Depth estimation with DepthAnything and OpenVINO

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Depth Anything is a highly practical solution for robust monocular depth estimation. Without pursuing novel technical modules, this project aims to build a simple yet powerful foundation model dealing with any images under any circumstances. The framework of Depth Anything is shown below. it adopts a standard pipeline to unleashing the power of large-scale unlabeled images.

More details about model can be found in project web page, paper and official repository

In this tutorial we will explore how to convert and run DepthAnything using OpenVINO. An additional part demonstrates how to run quantization with NNCF to speed up the model.

Table of contents:

• Prerequisites

• Load and run PyTorch model

  • Prepare input data

  • Run model inference

• Convert Model to OpenVINO IR format

• Run OpenVINO model inference

  • Select inference device

  • Run inference on image

  • Run inference on video

• Quantization

  • Prepare calibration dataset

  • Run quantization

  • Compare inference time of the FP16 and INT8 models

  • Compare model file size

• Interactive demo

Prerequisites

from pathlib import Path
import platform

repo_dir = Path("Depth-Anything")

if not repo_dir.exists():
    !git clone https://github.com/LiheYoung/Depth-Anything
%cd Depth-Anything

%pip install -q "openvino>=2023.3.0" "datasets>=2.14.6" "nncf" "tqdm"
%pip install -q "typing-extensions>=4.9.0" eval-type-backport "gradio>=4.19"
%pip install -q -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cpu

if platform.python_version_tuple()[1] in ["8", "9"]:
    %pip install -q "gradio-imageslider<=0.0.17" "typing-extensions>=4.9.0"

Cloning into 'Depth-Anything'...
remote: Enumerating objects: 421, done.
remote: Counting objects: 100% (144/144), done.
remote: Compressing objects: 100% (105/105), done.
remote: Total 421 (delta 101), reused 43 (delta 39), pack-reused 277
Receiving objects: 100% (421/421), 237.89 MiB | 26.48 MiB/s, done.
Resolving deltas: 100% (144/144), done.
/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything
Note: you may need to restart the kernel to use updated packages.
Note: you may need to restart the kernel to use updated packages.
WARNING: typer 0.12.3 does not provide the extra 'all'
Note: you may need to restart the kernel to use updated packages.
Note: you may need to restart the kernel to use updated packages.

Load and run PyTorch model

To be able run PyTorch model on CPU, we should disable xformers attention optimizations first.

attention_file_path = Path("./torchhub/facebookresearch_dinov2_main/dinov2/layers/attention.py")
orig_attention_path = attention_file_path.parent / ("orig_" + attention_file_path.name)

if not orig_attention_path.exists():
    attention_file_path.rename(orig_attention_path)

    with orig_attention_path.open("r") as f:
        data = f.read()
        data = data.replace("XFORMERS_AVAILABLE = True", "XFORMERS_AVAILABLE = False")
        with attention_file_path.open("w") as out_f:
            out_f.write(data)

DepthAnything.from_pretrained method creates PyTorch model class instance and load model weights. There are 3 available models in repository depends on VIT encoder size: * Depth-Anything-ViT-Small (24.8M) * Depth-Anything-ViT-Base (97.5M) * Depth-Anything-ViT-Large (335.3M)

We will use Depth-Anything-ViT-Small, but the same steps for running model and converting to OpenVINO are applicable for other models from DepthAnything family.

from depth_anything.dpt import DepthAnything

encoder = "vits"  # can also be 'vitb' or 'vitl'
model_id = "depth_anything_{:}14".format(encoder)
depth_anything = DepthAnything.from_pretrained(f"LiheYoung/{model_id}")

xFormers not available
xFormers not available

Prepare input data

from PIL import Image

import requests

r = requests.get(
    url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
)

open("notebook_utils.py", "w").write(r.text)
from notebook_utils import download_file

download_file(
    "https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/3f779fc1-c1b2-4dec-915a-64dae510a2bb",
    "furseal.png",
)

Image.open("furseal.png").resize((600, 400))

furseal.png:   0%|          | 0.00/2.55M [00:00<?, ?B/s]

for simplicity of usage, model authors provide helper functions for preprocessing input image. The main conditions are that image size should be divisible on 14 (size of vit patch) and normalized in [0, 1] range.

from depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet
from torchvision.transforms import Compose

import cv2
import torch

transform = Compose(
    [
        Resize(
            width=518,
            height=518,
            resize_target=False,
            ensure_multiple_of=14,
            resize_method="lower_bound",
            image_interpolation_method=cv2.INTER_CUBIC,
        ),
        NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
        PrepareForNet(),
    ]
)


image = cv2.cvtColor(cv2.imread("furseal.png"), cv2.COLOR_BGR2RGB) / 255.0
h, w = image.shape[:-1]
image = transform({"image": image})["image"]
image = torch.from_numpy(image).unsqueeze(0)

Run model inference

Preprocessed image passed to model forward and model returns depth map in format B x H x W, where B is input batch size, H is preprocessed image height, W is preprocessed image width.

# depth shape: 1xHxW
depth = depth_anything(image)

After image processing finished, we can resize depth map into original image size and prepare it for visualization.

import torch.nn.functional as F
import numpy as np

depth = F.interpolate(depth[None], (h, w), mode="bilinear", align_corners=False)[0, 0]
depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0

depth = depth.cpu().detach().numpy().astype(np.uint8)
depth_color = cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO)

from matplotlib import pyplot as plt

plt.imshow(depth_color[:, :, ::-1]);

Convert Model to OpenVINO IR format

OpenVINO supports PyTorch models via conversion to OpenVINO Intermediate Representation (IR). OpenVINO model conversion API should be used for these purposes. ov.convert_model function accepts original PyTorch model instance and example input for tracing and returns ov.Model representing this model in OpenVINO framework. Converted model can be used for saving on disk using ov.save_model function or directly loading on device using core.complie_model.

import openvino as ov

OV_DEPTH_ANYTHING_PATH = Path(f"{model_id}.xml")

if not OV_DEPTH_ANYTHING_PATH.exists():
    ov_model = ov.convert_model(depth_anything, example_input=image, input=[1, 3, 518, 518])
    ov.save_model(ov_model, OV_DEPTH_ANYTHING_PATH)

/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/torchhub/facebookresearch_dinov2_main/dinov2/layers/patch_embed.py:73: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  assert H % patch_H == 0, f"Input image height {H} is not a multiple of patch height {patch_H}"
/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/torchhub/facebookresearch_dinov2_main/dinov2/layers/patch_embed.py:74: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  assert W % patch_W == 0, f"Input image width {W} is not a multiple of patch width: {patch_W}"
/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/torchhub/facebookresearch_dinov2_main/vision_transformer.py:183: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  if npatch == N and w == h:
/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/depth_anything/dpt.py:133: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  out = F.interpolate(out, (int(patch_h * 14), int(patch_w * 14)), mode="bilinear", align_corners=True)

Run OpenVINO model inference

Now, we are ready to run OpenVINO model

Select inference device

For starting work, please select inference device from dropdown list.

import ipywidgets as widgets

core = ov.Core()

device = widgets.Dropdown(
    options=core.available_devices + ["AUTO"],
    value="AUTO",
    description="Device:",
    disabled=False,
)

device

Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')

compiled_model = core.compile_model(OV_DEPTH_ANYTHING_PATH, device.value)

Run inference on image

res = compiled_model(image)[0]

def get_depth_map(model_output):
    depth = model_output[0]
    depth = cv2.resize(depth, (w, h))
    depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0

    depth = depth.astype(np.uint8)
    depth_color = cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO)
    return depth_color


depth_color = get_depth_map(res)

plt.imshow(depth_color[:, :, ::-1]);

Run inference on video

download_file(
    "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/video/Coco%20Walking%20in%20Berkeley.mp4",
    "./Coco Walking in Berkeley.mp4",
)

VIDEO_FILE = "./Coco Walking in Berkeley.mp4"
# Number of seconds of input video to process. Set `NUM_SECONDS` to 0 to process
# the full video.
NUM_SECONDS = 4
# Set `ADVANCE_FRAMES` to 1 to process every frame from the input video
# Set `ADVANCE_FRAMES` to 2 to process every second frame. This reduces
# the time it takes to process the video.
ADVANCE_FRAMES = 2
# Set `SCALE_OUTPUT` to reduce the size of the result video
# If `SCALE_OUTPUT` is 0.5, the width and height of the result video
# will be half the width and height of the input video.
SCALE_OUTPUT = 0.5
# The format to use for video encoding. The 'vp09` is slow,
# but it works on most systems.
# Try the `THEO` encoding if you have FFMPEG installed.
# FOURCC = cv2.VideoWriter_fourcc(*"THEO")
FOURCC = cv2.VideoWriter_fourcc(*"vp09")

