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Run and consider monocular depth estimation fashions with Hugging Face and FiftyOne
People view the world via two eyes. One of many main advantages of this binocular imaginative and prescient is the power to understand depth — how close to or far objects are. The human mind infers object depths by evaluating the photographs captured by left and proper eyes on the identical time and deciphering the disparities. This course of is named stereopsis.
Simply as depth notion performs an important position in human imaginative and prescient and navigation, the power to estimate depth is crucial for a variety of pc imaginative and prescient functions, from autonomous driving to robotics, and even augmented actuality. But a slew of sensible issues from spatial limitations to budgetary constraints typically restrict these functions to a single digital camera.
Monocular depth estimation (MDE) is the duty of predicting the depth of a scene from a single picture. Depth computation from a single picture is inherently ambiguous, as there are a number of methods to challenge the identical 3D scene onto the 2D airplane of a picture. In consequence, MDE is a difficult process that requires (both explicitly or implicitly) factoring in lots of cues comparable to object dimension, occlusion, and perspective.
On this put up, we’ll illustrate the best way to load and visualize depth map knowledge, run monocular depth estimation fashions, and consider depth predictions. We’ll achieve this utilizing knowledge from the SUN RGB-D dataset.
Specifically, we’ll cowl the next:
We’ll use the Hugging Face transformers and diffusers libraries for inference, FiftyOne for knowledge administration and visualization, and scikit-image for analysis metrics. All of those libraries are open supply and free to make use of. Disclaimer: I work at Voxel51, the lead maintainers of certainly one of these libraries (FiftyOne).
Earlier than we get began, ensure you have all the obligatory libraries put in:
pip set up -U torch fiftyone diffusers transformers scikit-image
Then we’ll import the modules we’ll be utilizing all through the put up:
from glob import globimport numpy as npfrom PIL import Imageimport torch
import fiftyone as foimport fiftyone.zoo as fozimport fiftyone.mind as fobfrom fiftyone import ViewField as F
The SUN RGB-D dataset accommodates 10,335 RGB-D photos, every of which has a corresponding RGB picture, depth picture, and digital camera intrinsics. It accommodates photos from the NYU depth v2, Berkeley B3DO, and SUN3D datasets. SUN RGB-D is likely one of the hottest datasets for monocular depth estimation and semantic segmentation duties!
💡For this walkthrough, we’ll solely use the NYU depth v2 parts. NYU depth v2 is permissively licensed for industrial use (MIT), and could be downloaded from Hugging Face instantly.
Downloading the Uncooked Information
First, obtain the SUN RGB-D dataset from right here and unzip it, or use the next command to obtain it instantly:
curl -o sunrgbd.zip https://rgbd.cs.princeton.edu/knowledge/SUNRGBD.zip
After which unzip it:
unzip sunrgbd.zip
If you wish to use the dataset for different duties, you may totally convert the annotations and cargo them into your fiftyone.Dataset. Nevertheless, for this tutorial, we’ll solely be utilizing the depth photos, so we’ll solely use the RGB photos and the depth photos (saved within the depth_bfx sub-directories).
Creating the Dataset
As a result of we’re simply involved in getting the purpose throughout, we’ll prohibit ourselves to the primary 20 samples, that are all from the NYU Depth v2 portion of the dataset:
## create, identify, and persist the datasetdataset = fo.Dataset(identify=”SUNRGBD-20″, persistent=True)
## pick first 20 scenesscene_dirs = glob(“SUNRGBD/kv1/NYUdata/*”)[:20]
samples = []
for scene_dir in scene_dirs:## Get picture file path from scene directoryimage_path = glob(f”{scene_dir}/picture/*”)[0]
## Get depth map file path from scene directorydepth_path = glob(f”{scene_dir}/depth_bfx/*”)[0]
depth_map = np.array(Picture.open(depth_path))depth_map = (depth_map * 255 / np.max(depth_map)).astype(“uint8”)
## Create samplesample = fo.Pattern(filepath=image_path,gt_depth=fo.Heatmap(map=depth_map),)
samples.append(pattern)
## Add samples to datasetdataset.add_samples(samples);
Right here we’re storing the depth maps as heatmaps. All the pieces is represented by way of normalized, relative distances, the place 255 represents the utmost distance within the scene and 0 represents the minimal distance within the scene. It is a widespread approach to characterize depth maps, though it’s removed from the one method to take action. If we had been involved in absolute distances, we might retailer sample-wise parameters for the minimal and most distances within the scene, and use these to reconstruct absolutely the distances from the relative distances.
Visualizing Floor Fact Information
With heatmaps saved on our samples, we will visualize the bottom fact knowledge:
session = fo.launch_app(dataset, auto=False)## then open tab to localhost:5151 in browser
When working with depth maps, the colour scheme and opacity of the heatmap are essential. I’m colorblind, so I discover that the viridis colormap with opacity turned all the way in which up works finest for me.
