object detection ski area.py

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

def convert_media_to_image(media):
    arr = np.asarray(bytearray(b64.b64decode(media["__data__"])), dtype=np.uint8)
    return cv2.imdecode(arr,-1)

history = 50
nGauss = 2
bgThresh = 0.75
noise = 20

fgbg = cv2.BackgroundSubtractorMOG(history, nGauss, bgThresh, noise)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(4,4))

def preprocess(image, ref):
    fgmask = fgbg.apply(image)
    fgmask = cv2.morphologyEx(fgmask, cv2.MORPH_OPEN, kernel)
    return fgmask

def display_image(image):
    _, png_image = cv2.imencode(".png", image)
    display(Image(data=png_image.tostring()))

images = [convert_media_to_image(rec["media"]) for oid, rec in data]
shape = list(images[0].shape)
shape.append(min(len(images), 100))
avg = np.zeros(shape)
for z, image in enumerate(images[:100]):
    avg[:,:,:,z] = image
avg = np.uint8(np.average(avg, axis=3))
avg = cv2.merge([avg[:,:,0],avg[:,:,1],avg[:,:,2]])
fgbg.apply(avg)
preprocessed = [preprocess(img, avg) for img in images]
cv2.imwrite("avg.jpg", avg)
@interact(idx=widgets.IntSlider(min=0,max=len(images)-1,step=1,value=0))
def display_contours(idx):
    contours, _ = cv2.findContours(preprocessed[idx],cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
    areas = [cv2.contourArea(c) for c in contours]
    good_contours = [c for c, a in zip(contours, areas) if a < 200]
    cv2.drawContours(images[idx], good_contours, -1, (0, 0, 255))
    display_image(images[idx])
    print "%i 'people' found" % len(good_contours)