# Create Path objects for the input video and the result video.
output_directory = Path("output")
output_directory.mkdir(exist_ok=True)
result_video_path = output_directory / f"{Path(VIDEO_FILE).stem}_depth_anything.mp4"

Coco Walking in Berkeley.mp4:   0%|          | 0.00/877k [00:00<?, ?B/s]

cap = cv2.VideoCapture(str(VIDEO_FILE))
ret, image = cap.read()
if not ret:
    raise ValueError(f"The video at {VIDEO_FILE} cannot be read.")
input_fps = cap.get(cv2.CAP_PROP_FPS)
input_video_frame_height, input_video_frame_width = image.shape[:2]

target_fps = input_fps / ADVANCE_FRAMES
target_frame_height = int(input_video_frame_height * SCALE_OUTPUT)
target_frame_width = int(input_video_frame_width * SCALE_OUTPUT)

cap.release()
print(f"The input video has a frame width of {input_video_frame_width}, " f"frame height of {input_video_frame_height} and runs at {input_fps:.2f} fps")
print(
    "The output video will be scaled with a factor "
    f"{SCALE_OUTPUT}, have width {target_frame_width}, "
    f" height {target_frame_height}, and run at {target_fps:.2f} fps"
)

The input video has a frame width of 640, frame height of 360 and runs at 30.00 fps
The output video will be scaled with a factor 0.5, have width 320,  height 180, and run at 15.00 fps

def normalize_minmax(data):
    """Normalizes the values in `data` between 0 and 1"""
    return (data - data.min()) / (data.max() - data.min())


def convert_result_to_image(result, colormap="viridis"):
    """
    Convert network result of floating point numbers to an RGB image with
    integer values from 0-255 by applying a colormap.

    `result` is expected to be a single network result in 1,H,W shape
    `colormap` is a matplotlib colormap.
    See https://matplotlib.org/stable/tutorials/colors/colormaps.html
    """
    result = result.squeeze(0)
    result = normalize_minmax(result)
    result = result * 255
    result = result.astype(np.uint8)
    result = cv2.applyColorMap(result, cv2.COLORMAP_INFERNO)[:, :, ::-1]
    return result


def to_rgb(image_data) -> np.ndarray:
    """
    Convert image_data from BGR to RGB
    """
    return cv2.cvtColor(image_data, cv2.COLOR_BGR2RGB)

import time
from IPython.display import (
    HTML,
    FileLink,
    Pretty,
    ProgressBar,
    Video,
    clear_output,
    display,
)


def process_video(compiled_model, video_file, result_video_path):
    # Initialize variables.
    input_video_frame_nr = 0
    start_time = time.perf_counter()
    total_inference_duration = 0

    # Open the input video
    cap = cv2.VideoCapture(str(video_file))

    # Create a result video.
    out_video = cv2.VideoWriter(
        str(result_video_path),
        FOURCC,
        target_fps,
        (target_frame_width * 2, target_frame_height),
    )

    num_frames = int(NUM_SECONDS * input_fps)
    total_frames = cap.get(cv2.CAP_PROP_FRAME_COUNT) if num_frames == 0 else num_frames
    progress_bar = ProgressBar(total=total_frames)
    progress_bar.display()

    try:
        while cap.isOpened():
            ret, image = cap.read()
            if not ret:
                cap.release()
                break

            if input_video_frame_nr >= total_frames:
                break

            h, w = image.shape[:-1]
            input_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) / 255.0
            input_image = transform({"image": input_image})["image"]
            # Reshape the image to network input shape NCHW.
            input_image = np.expand_dims(input_image, 0)

            # Do inference.
            inference_start_time = time.perf_counter()
            result = compiled_model(input_image)[0]
            inference_stop_time = time.perf_counter()
            inference_duration = inference_stop_time - inference_start_time
            total_inference_duration += inference_duration

            if input_video_frame_nr % (10 * ADVANCE_FRAMES) == 0:
                clear_output(wait=True)
                progress_bar.display()
                # input_video_frame_nr // ADVANCE_FRAMES gives the number of
                # Frames that have been processed by the network.
                display(
                    Pretty(
                        f"Processed frame {input_video_frame_nr // ADVANCE_FRAMES}"
                        f"/{total_frames // ADVANCE_FRAMES}. "
                        f"Inference time per frame: {inference_duration:.2f} seconds "
                        f"({1/inference_duration:.2f} FPS)"
                    )
                )

