MyCity/server_side/api/modules/detectSpots.py

import cv2
import numpy as np
import json
from pysolar.solar import *
from datetime import datetime
from PIL import Image
from matplotlib import pyplot as plt

def generateAvg(locs, img, avgs):
	time = datetime.strptime( "2019-04-27 17:52:00 -0300","%Y-%m-%d %H:%M:%S %z")
	altitude = int(get_altitude(39.9127938,32.8073577,time))

	loc_images = {}

	for i in locs:
		temp = locs[i]
		crop_img = img[temp["y1"]:temp["y2"], temp["x1"]:temp["x2"]]
		loc_images[i]=[crop_img]

	vals = {}
	if str(altitude) in avgs:
		vals = avgs[str(altitude)]
	else:
		for spot in loc_images:
			vals[spot] = loc_images[spot]

	for spot in loc_images:
		for col in range(len(vals[spot][0])):
			for pix in range(len(vals[spot][0][col])):

				vals[spot][0][col][pix] = [
					np.uint8((int(vals[spot][0][col][pix][0]) + int(loc_images[spot][0][col][pix][0]))/2),
					np.uint8((int(vals[spot][0][col][pix][1]) + int(loc_images[spot][0][col][pix][1]))/2),
					np.uint8((int(vals[spot][0][col][pix][2]) + int(loc_images[spot][0][col][pix][2]))/2)]


	for i in vals:
		vals[i] = vals[i][0].tolist()

	avgs[altitude] = vals

	return avgs


def generateData(locs, img, avgs,show):

	time = datetime.strptime( "2019-04-27 17:52:00 -0300","%Y-%m-%d %H:%M:%S %z")
	altitude = int(get_altitude(39.9127938,32.8073577,time))

	loc_images = {}
	distances = {}

	for i in locs:
		temp = locs[i]
		crop_img = img[temp["y1"]:temp["y2"], temp["x1"]:temp["x2"]]
		loc_images[i]=[crop_img]

	vals = {}

	if str(altitude) in avgs:

		for spot in avgs[str(altitude)]:
			vals[spot] = np.array(avgs[str(altitude)][spot])
	else:

		for spot in loc_images:
			vals[spot] = loc_images[spot]

	for spot in loc_images:
		foo = np.zeros((len(vals[spot]),len(vals[spot][0])),dtype=int)
		distances[spot] = 0
		for col in range(len(vals[spot])):
			for pix in range(len(vals[spot][col])):
				vals[spot][col][pix] = [
					np.uint8(abs(int(vals[spot][col][pix][0]) - int(loc_images[spot][0][col][pix][0]))),
					np.uint8(abs(int(vals[spot][col][pix][1]) - int(loc_images[spot][0][col][pix][1]))),
					np.uint8(abs(int(vals[spot][col][pix][2]) - int(loc_images[spot][0][col][pix][2])))]

				distances[spot] += np.sum(vals[spot][col][pix])

				foo[col][pix] = np.max(vals[spot][col][pix])
		distances[spot] = int(distances[spot]/vals[spot].size)
		vals[spot] = foo

		if spot in show:
			plt.imshow(vals[spot], interpolation='nearest')
			plt.show()

	return distances


plt.axis("off")
with open("databases/locations.json","r") as f:
	locs = json.loads(f.read())

with open("databases/park_data.json","r") as f:
	data = json.loads(f.read())

while 0:
	data = generateAvg(locs,cv2.imread("parking_images/8.jpg"),data)

	with open("databases/park_data.json","w") as f:
		f.write(json.dumps(data,indent=4))
	exit(0)

image = cv2.imread("parking_images/7.jpg")
spot_data = generateData(locs,image,data,["0","1","2"])

print(spot_data)

best_spot = -1
for loc in spot_data:
	spot_data[loc] = spot_data[loc] < 30

	color = (0,255*spot_data[loc],255*(not spot_data[loc]))

	cv2.rectangle(image,(locs[loc]["x1"],locs[loc]["y1"]),(locs[loc]["x2"],locs[loc]["y2"]),color,5)

	if spot_data[loc]:
		if best_spot == -1:
			best_spot = loc
			continue

		if locs[loc]["priority"] < locs[best_spot]["priority"]:
			best_spot = loc

print(spot_data)

if best_spot == -1:
	print("Sorry, no spot found :(")

else:
	print("Empty spot found at {}".format(int(best_spot) + 1))




plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
plt.show()