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196 lines
5.0 KiB
196 lines
5.0 KiB
import xml.etree.ElementTree as ET
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import cv2
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import numpy as np
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import os
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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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def timeAverage():
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data_file = open("data.json","r")
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time_file = open("timely.json","w")
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averages = json.loads(data_file.read())
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timely = {}
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altitudes = {}
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for key in averages:
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date = key[37:-13]
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time = key[49:-4].replace("_",":")
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averages[key]["time"]=time
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averages[key]["date"]=date
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data_file.close()
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data_file = open("data.json","w")
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data_file.write(json.dumps(averages))
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for key in averages:
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time = datetime.strptime(averages[key]["date"] + " " + averages[key]["time"] + " -0300","%Y-%m-%d %H:%M:%S %z")
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altitude = int(get_altitude(-25.4269081,-49.3318036,time))
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altitudes[averages[key]["date"] + " " + averages[key]["time"] ] = altitude
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if not (altitude in timely):
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timely[altitude] = {}
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for spot in averages[key]:
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if(spot == "time" or spot == "date"):
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continue
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if not spot in timely[altitude]:
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timely[altitude][spot]=[]
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timely[altitude][spot].append({"r":averages[key][spot]["r"],"b":averages[key][spot]["b"],"g":averages[key][spot]["g"]})
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print(altitudes)
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for key in timely:
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for id in timely[key]:
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total = {"r":0,"g":0,"b":0}
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for i in range(len(timely[key][id])):
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for color in total:
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total[color] += timely[key][id][i][color]
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for color in total:
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total[color] = total[color] / len(timely[key][id])
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timely[key][id] = total
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print(timely)
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timely_json = json.dumps(timely)
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time_file.write(timely_json)
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data_file.close()
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time_file.close()
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def generateData(locations,image,show,averages):
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loc_images = {}
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average_values = {}
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average_total=[0,0,0]
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for col in range(len(image)):
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for pix in range(len(image[col])):
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if (image[col][pix] == [255,255,255]).all():
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image[col][pix] == [255,255,254]
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for i in locations:
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temp = locations[i]
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pts = np.array([[int(temp[0]['x']),int(temp[0]['y'])],
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[int(temp[1]['x']),int(temp[1]['y'])],
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[int(temp[2]['x']),int(temp[2]['y'])],
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[int(temp[3]['x']),int(temp[3]['y'])]])
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rect = cv2.boundingRect(pts)
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x,y,w,h = rect
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croped = image[y:y+h, x:x+w].copy()
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pts = pts - pts.min(axis=0)
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mask = np.zeros(croped.shape[:2], np.uint8)
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cv2.drawContours(mask, [pts], -1, (255, 255, 255), -1, cv2.LINE_AA)
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dst = cv2.bitwise_and(croped, croped, mask=mask)
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bg = np.ones_like(croped, np.uint8)*255
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cv2.bitwise_not(bg,bg, mask=mask)
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dst2 = bg+ dst
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split_len = len(dst2)//3
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splitted = [dst2[:split_len,:],dst2[split_len:split_len*2,:],dst2[split_len*2:,:]]
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loc_images[i]=[dst2]
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for lot in loc_images:
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average_values[lot] = []
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for i in range(1):
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diff_pix = 0
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reps = 0
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avg_rgb = [0,0,0]
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rgb = ["b","g","r"]
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for col in loc_images[lot][i]:
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for pix in col:
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if (pix == [255,255,255]).all():
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continue
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different = False
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print("[",end="")
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for j in range(3):
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print(abs(int(averages[lot][rgb[i]]-pix[i])) , end=",")
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if abs(averages[lot][rgb[i]]-pix[i]) > 59:
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different = True
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reps += 1
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diff_pix += different
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print("]",end=" ")
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print("")
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print("\n\n")
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average_values[lot] = (diff_pix/reps)*100
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if lot in show:
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cv2.imshow("a" , loc_images[lot][0])
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cv2.waitKey(0)
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return average_values
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def findSpot(img_loc,locations):
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empty = []
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timely_values = time_file = open("timely.json","r")
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timely_averages = json.loads(timely_values.read())
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img = cv2.imread(img_loc)
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date = img_loc[37:-13]
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time = img_loc[49:-4]
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time = time.replace("_",":")
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time = datetime.strptime(date + " " + time + " -0300","%Y-%m-%d %H:%M:%S %z")
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altitude = int(get_altitude(-25.4269081,-49.3318036,time))
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base_value = timely_averages[str(altitude)]
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print(altitude)
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averages = generateData(locations,img,[],base_value)
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for i in averages:
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print(i + " " + str(averages[i]))
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for i in locations:
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if(averages[i] > 32):
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color=[0,0,255]
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empty.append(i)
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else:
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color=[0,255,0]
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corners = [[],[]]
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for j in range(0,4):
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val2 = j+1
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if(val2 == 4):
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val2 = 0
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pt1 = (int(locations[i][j]["x"]),int(locations[i][j]["y"]))
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pt2 = (int(locations[i][val2]["x"]),int(locations[i][val2]["y"]))
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cv2.line(img,pt1,pt2,color)
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corners[0].append(int(locations[i][j]["x"]))
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corners[1].append(int(locations[i][j]["y"]))
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x1=min(corners[0][0],corners[0][1],corners[0][2],corners[0][3]);
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x2=max(corners[0][0],corners[0][1],corners[0][2],corners[0][3]);
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y1=min(corners[1][0],corners[1][1],corners[1][2],corners[1][3]);
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y2=max(corners[1][0],corners[1][1],corners[1][2],corners[1][3]);
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pt = (int((x1+x2)/2),int((y1+y2)/2))
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cv2.putText(img,str(i), pt, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 255)
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cv2.imshow("Parking Lot",img)
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cv2.waitKey(0)
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return empty
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location_file = open("locations.json","r")
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location_json = json.loads(location_file.read())
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empty_locs = findSpot('./PKLot/PKLot/PUCPR/Sunny/2012-09-11/2012-09-11_18_08_41.jpg',location_json)
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closest = 1000
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for i in empty_locs:
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print (location_json[i][4])
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if location_json[i][4] < closest:
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closest = int(i)
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print("The closest spot is " + str(closest))
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print("done")
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