            # Transform the network result to a RGB image.
            result_frame = to_rgb(convert_result_to_image(result))
            # Resize the image and the result to a target frame shape.
            result_frame = cv2.resize(result_frame, (target_frame_width, target_frame_height))
            image = cv2.resize(image, (target_frame_width, target_frame_height))
            # Put the image and the result side by side.
            stacked_frame = np.hstack((image, result_frame))
            # Save a frame to the video.
            out_video.write(stacked_frame)

            input_video_frame_nr = input_video_frame_nr + ADVANCE_FRAMES
            cap.set(1, input_video_frame_nr)

            progress_bar.progress = input_video_frame_nr
            progress_bar.update()

    except KeyboardInterrupt:
        print("Processing interrupted.")
    finally:
        clear_output()
        processed_frames = num_frames // ADVANCE_FRAMES
        out_video.release()
        cap.release()
        end_time = time.perf_counter()
        duration = end_time - start_time

        print(
            f"Processed {processed_frames} frames in {duration:.2f} seconds. "
            f"Total FPS (including video processing): {processed_frames/duration:.2f}."
            f"Inference FPS: {processed_frames/total_inference_duration:.2f} "
        )
        print(f"Video saved to '{str(result_video_path)}'.")
    return stacked_frame

stacked_frame = process_video(compiled_model, VIDEO_FILE, result_video_path)

Processed 60 frames in 13.33 seconds. Total FPS (including video processing): 4.50.Inference FPS: 10.52
Video saved to 'output/Coco Walking in Berkeley_depth_anything.mp4'.

def display_video(stacked_frame):
    video = Video(result_video_path, width=800, embed=True)
    if not result_video_path.exists():
        plt.imshow(stacked_frame)
        raise ValueError("OpenCV was unable to write the video file. Showing one video frame.")
    else:
        print(f"Showing video saved at\n{result_video_path.resolve()}")
        print("If you cannot see the video in your browser, please click on the " "following link to download the video ")
        video_link = FileLink(result_video_path)
        video_link.html_link_str = "<a href='%s' download>%s</a>"
        display(HTML(video_link._repr_html_()))
        display(video)

display_video(stacked_frame)

Showing video saved at
/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/output/Coco Walking in Berkeley_depth_anything.mp4
If you cannot see the video in your browser, please click on the following link to download the video

output/Coco Walking in Berkeley_depth_anything.mp4

Quantization

NNCF enables post-training quantization by adding quantization layers into model graph and then using a subset of the training dataset to initialize the parameters of these additional quantization layers. Quantized operations are executed in INT8 instead of FP32/FP16 making model inference faster.

The optimization process contains the following steps:

1. Create a calibration dataset for quantization.

2. Run nncf.quantize() to obtain quantized model.

3. Save the INT8 model using openvino.save_model() function.

Please select below whether you would like to run quantization to improve model inference speed.

to_quantize = widgets.Checkbox(
    value=True,
    description="Quantization",
    disabled=False,
)

to_quantize

Checkbox(value=True, description='Quantization')

# Fetch `skip_kernel_extension` module
r = requests.get(
    url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
)
open("skip_kernel_extension.py", "w").write(r.text)

OV_DEPTH_ANYTHING_INT8_PATH = Path(f"{model_id}_int8.xml")

%load_ext skip_kernel_extension

Prepare calibration dataset

We use a portion of Nahrawy/VIDIT-Depth-ControlNet dataset from Hugging Face as calibration data.

%%skip not $to_quantize.value

import datasets

if not OV_DEPTH_ANYTHING_INT8_PATH.exists():
    subset_size = 300
    calibration_data = []
    dataset = datasets.load_dataset("Nahrawy/VIDIT-Depth-ControlNet", split="train", streaming=True).shuffle(seed=42).take(subset_size)
    for batch in dataset:
        image = np.array(batch["image"])[...,:3]
        image = image / 255.0
        image = transform({'image': image})['image']
        image = np.expand_dims(image, 0)
        calibration_data.append(image)

Resolving data files:   0%|          | 0/42 [00:00<?, ?it/s]

Run quantization

Create a quantized model from the pre-trained converted OpenVINO model. > NOTE: Quantization is time and memory consuming operation. Running quantization code below may take some time.