Floor Fact?
Inspecting these RGB photos and depth maps, we will see that there are some inaccuracies within the floor fact depth maps. For instance, on this picture, the darkish rift via the middle of the picture is definitely the farthest a part of the scene, however the floor fact depth map reveals it because the closest a part of the scene:
This is likely one of the key challenges for MDE duties: floor fact knowledge is tough to return by, and is usually noisy! It’s important to concentrate on this whereas evaluating your MDE fashions.
Now that now we have our dataset loaded in, we will run monocular depth estimation fashions on our RGB photos!
For a very long time, the state-of-the-art fashions for monocular depth estimation comparable to DORN and DenseDepth had been constructed with convolutional neural networks. Lately, nonetheless, each transformer-based fashions comparable to DPT and GLPN, and diffusion-based fashions like Marigold have achieved exceptional outcomes!
On this part, we’ll present you the best way to generate MDE depth map predictions with each DPT and Marigold. In each circumstances, you may optionally run the mannequin domestically with the respective Hugging Face library, or run remotely with Replicate.
To run by way of Replicate, set up the Python consumer:
pip set up replicate
And export your Replicate API token:
export REPLICATE_API_TOKEN=r8_<your_token_here>
💡 With Replicate, it’d take a minute for the mannequin to load into reminiscence on the server (cold-start downside), however as soon as it does the prediction ought to solely take a number of seconds. Relying in your native compute sources, operating on server could provide you with huge speedups in comparison with operating domestically, particularly for Marigold and different diffusion-based depth-estimation approaches.
Monocular Depth Estimation with DPT
The primary mannequin we’ll run is a dense-prediction transformer (DPT). DPT fashions have discovered utility in each MDE and semantic segmentation — duties that require “dense”, pixel-level predictions.
The checkpoint beneath makes use of MiDaS, which returns the inverse depth map, so now we have to invert it again to get a comparable depth map.
To run domestically with transformers, first we load the mannequin and picture processor:
from transformers import AutoImageProcessor, AutoModelForDepthEstimation
## swap for “Intel/dpt-large” if you happen to’d likepretrained = “Intel/dpt-hybrid-midas”
image_processor = AutoImageProcessor.from_pretrained(pretrained)dpt_model = AutoModelForDepthEstimation.from_pretrained(pretrained)
Subsequent, we encapsulate the code for inference on a pattern, together with pre and put up processing:
def apply_dpt_model(pattern, mannequin, label_field):picture = Picture.open(pattern.filepath)inputs = image_processor(photos=picture, return_tensors=”pt”)
with torch.no_grad():outputs = mannequin(**inputs)predicted_depth = outputs.predicted_depth
prediction = torch.nn.useful.interpolate(predicted_depth.unsqueeze(1),dimension=picture.dimension[::-1],mode=”bicubic”,align_corners=False,)
output = prediction.squeeze().cpu().numpy()## flip b/c MiDaS returns inverse depthformatted = (255 – output * 255 / np.max(output)).astype(“uint8”)
pattern[label_field] = fo.Heatmap(map=formatted)pattern.save()
Right here, we’re storing predictions in a label_field area on our samples, represented with a heatmap similar to the bottom fact labels.
Observe that within the apply_dpt_model() perform, between the mannequin’s ahead move and the heatmap era, discover that we make a name to torch.nn.useful.interpolate(). It’s because the mannequin’s ahead move is run on a downsampled model of the picture, and we need to return a heatmap that’s the identical dimension as the unique picture.
Why do we have to do that? If we simply need to *look* on the heatmaps, this is able to not matter. But when we need to examine the bottom fact depth maps to the mannequin’s predictions on a per-pixel foundation, we have to guarantee that they’re the identical dimension.
All that’s left to do is iterate via the dataset:
for pattern in dataset.iter_samples(autosave=True, progress=True):apply_dpt_model(pattern, dpt_model, “dpt”)
session = fo.launch_app(dataset)
To run with Replicate, you need to use this mannequin. Here’s what the API seems to be like:
import replicate
## instance utility to first samplergb_fp = dataset.first().filepath
output = replicate.run(“cjwbw/midas:a6ba5798f04f80d3b314de0f0a62277f21ab3503c60c84d4817de83c5edfdae0”,enter={“model_type”: “dpt_beit_large_512″,”picture”:open(rgb_fp, “rb”)})print(output)
Monocular Depth Estimation with Marigold
Stemming from their large success in text-to-image contexts, diffusion fashions are being utilized to an ever-broadening vary of issues. Marigold “repurposes” diffusion-based picture era fashions for monocular depth estimation.
To run Marigold domestically, you have to to clone the git repository:
git clone https://github.com/prs-eth/Marigold.git
This repository introduces a brand new diffusers pipeline, MarigoldPipeline, which makes making use of Marigold straightforward:
## load modelfrom Marigold.marigold import MarigoldPipelinepipe = MarigoldPipeline.from_pretrained(“Bingxin/Marigold”)
## apply to first pattern, as examplergb_image = Picture.open(dataset.first().filepath)output = pipe(rgb_image)depth_image = output[‘depth_colored’]
Put up-processing of the output depth picture is then wanted.
