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162 lines
4.0 KiB
162 lines
4.0 KiB
import cv2
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import numpy as np
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import json
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from pysolar.solar import *
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from datetime import datetime
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from flask import Flask, request
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from flask_restful import Resource, Api, abort
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import base64
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import pickle
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from PIL import Image
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from matplotlib import pyplot as plt
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from io import BytesIO
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app = Flask(__name__)
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api = Api(app)
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def generateAvg(locs, img, avgs):
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time = datetime.strptime( "2019-04-27 17:52:00 -0300","%Y-%m-%d %H:%M:%S %z")
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altitude = int(get_altitude(39.9127938,32.8073577,time))
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loc_images = {}
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for i in locs:
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temp = locs[i]
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crop_img = img[temp["y1"]:temp["y2"], temp["x1"]:temp["x2"]]
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loc_images[i]=[crop_img]
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vals = {}
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if str(altitude) in avgs:
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vals = avgs[str(altitude)]
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else:
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for spot in loc_images:
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vals[spot] = loc_images[spot]
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for spot in loc_images:
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for col in range(len(vals[spot][0])):
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for pix in range(len(vals[spot][0][col])):
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vals[spot][0][col][pix] = [
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np.uint8((int(vals[spot][0][col][pix][0]) + int(loc_images[spot][0][col][pix][0]))/2),
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np.uint8((int(vals[spot][0][col][pix][1]) + int(loc_images[spot][0][col][pix][1]))/2),
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np.uint8((int(vals[spot][0][col][pix][2]) + int(loc_images[spot][0][col][pix][2]))/2)]
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for i in vals:
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vals[i] = vals[i][0].tolist()
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avgs[altitude] = vals
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return avgs
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def generateData(locs, img, avgs,show):
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time = datetime.strptime( "2019-04-27 17:52:00 -0300","%Y-%m-%d %H:%M:%S %z")
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altitude = int(get_altitude(39.9127938,32.8073577,time))
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loc_images = {}
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distances = {}
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for i in locs:
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temp = locs[i]
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crop_img = img[temp["y1"]:temp["y2"], temp["x1"]:temp["x2"]]
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loc_images[i]=[crop_img]
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vals = {}
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if str(altitude) in avgs:
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for spot in avgs[str(altitude)]:
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vals[spot] = np.array(avgs[str(altitude)][spot])
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else:
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for spot in loc_images:
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vals[spot] = loc_images[spot]
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for spot in loc_images:
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foo = np.zeros((len(vals[spot]),len(vals[spot][0])),dtype=int)
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distances[spot] = 0
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for col in range(len(vals[spot])):
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for pix in range(len(vals[spot][col])):
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vals[spot][col][pix] = [
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np.uint8(abs(int(vals[spot][col][pix][0]) - int(loc_images[spot][0][col][pix][0]))),
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np.uint8(abs(int(vals[spot][col][pix][1]) - int(loc_images[spot][0][col][pix][1]))),
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np.uint8(abs(int(vals[spot][col][pix][2]) - int(loc_images[spot][0][col][pix][2])))]
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distances[spot] += np.sum(vals[spot][col][pix])
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foo[col][pix] = np.max(vals[spot][col][pix])
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distances[spot] = int(distances[spot]/vals[spot].size)
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vals[spot] = foo
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if spot in show:
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plt.imshow(vals[spot], interpolation='nearest')
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#plt.show()
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return distances
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def im2str(im):
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imdata = pickle.dumps(im)
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return base64.b64encode(imdata).decode('ascii')
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plt.axis("off")
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with open("modules/databases/locations.json","r") as f:
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locs = json.loads(f.read())
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with open("modules/databases/park_data.json","r") as f:
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data = json.loads(f.read())
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cam = cv2.VideoCapture(5)
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if 1:
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ret,im = cam.read()
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data = generateAvg(locs,im,data)
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with open("modules/databases/park_data.json","w") as f:
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f.write(json.dumps(data,indent=2))
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class Empty(Resource):
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def get(self):
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image = cv2.imread("modules/lot.jpg")
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backup = image.copy()
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spot_data = generateData(locs,image,data,["0","1","2"])
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print(spot_data)
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best_spot = -1
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for loc in spot_data:
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spot_data[loc] = spot_data[loc] < 30
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color = (0,255*spot_data[loc],255*(not spot_data[loc]))
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cv2.rectangle(image,(locs[loc]["x1"],locs[loc]["y1"]),(locs[loc]["x2"],locs[loc]["y2"]),color,5)
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if spot_data[loc]:
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if best_spot == -1:
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best_spot = loc
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continue
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if locs[loc]["priority"] < locs[best_spot]["priority"]:
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best_spot = loc
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print(spot_data)
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if best_spot == -1:
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print("Sorry, no spot found :(")
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return
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else:
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print("Empty spot found at {}".format(int(best_spot) + 1))
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foo = locs[best_spot]
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crop_img = backup[foo["y1"]:foo["y2"], foo["x1"]:foo["x2"]].copy(order='C')
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crop_img = Image.fromarray(crop_img,"RGB")
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buffered = BytesIO()
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crop_img.save(buffered, format="JPEG")
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img = base64.b64encode(buffered.getvalue()).decode("ascii")
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return {"lat":foo["lat"], "lng":foo["lng"], "img":img}
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