%%skip not $to_quantize.value

import nncf

if not OV_DEPTH_ANYTHING_INT8_PATH.exists():
    model = core.read_model(OV_DEPTH_ANYTHING_PATH)
    quantized_model = nncf.quantize(
        model=model,
        subset_size=subset_size,
        model_type=nncf.ModelType.TRANSFORMER,
        calibration_dataset=nncf.Dataset(calibration_data),
    )
    ov.save_model(quantized_model, OV_DEPTH_ANYTHING_INT8_PATH)

INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino

2024-05-06 23:52:17.005825: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0.
2024-05-06 23:52:17.039208: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-05-06 23:52:17.604619: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT

Output()

Output()

INFO:nncf:36 ignored nodes were found by name in the NNCFGraph
INFO:nncf:48 ignored nodes were found by name in the NNCFGraph

Output()

Output()

Let us check predictions with the quantized model using the same input data.

%%skip not $to_quantize.value

def visualize_results(orig_img:Image.Image, optimized_img:Image.Image):
    """
    Helper function for results visualization

    Parameters:
       orig_img (Image.Image): generated image using FP16 model
       optimized_img (Image.Image): generated image using quantized model
    Returns:
       fig (matplotlib.pyplot.Figure): matplotlib generated figure contains drawing result
    """
    orig_title = "FP16 model"
    control_title = "INT8 model"
    figsize = (20, 20)
    fig, axs = plt.subplots(1, 2, figsize=figsize, sharex='all', sharey='all')
    list_axes = list(axs.flat)
    for a in list_axes:
        a.set_xticklabels([])
        a.set_yticklabels([])
        a.get_xaxis().set_visible(False)
        a.get_yaxis().set_visible(False)
        a.grid(False)
    list_axes[0].imshow(np.array(orig_img))
    list_axes[1].imshow(np.array(optimized_img))
    list_axes[0].set_title(orig_title, fontsize=15)
    list_axes[1].set_title(control_title, fontsize=15)

    fig.subplots_adjust(wspace=0.01, hspace=0.01)
    fig.tight_layout()
    return fig

%%skip not $to_quantize.value

image = cv2.cvtColor(cv2.imread('furseal.png'), cv2.COLOR_BGR2RGB) / 255.0
image = transform({'image': image})['image']
image = torch.from_numpy(image).unsqueeze(0)

int8_compiled_model = core.compile_model(OV_DEPTH_ANYTHING_INT8_PATH, device.value)
int8_res = int8_compiled_model(image)[0]
int8_depth_color = get_depth_map(int8_res)

%%skip not $to_quantize.value

visualize_results(depth_color[:, :, ::-1], int8_depth_color[:, :, ::-1])

%%skip not $to_quantize.value

int8_result_video_path = output_directory / f"{Path(VIDEO_FILE).stem}_depth_anything_int8.mp4"
stacked_frame = process_video(int8_compiled_model, VIDEO_FILE, int8_result_video_path)
display_video(stacked_frame)

Processed 60 frames in 12.84 seconds. Total FPS (including video processing): 4.67.Inference FPS: 12.79
Video saved to 'output/Coco Walking in Berkeley_depth_anything_int8.mp4'.
Showing video saved at
/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/output/Coco Walking in Berkeley_depth_anything.mp4
If you cannot see the video in your browser, please click on the following link to download the video

output/Coco Walking in Berkeley_depth_anything.mp4

Compare model file size

%%skip not $to_quantize.value

fp16_ir_model_size = OV_DEPTH_ANYTHING_PATH.with_suffix(".bin").stat().st_size / 2**20
quantized_model_size = OV_DEPTH_ANYTHING_INT8_PATH.with_suffix(".bin").stat().st_size / 2**20

print(f"FP16 model size: {fp16_ir_model_size:.2f} MB")
print(f"INT8 model size: {quantized_model_size:.2f} MB")
print(f"Model compression rate: {fp16_ir_model_size / quantized_model_size:.3f}")

FP16 model size: 47.11 MB
INT8 model size: 24.41 MB
Model compression rate: 1.930

To measure the inference performance of OpenVINO FP16 and INT8 models, use Benchmark Tool.

