Transforming a 2D image into a 3D environment requires depth estimation, which can be a difficult operation depending on the amount of precision and information required. OpenCV supports a variety of depth estimation approaches, including stereo vision and depth from focus/defocus.
In this article we’ll see a basic technique utilizing stereovision:
Transform a 2D image into a 3D space using OpenCV
Transforming a 2D image into a 3D space using OpenCV refers to the process of converting a two-dimensional image into a three-dimensional spatial representation using the Open Source Computer Vision Library (OpenCV). This transformation involves inferring the depth information from the 2D image, typically through techniques such as stereo vision, depth estimation, or other computer vision algorithms, to create a 3D model with depth perception. This process enables various applications such as 3D reconstruction, depth sensing, and augmented reality.
Importance of transformations of a 2D image into a 3D space
Transforming 2D images into 3D space becomes crucial in various fields due to its numerous applications and benefits:
- Depth Perception: We are able to detect depth by transforming 2D pictures into 3D space. This makes it possible to use augmented reality, object recognition, and scene understanding.
- 3D Reconstruction: Converting 2D photos into 3D space makes it easier to recreate 3D scenes, which is crucial in industries like robotics, computer vision, and the preservation of cultural assets.
- Stereo Vision: Stereo vision depends on converting 2D images into 3D space. It entails taking pictures from various angles and calculating depth from the difference between matching spots. It is employed in 3D modeling, autonomous navigation, and depth sensing, among other applications.
- Medical Imaging: Improved visualization, diagnosis, and treatment planning are possible in medical imaging when 2D medical scans—such as CT or MRI scans—are converted into 3D space.
- Virtual Reality and Simulation: In virtual reality, simulation, and gaming, realistic 3D worlds must be constructed from 2D photos or video. This requires translating 2D visuals into 3D space.
How you get a 3D image from a 2D?
- In conventional photography, you can either utilize a mirror and attach a camera to it to create an immediate 3D effect, or you can take a shot, step to your right (or left), and then shoot another, ensuring that all components from the first photo are present in the second.
- However, if you just move a 2D picture left by 10 pixels, nothing changes. This is because you are shifting the entire environment, and no 3D information is saved.
- Instead, there must be a bigger shift distance between the foreground and backdrop. In other words, the farthest point distant from the lens remains motionless while the nearest point moves.
OpenCV
OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning software library written in C++ with interfaces for Python, Java, and MATLAB. It provides a wide range of features for processing and evaluating pictures and movies.
Some of its capabilities are:
- Image Processing: Filtering, edge detection, morphological operations, and color space conversions are just a few of the functions that OpenCV can do for image processing jobs.
- Feature detection and description: OpenCV offers methods for keypoint detection and descriptor computation, which are crucial for applications like motion tracking, object identification, and picture stitching.
- Object Identification and Recognition: OpenCV incorporates deep learning-based techniques, Haar cascades, and HOG algorithms for object detection and recognition.
- Stereo vision and camera calibration: These are supported by OpenCV, which may be used to estimate parameters and correct distortion. Additionally, it makes stereo vision jobs easier, such 3D reconstruction from stereo pictures and disparity mapping.
- Machine Learning Integration: Users may create and implement models for computer vision applications by integrating OpenCV with machine learning frameworks such as TensorFlow and PyTorch.
- Real-time Processing: Suitable for applications like gesture detection, augmented reality, and surveillance, OpenCV is designed for real-time processing and may be used with cameras and video streams.
How is this related to using a 2D image to create a 3D image?
We need a method to move the pixels since an picture becomes three-dimensional when the foreground moves more than the background.
Fortunately, a technique known as depth detection exists that generates what is known as a depth map.
Now remember that this is only an estimate. Furthermore, it won’t reach every nook and corner. All that depth detection does is use cues like shadows, haze, and depth of focus to determine if an object is in the forefront or background.
We can instruct a program on how far to move pixels now that we have a depth map.
Approach:
- Obtain Stereo Images: Take or obtain two pictures of the same scene taken from two separate perspectives.
- Stereo Calibration: For precise depth estimation, ascertain each camera’s intrinsic and extrinsic properties.
- Rectification: To make matching easier, make sure corresponding spots in stereo pictures line up on the same scanlines.
- Stereo matching: Use methods such as block matching or SGBM to find correspondences between corrected stereo pictures.
