'Vacant Parking Slot Detection in the Around View Image Based on Deep Learning.' Sensors 20.7 (2020): 2138. 'Vision-Based Parking Slot Detection Based on End-to-End Semantic Segmentation Training.' 2020 IEEE International Conference on Consumer Electronics (ICCE). 'End-to-End Trainable One-Stage Parking Slot Detection Integrating. CNRPark+EXT is a dataset for visual occupancy detection of parking lots of roughly 150,000 labeled images (patches) of vacant and occupied parking spaces, built on a parking lot of 164 parking spaces. CNRPark+EXT extends CNRPark, a preliminary dataset composed by 12,000 images collected in different days of July 2015 from 2 cameras. The additional subset, called CNR-EXT, is composed. For vehicles equipped with the automatic parking system, the accuracy and speed of the parking slot detection are crucial. But the high accuracy is obtained at the price of low speed or expensive computation equipment, which are sensitive for many car manufacturers.
Second, to facilitate the study of vision-based parking-slot detection, a large-scale labeled dataset is established. This dataset is the largest in this field, comprising 12 165 surround-view images collected from typical indoor and outdoor parking sites. For each image, the marking points and parking slots are carefully labeled.
Second, to facilitate the study of vision-based parking-slot detection, a large-scale labeled dataset is established. This dataset is the largest in this field, comprising 12 165 surround-view images collected from typical indoor and outdoor parking sites. For each image, the marking points and parking slots are carefully labeled.
Parking Slot Detection Github Machine Learning
| fromSimpleCVimport* |
| img=Image('./parking_full.png') |
| img=img/2 |
| img=img.edges(25, 400) |
| img.save('parking_edge.png') |
| num_spots=0 |
| spots= [ |
| [(0, 420), (130, 461), (235, 419), (73, 374)], |
| [(68, 374), (247, 422), (326, 378), (170, 338)], |
| [(170, 338), (322, 380), (398, 350), (242, 320)], |
| [(230, 300), (403, 352), (456, 313), (328, 248)] |
| ] |
| full_mask=Image(img.size()) |
| draw_full=DrawingLayer(img.size()) |
| draw_full.polygon([(0,0), (0, 960), (1280, 960), (1280, 0)], filled=True, color=Color.WHITE) |
| full_mask.addDrawingLayer(draw_full) |
| full_mask=full_mask.applyLayers() |
| i=0 |
| forsinspots: |
| mask_spot=Image(img.size()) |
| draw_spot=DrawingLayer(img.size()) |
| draw_spot.polygon(s, filled=True, color=Color.WHITE) |
| mask_spot.addDrawingLayer(draw_spot) |
| mask_spot=mask_spot.applyLayers() |
| masked_img= (img- (full_mask-mask_spot)) |
| img_matrix=masked_img.getNumpy().flatten() |
| img_pixel_count=cv2.countNonZero(img_matrix) |
| print'Pixel count %d'%img_pixel_count |
| ifimg_pixel_count<1500: |
| print'Spot found' |
| num_spots=num_spots+1 |
| masked_img.save('./parking_%d.png'%i) |
| i=i+1 |
| print'Found %d spots'%num_spots |
Parking Slot Detection Github Failed
