Implementing Monocular Depth Estimation with Intel MiDaS
Monocular depth estimation is an essential process in computer vision that entails predicting the depth of a scene from a single RGB image. This capability has a variety of applications, including augmented reality, robotics, and enhancing 3D scene understanding. In this guide, we will explore how to implement Intel’s MiDaS (Monocular Depth Estimation via a Multi-Scale Vision Transformer), a cutting-edge model that provides high-quality depth predictions from single images.
Getting Started
We will utilize Google Colab as our computational environment, along with Python libraries such as PyTorch for model building, OpenCV for image processing, and Matplotlib for visualization. This setup allows for straightforward image uploads and depth map visualizations.
Step 1: Install Required Libraries
To begin, we need to install several Python libraries:
- timm: For model support
- opencv-python: For image processing
- matplotlib: For visualizing depth maps
Use the following command in your Colab notebook:
!pip install -q timm opencv-python matplotlib
Step 2: Clone the MiDaS Repository
Next, we will clone the official Intel MiDaS repository from GitHub. This action allows us to access the model code and necessary utilities:
!git clone
%cd MiDaS
Step 3: Import Required Libraries
To load the model and preprocess images, we need to import several libraries:
import torch
import cv2
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
from forms import Compose
from import files
from _depth import DPTDepthModel
from forms import Resize, NormalizeImage, PrepareForNet
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
Step 4: Load the Pretrained Model
We will now download the pretrained MiDaS DPT_Large model and set it to evaluation mode:
model_path = ("intel-isl/MiDaS", "DPT_Large", pretrained=True, force_reload=True)
model = DPTDepthModel(model_path).to(device)
Step 5: Define the Image Preprocessing Pipeline
We need to set up an image preprocessing pipeline that will resize, normalize, and prepare the images for model inference:
transform = Compose([
Resize(384, 384, resize_target=None, keep_aspect_ratio=True, ensure_multiple_of=32, resize_method="upper_bound"),
NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
PrepareForNet()
])
Step 6: Upload and Process Image
We will allow users to upload an image, convert its color format, and prepare it for depth prediction:
uploaded = files.upload()
for filename in uploaded:
img = cv2.imread(filename)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
break
Step 7: Depth Prediction
After uploading, we’ll convert the image to a tensor format, perform the depth prediction using the MiDaS model, and resize the output:
img_input = transform({"image": img})["image"]
input_tensor = torch.from_numpy(img_input).unsqueeze(0).to(device)
with torch.no_grad():
prediction = model(input_tensor)
depth_map = prediction.squeeze().cpu().numpy()
Step 8: Visualize Results
Finally, we will visualize the original image alongside its corresponding depth map:
plt.figure(figsize=(10, 5))
plt.subplot(1, 2, 1)
plt.imshow(img)
plt.title("Original Image")
plt.axis("off")
plt.subplot(1, 2, 2)
plt.imshow(depth_map, cmap='inferno')
plt.title("Depth Map")
plt.axis("off")
plt.show()
Conclusion
Through this guide, we successfully implemented Intel’s MiDaS model on Google Colab for monocular depth estimation using just a single RGB image. This robust pipeline, built with PyTorch, OpenCV, and Matplotlib, provides a solid foundation for further applications, such as video depth estimation, real-time usage, and integration into AR/VR systems.
Next Steps
To explore more applications of artificial intelligence in your business, consider the following:
- Identify processes that can be automated to improve efficiency.
- Look for customer interaction points where AI can add significant value.
- Establish key performance indicators (KPIs) to measure the effectiveness of your AI initiatives.
- Select customizable AI tools that align with your organizational goals.
- Start with small projects, assess their impact, and gradually expand your AI implementations.
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