reg in enumerate(img):
        for cnt in reg['contours']:
            areas.append((cv2.contourArea(cnt), r, i))
            
df = pd.DataFrame(data = areas, columns=['area', 'dock', 'image'])