To as a substitute run by way of Replicate, we will create an apply_marigold_model() perform in analogy with the DPT case above and iterate over the samples in our dataset:
import replicateimport requestsimport io
def marigold_model(rgb_image):output = replicate.run(“adirik/marigold:1a363593bc4882684fc58042d19db5e13a810e44e02f8d4c32afd1eb30464818”,enter={“picture”:rgb_image})## get the black and white depth mapresponse = requests.get(output[1]).contentreturn response
def apply_marigold_model(pattern, mannequin, label_field):rgb_image = open(pattern.filepath, “rb”)response = mannequin(rgb_image)depth_image = np.array(Picture.open(io.BytesIO(response)))[:, :, 0] ## all channels are the sameformatted = (255 – depth_image).astype(“uint8”)pattern[label_field] = fo.Heatmap(map=formatted)pattern.save()
for pattern in dataset.iter_samples(autosave=True, progress=True):apply_marigold_model(pattern, marigold_model, “marigold”)
session = fo.launch_app(dataset)
Now that now we have predictions from a number of fashions, let’s consider them! We’ll leverage scikit-image to use three easy metrics generally used for monocular depth estimation: root imply squared error (RMSE), peak sign to noise ratio (PSNR), and structural similarity index (SSIM).
💡Increased PSNR and SSIM scores point out higher predictions, whereas decrease RMSE scores point out higher predictions.
Observe that the precise values I arrive at are a consequence of the precise pre-and-post processing steps I carried out alongside the way in which. What issues is the relative efficiency!
We’ll outline the analysis routine:
from skimage.metrics import peak_signal_noise_ratio, mean_squared_error, structural_similarity
def rmse(gt, pred):”””Compute root imply squared error between floor fact and prediction”””return np.sqrt(mean_squared_error(gt, pred))
def evaluate_depth(dataset, prediction_field, gt_field):”””Run 3 analysis metrics for all samples for `prediction_field`with respect to `gt_field`”””for pattern in dataset.iter_samples(autosave=True, progress=True):gt_map = pattern[gt_field].mappred = pattern[prediction_field]pred_map = pred.mappred[“rmse”] = rmse(gt_map, pred_map)pred[“psnr”] = peak_signal_noise_ratio(gt_map, pred_map)pred[“ssim”] = structural_similarity(gt_map, pred_map)pattern[prediction_field] = pred
## add dynamic fields to dataset so we will view them within the Appdataset.add_dynamic_sample_fields()
After which apply the analysis to the predictions from each fashions:
evaluate_depth(dataset, “dpt”, “gt_depth”)evaluate_depth(dataset, “marigold”, “gt_depth”)
Computing common efficiency for a sure mannequin/metric is so simple as calling the dataset’s imply()technique on that area:
print(“Imply Error Metrics”)for mannequin in [“dpt”, “marigold”]:print(“-“*50)for metric in [“rmse”, “psnr”, “ssim”]:mean_metric_value = dataset.imply(f”{mannequin}.{metric}”)print(f”Imply {metric} for {mannequin}: {mean_metric_value}”)Imply Error Metrics————————————————–Imply rmse for dpt: 49.8915828817003Mean psnr for dpt: 14.805904629602551Mean ssim for dpt: 0.8398022368184576————————————————–Imply rmse for marigold: 104.0061165272178Mean psnr for marigold: 7.93015537185192Mean ssim for marigold: 0.42766803372861134
The entire metrics appear to agree that DPT outperforms Marigold. Nevertheless, it is very important observe that these metrics should not excellent. For instance, RMSE may be very delicate to outliers, and SSIM will not be very delicate to small errors. For a extra thorough analysis, we will filter by these metrics within the app to be able to visualize what the mannequin is doing effectively and what it’s doing poorly — or the place the metrics are failing to seize the mannequin’s efficiency.
Lastly, toggling masks on and off is a good way to visualise the variations between the bottom fact and the mannequin’s predictions:
To recap, we discovered the best way to run monocular depth estimation fashions on our knowledge, the best way to consider the predictions utilizing widespread metrics, and the best way to visualize the outcomes. We additionally discovered that monocular depth estimation is a notoriously troublesome process.
Information high quality and amount are severely limiting elements; fashions typically battle to generalize to new environments; and metrics should not all the time good indicators of mannequin efficiency. The particular numeric values quantifying mannequin efficiency can rely enormously in your processing pipeline. And even your qualitative evaluation of predicted depth maps could be closely influenced by your shade schemes and opacity scales.
If there’s one factor you’re taking away from this put up, I hope it’s this: it’s mission-critical that you just have a look at the depth maps themselves, and never simply the metrics!
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