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Depth estimation using Midas model
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| import torch | |
| from torchvision.transforms import Compose, ToTensor, Resize | |
| from torchvision.datasets import ImageFolder | |
| from torch.utils.data import DataLoader | |
| import matplotlib.pyplot as plt | |
| import torch | |
| import os | |
| import numpy as np | |
| class DepthEstimator: | |
| def __init__(self,modelType = "DPT_Large", imgs_dir = "./test/", depth_dir = "./output/", batch_size = 1): | |
| self.TEST_PATH = imgs_dir | |
| self.IMAGE_SIZE = 384 | |
| self.BATCH_SIZE = batch_size | |
| self.DEVICE = torch.device("cuda") if torch.cuda.is_available() else "cpu" | |
| self.OUTPUT_PATH = depth_dir | |
| self.testTransform = Compose([ | |
| Resize((self.IMAGE_SIZE, self.IMAGE_SIZE)), ToTensor()]) | |
| self.midas = torch.hub.load("intel-isl/MiDaS", modelType) | |
| self.midas.to(self.DEVICE) | |
| self.midas.eval() | |
| self.testDataset = ImageFolder(self.TEST_PATH, self.testTransform) | |
| self.testLoader = DataLoader(self.testDataset, batch_size=self.BATCH_SIZE,shuffle=False) | |
| if not os.path.exists(self.OUTPUT_PATH): | |
| os.makedirs(self.OUTPUT_PATH) | |
| self.idx=0 | |
| def run(self): | |
| for images, _ in self.testLoader: | |
| self.process_images(images) | |
| def process_images(self,images): | |
| images = images.to(self.DEVICE) | |
| with torch.no_grad(): | |
| prediction = self.midas(images) | |
| prediction = torch.nn.functional.interpolate( | |
| prediction.unsqueeze(1), size=[self.IMAGE_SIZE,self.IMAGE_SIZE], mode="bicubic", | |
| align_corners=False).squeeze() | |
| # convert to a numpy array | |
| if len(prediction.shape) == 2: | |
| output = np.array([prediction.cpu().numpy()]) | |
| else: | |
| output = prediction.cpu().numpy() | |
| axes = [] | |
| fig=plt.figure(figsize=(10, 5)) | |
| # loop over the rows and columns | |
| for i in range(self.BATCH_SIZE): | |
| axes.append(fig.add_subplot(1, 2, 1)) | |
| plt.imshow(images[i].permute((1, 2, 0)).cpu().detach().numpy()) | |
| axes.append(fig.add_subplot(1, 2, 2)) | |
| plt.imshow(output[i]) | |
| fig.tight_layout() | |
| #self.testLoader. | |
| outputFileName = os.path.join(self.OUTPUT_PATH, f'{self.idx}.jpg') | |
| plt.savefig(outputFileName) | |
| self.idx += 1 | |
| if __name__ == "__main__": | |
| depth_estimator = DepthEstimator(imgs_dir = "./test/", depth_dir = "./output/") | |
| depth_estimator.run() |
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