Using detectParking for a picture instead of video

[Vafa-Andalibi]

Hello,

Is it possible to use detectParking on a picture of a parking lot instead of the video?
If so, could you please give me a guideline how to do so?

Thanks,

[cnd]

there is while with VideoCapture::read() inside this function program asks for new frame every time, read is combo of VideoCapture::grab() and VideoCapture::retrieve() so for example you can do not call VideoCapture::grab() or check if output result of this function is true

[OAlramahy]

I also have the same issue... would be cool if someone can post the resolution.

[errolyan]

Hello,

Is it possible to use detectParking on a picture of a parking lot instead of the video?
If so, could you please give me a guideline how to do so?

Thanks,

do you have solved this problems

[Vafa-Andalibi]

This is the modified version of the python code that I wrote based on this repo's code:

#!/usr/bin/env python

import yaml
import numpy as np
import cv2
#from matplotlib import pyplot as plt
import imageio
import time
import sys

debug = False 
assert len(sys.argv) == 4, 'Usage: sportdetector.py input_image output_image output_text'
fn_yaml = r"/path/to/yaml/spots_mapping.yml"
fn2 = sys.argv[1]
output_image = sys.argv[2]
output_text = sys.argv[3]
#print 'Updaing the slot.txt every 60 seconds ... '
#while True:
config = {'save_video': False,
          'text_overlay': True,
          'parking_overlay': True,
          'parking_id_overlay': True,
          'parking_detection': True,
          'motion_detection': False,
          'pedestrian_detction': False,
          'min_area_motion_contour': 150,
          'park_laplacian_th': [1.7]*18,
          'park_sec_to_wait': 5,
          'start_frame': 0} #35000
##### manual configs:
config['park_laplacian_th'][8] = 2
config['park_laplacian_th'][10] = 2
#config['park_laplacian_th'][7] = 1.3
#config['park_laplacian_th'][2] = 3.05
#'park_laplacian_th': 3.5,
cap = cv2.imread(fn2)
with open(fn_yaml, 'r') as stream:
    parking_data = yaml.load(stream)

parking_contours = []
parking_bounding_rects = []
parking_mask = []

for park in parking_data:
    points = np.array(park['points'])
    rect = cv2.boundingRect(points)
    points_shifted = points.copy()
    points_shifted[:,0] = points[:,0] - rect[0] # shift contour to roi
    points_shifted[:,1] = points[:,1] - rect[1]
    parking_contours.append(points)
    parking_bounding_rects.append(rect)
    mask = cv2.drawContours(np.zeros((rect[3], rect[2]), dtype=np.uint8), [points_shifted], contourIdx=-1,
                            color=255, thickness=-1, lineType=cv2.LINE_8)
    mask = mask==255
    parking_mask.append(mask)
#     plt.imshow(mask)

parking_status = [False]*len(parking_data)
parking_buffer = [None]*len(parking_data)

frame_blur = cv2.GaussianBlur(cap[:], (5,5), 3)
frame_gray = cv2.cvtColor(frame_blur, cv2.COLOR_BGR2GRAY)
frame_out = cap[:]

all_deltas= []

for ind, park in enumerate(parking_data):
    points = np.array(park['points'])
    rect = parking_bounding_rects[ind]
    roi_gray = frame_gray[rect[1]:(rect[1] + rect[3]), rect[0]:(rect[0] + rect[2])]  # crop roi for faster calcluation
    laplacian = cv2.Laplacian(roi_gray, cv2.CV_64F)
    points[:, 0] = points[:, 0] - rect[0]  # shift contour to roi
    points[:, 1] = points[:, 1] - rect[1]
    delta = np.mean(np.abs(laplacian * parking_mask[ind]))
    if debug:
    	print "index: %d , delta: %f"%(ind,delta)
    all_deltas.append(delta)
    status = delta < config['park_laplacian_th'][ind]
    parking_status[ind] = status

for ind, park in enumerate(parking_data):
    points = np.array(park['points'])
    if parking_status[ind]: color = (0,255,0)
    else: color = (255,0,0)
    cv2.drawContours(frame_out, [points], contourIdx=-1,
                     color=color, thickness=2, lineType=cv2.LINE_8)
    moments = cv2.moments(points)
    centroid = (int(moments['m10']/moments['m00'])-3, int(moments['m01']/moments['m00'])+3)
    cv2.putText(frame_out, str(park['id']), (centroid[0]+1, centroid[1]+1), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,255,255), 1, cv2.LINE_AA)
    cv2.putText(frame_out, str(park['id']), (centroid[0]-1, centroid[1]-1), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,255,255), 1, cv2.LINE_AA)
    cv2.putText(frame_out, str(park['id']), (centroid[0]+1, centroid[1]-1), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,255,255), 1, cv2.LINE_AA)
    cv2.putText(frame_out, str(park['id']), (centroid[0]-1, centroid[1]+1), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,255,255), 1, cv2.LINE_AA)
    cv2.putText(frame_out, str(park['id']), centroid, cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,0), 1, cv2.LINE_AA)

#    plt.imshow(frame_out)
#    plt.show()
if sum(all_deltas) > 10:
    imageio.imwrite(output_image,frame_out)
    with open (output_text , 'w') as wr:
        wr.write(''.join([str(parking_data[i]['id'])+'\n' for i, x in enumerate(parking_status) if x==True]))
#    time.sleep(60)

The yaml file example is similar to the one provided in this repo.