NOTE: For the most accurate performance estimation, it is recommended to run benchmark_app in a terminal/command prompt after closing other applications.

import re


def get_fps(benchmark_output: str):
    parsed_output = [line for line in benchmark_output if "Throughput:" in line]
    fps = re.findall(r"\d+\.\d+", parsed_output[0])[0]
    return fps


if OV_DEPTH_ANYTHING_INT8_PATH.exists():
    benchmark_output = !benchmark_app -m $OV_DEPTH_ANYTHING_PATH -d $device.value -api async
    original_fps = get_fps(benchmark_output)
    print(f"FP16 Throughput: {original_fps} FPS")

    benchmark_output = !benchmark_app -m $OV_DEPTH_ANYTHING_INT8_PATH -d $device.value -api async
    optimized_fps = get_fps(benchmark_output)
    print(f"INT8 Throughput: {optimized_fps} FPS")
    print(f"Speed-up: {float(optimized_fps) / float(original_fps):.2f}")

FP16 Throughput: 10.69 FPS
INT8 Throughput: 14.04 FPS
Speed-up: 1.31

Interactive demo

You can apply model on own images. You can move the slider on the resulting image to switch between the original image and the depth map view.

Please select below whether you would like to use the quantized model to launch the interactive demo.

quantized_model_present = OV_DEPTH_ANYTHING_INT8_PATH.exists()

use_quantized_model = widgets.Checkbox(
    value=True if quantized_model_present else False,
    description="Use quantized model",
    disabled=False,
)

use_quantized_model

Checkbox(value=True, description='Use quantized model')

import gradio as gr
import cv2
import numpy as np
import os
import tempfile
from gradio_imageslider import ImageSlider

css = """
#img-display-container {
    max-height: 100vh;
    }
#img-display-input {
    max-height: 80vh;
    }
#img-display-output {
    max-height: 80vh;
    }
"""


title = "# Depth Anything with OpenVINO"

if use_quantized_model.value:
    compiled_model = core.compile_model(OV_DEPTH_ANYTHING_INT8_PATH, device.value)


def predict_depth(model, image):
    return model(image)[0]


with gr.Blocks(css=css) as demo:
    gr.Markdown(title)
    gr.Markdown("### Depth Prediction demo")
    gr.Markdown("You can slide the output to compare the depth prediction with input image")

    with gr.Row():
        input_image = gr.Image(label="Input Image", type="numpy", elem_id="img-display-input")
        depth_image_slider = ImageSlider(label="Depth Map with Slider View", elem_id="img-display-output", position=0)
    raw_file = gr.File(label="16-bit raw depth (can be considered as disparity)")
    submit = gr.Button("Submit")

    def on_submit(image):
        original_image = image.copy()

        h, w = image.shape[:2]

        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) / 255.0
        image = transform({"image": image})["image"]
        image = np.expand_dims(image, 0)

        depth = predict_depth(compiled_model, image)
        depth = cv2.resize(depth[0], (w, h), interpolation=cv2.INTER_LINEAR)

        raw_depth = Image.fromarray(depth.astype("uint16"))
        tmp = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
        raw_depth.save(tmp.name)

        depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
        depth = depth.astype(np.uint8)
        colored_depth = cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO)[:, :, ::-1]

        return [(original_image, colored_depth), tmp.name]

    submit.click(on_submit, inputs=[input_image], outputs=[depth_image_slider, raw_file])

    example_files = os.listdir("assets/examples")
    example_files.sort()
    example_files = [os.path.join("assets/examples", filename) for filename in example_files]
    examples = gr.Examples(
        examples=example_files,
        inputs=[input_image],
        outputs=[depth_image_slider, raw_file],
        fn=on_submit,
        cache_examples=False,
    )


if __name__ == "__main__":
    try:
        demo.queue().launch(debug=False)
    except Exception:
        demo.queue().launch(share=True, debug=False)
# if you are launching remotely, specify server_name and server_port
# demo.launch(server_name='your server name', server_port='server port in int')
# Read more in the docs: https://gradio.app/docs/

Running on local URL:  http://127.0.0.1:7860

To create a public link, set share=True in launch().