- Disparity: Calculate the disparity by dividing the horizontal locations of corresponding points by their pixels.
- Depth Estimation: Using camera settings and stereo geometry, convert disparity map to depth map.
- 3D Reconstruction: Using the depth map as a guide, reconstruct the scene’s three dimensions.
Implementations of a 2D image into a 3D space Transformations using OpenCV
Input image:
Code steps:
-
First we have imported the required libraries.
- PIL (Python Imaging Library), which is used to work with images
- numpy is used for numerical computations
- Then we have defined a function named `shift_image` that takes three parameters: `img`, `depth_img`, and `shift_amount`. `img` is the base image that will be shifted, `depth_img` is the depth map providing depth information for the shift, and `shift_amount`specifies the maximum amount of horizontal shift allowed.
- After that we have converted the input images (`img` and `depth_img`) into arrays using NumPy for further processing. The base image (img) is converted to RGBA format to ensure it has an alpha channel, while the depth image (depth_img) is converted to grayscale (`L`) to ensure it contains only one channel representing depth values.
- Then calculates the shift amounts based on the depth values obtained from the depth image. It scales the depth values to the range [0, 1] and then multiplies it by the `shift_amount`. The result is then converted to integers using `astype(int)`.
- Then initializes an array (`shifted_data`) with the same shape as the base image (`data`) filled with zeros. This array will store the shifted image data.
- The nested loops iterate over the elements of the `deltas` array to perform the shift operation. For each pixel position (x, y) in the base image, the corresponding shift amount `dx` is applied horizontally. If the resulting position (x + dx, y) is within the bounds of the image, the pixel value at (x, y) in the base image is copied to (x + dx, y) in the shifted image.
- Image.fromarray converts the shifted image data (`shifted_data`) back to a PIL Image object. The data is converted to np.uint8 type before creating the image.
- Then we read the images from our local files and applied to the newly created functions.
- shifted_img.show will plot the newly transformed image
from PIL import Image
import numpy as np
def shift_image(img, depth_img, shift_amount=10):
# Ensure base image has alpha
img = img.convert("RGBA")
data = np.array(img)
# Ensure depth image is grayscale (for single value)
depth_img = depth_img.convert("L")
depth_data = np.array(depth_img)
deltas = ((depth_data / 255.0) * float(shift_amount)).astype(int)
# This creates the transparent resulting image.
# For now, we're dealing with pixel data.
shifted_data = np.zeros_like(data)
height, width, _ = data.shape
for y, row in enumerate(deltas):
for x, dx in enumerate(row):
if x + dx < width and x + dx >= 0:
shifted_data[y, x + dx] = data[y, x]
# Convert the pixel data to an image.
shifted_image = Image.fromarray(shifted_data.astype(np.uint8))
return shifted_image
img = Image.open("cube1.jpg")
depth_img = Image.open("cube2.jpg")
shifted_img = shift_image(img, depth_img, shift_amount=10)
shifted_img.show() |
Output:
transformed image
Limitations of OpenCV to 2D image into a 3D space image tranformations
The process of simulating a 3D effect by shifting pixels based on depth information has several limitations also:
- Simplified Depth Representation: Since the depth map is a grayscale picture, intricate depth fluctuations might not be properly captured.
- It Only moves pixels horizontally, assuming that all depth fluctuations are horizontal. This assumption might not hold true in scenarios found in the actual world.
- Uniform Shift Amount: Unrealistic effects may result from the linear connection between depth and pixel shift, which may not adequately reflect real-world depth perception.
- Limited Depth Cues: It does not use other depth cues such as perspective distortion or occlusion, instead depending solely on the parallax effect.
- Boundary Artifacts: Boundary artifacts may arise from the disregard of pixels that have been pushed outside the limits of the picture.
- Dependency on Parameter: The shift amount parameter that is selected has a significant impact on the 3D effect’s quality.
- Limited Application Scope: Although it works well for basic 3D effects, it might not be accurate enough for complex 3D activities like medical imaging or augmented reality.
Conclusion
In summary, using OpenCV in Python to convert a 2D picture into a 3D space entails a number of steps, including the capture of stereo images, calibration, rectification, matching, disparity computation, depth estimate, and, in the end, 3D scene reconstruction. The extraction of spatial information from 2D pictures is made possible by this all-inclusive methodology, which makes depth sensing, augmented reality, and computer vision applications easier to use.