The depth map produces a new image of the same width and height of the original image, but the pixels are representative of the depth at a particular pixel. With each key in the dictionary correlated to one maya object. Running the file combineSegAndMap.py with the correct file paths will write to a newly created file in all_image_data with the newly created maya input in dictionary form. Combining Image Segmentation & Depth Mapping This repo provides the ability to get the depth mappings of a single image using a pre-trained model. models/model_cityscapes/model_cityscapes all_image_data/nyc/nyc.jpg -checkpoint_path. This repo has been modified locally to output a numpy array of the image representing the original image, and a png the size of the original image, with colors representing the depths to all_image_data/folderName. A downside to using this image segmentation is that unique buildings are not inherently marked somehow, but rather every building would be marked under buildings. The dictionary is marked by the labels of each mapping. This is done by taking the masks found, and exporting the numpy array of the mask to another file, where a dictionary of the numpy array. It returns n images that each represent a specific class, with a mask over the specified location of a class. This repo provides the ability to semantically segment a single image using a pre-trained model. pretrained_model/cocostuff164k_iter100k.pth -image-path. Python3 demo.py -config config/cocostuff164k.yaml -model-path. This repo has been modified locally to output a dictionary of numpy arrays of the same size as the original image to all_image_data/folderName.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |