本文将带您了解关于AzureComputerVision之SmartCrop智能裁剪图片的新内容，同时我们还将为您解释azureautomation的相关知识，另外，我们还将为您提供关于3DCompu
本文将带您了解关于Azure Computer Vision 之 Smart Crop 智能裁剪图片的新内容，同时我们还将为您解释azure automation的相关知识，另外，我们还将为您提供关于3D Computer Vision、android studio问题-E / Vision：加载可选模块com.google.android.gms.vision.ocr时出错、awesome computer vision repo、Computer Vision的实用信息。
本文目录一览：- Azure Computer Vision 之 Smart Crop 智能裁剪图片（azure automation）
- 3D Computer Vision
- android studio问题-E / Vision：加载可选模块com.google.android.gms.vision.ocr时出错
- awesome computer vision repo
- Computer Vision
Azure Computer Vision 之 Smart Crop 智能裁剪图片（azure automation）
前言
一个网站通常有许多地方会用到同一张图，但是比例又不一样.
一般的做法就是用 CSS 的 cover 和 contain 来处理.
由于 cover 只会保留中间信息, 所以很多时候需要人工裁剪.
于是就有了智能裁剪的需求了.
Azure Computer Vision
参考:
官网示范
价格
价格还可以
Azure SDK for .NET
Sample code
实现步骤
1. 到 Azure portal 创建 Computer Vision
没有什么特别的, 默认就可以了 (注: 一个 account 只能有一个 free 的 Computer Vision 哦)
2. 进入 Computer Vision Resource > Keys and Endpoint 把 key 和 endpoint 抄起来
3. 安装 SDK
dotnet add package Microsoft.Azure.CognitiveServices.Vision.ComputerVision
2 个核心功能, 第 1 个是获取全图焦点, 第 2 个是给定要求智能裁剪
[HttpPost("SmartCrop")]
public async Task<ActionResult> SmartCropAsync()
{
var subscriptionKey = "key";
var endpoint = "https://jbreviews-cv.cognitiveservices.azure.com/";
var client = new ComputerVisionClient(new ApiKeyServiceClientCredentials(subscriptionKey))
{
Endpoint = endpoint
};
var imageFileFullPath = @"WebApi\\Controller\\Test\\SmartCrop\\10.png";
using var imagestream = new FileStream(imageFileFullPath, FileMode.Open);
// get area of interest
var areaOfInterestResult = await client.GetAreaOfInterestInStreamAsync(imagestream); // 这里返回之后 imagestream 就自动被 close 了
using var image = Image.Load(imageFileFullPath);
var croppedImage = image.Clone(imageProcessing =>
{
imageProcessing.Crop(new Rectangle(
x: areaOfInterestResult.AreaOfInterest.X,
y: areaOfInterestResult.AreaOfInterest.Y,
width: areaOfInterestResult.AreaOfInterest.W,
height: areaOfInterestResult.AreaOfInterest.H)
);
});
croppedImage.SaveAsJpeg(
@"WebApi\\Controller\\Test\\SmartCrop\\11.png", new SixLabors.ImageSharp.Formats.Jpeg.JpegEncoder
{
Quality = 85
}
);
// get smart crop image
using var imagestream2 = new FileStream(imageFileFullPath, FileMode.Open);
var croppedimagestream = await client.GenerateThumbnailInStreamAsync(300, 100, imagestream2, smartCropping: true);
using var imageFileStream = System.IO.File.Create(@"WebApi\\Controller\\Test\\SmartCrop\\12.png");
croppedimagestream.copyTo(imageFileStream);
return Ok();
}
3D Computer Vision
3D Computer Vision
Programming Assignment 2 – Epipolar Geometry
You will upload your codes and a short report (PDF) in a zip file to the NewE3 system. Grading will be done
at demo time (face-to-face or Skype).
A C++ Visual Studio project is provided. To build the code, install VS 2019 (Community). When open the
solution file (project2.sln), be sure NOT to upgrade the Windows SDK Version nor the Platform Toolset:
The project should be buildable and runnable on a Windows system. Your tasks are:
- [2p] For the test stereo images (pictures/stereo1_left.png , stereo1_right.png), find 8 matching pairs of
2D points. List them as g_matching_left and g_matching_right. Note: x and y are in [-1,1] range. You
can define the matching manually or
[Bonus: +1~2p to mid-term] use off-the-shelf matching methods (such as OpenGL feature matching or
others). The bonus amount depends on how well you understood and explains your matching method. - [5p] Implement the normalized eight-point method in EpipolarGeometry() to calculate the fundamental
matrix (same as essential matrix). Remember to fill your result in g_epipolar_E To verify your result, the
eight “*multiply:” stdout should output values very close to zero (around e-6 ~ e-7). The rendering
should look like:
(Here the 8 matching are the 8 vertices of the “cube”. But your matching can be anything.) - [1p] Explain what line 382-389 do? What does the “multiply” result means? Why should all the multiply
values be (close to) zero? - [3p] Download the OpenCV sfm module source code at https://github.com/opencv/ope... Go
to \modules\sfm\src\libmv_light\libmv\multiview. Explain the following functions:
FundamentalFromEssential () in fundamental.cc [1p].
MotionFromEssential() in fundamental.cc [1p].
P_From_KRt () in projection.cc [1p].
Note: “HZ” means the textbook “Multiple View Geometry in Computer Vision” by Richard Hartley and
Andrew Zisserman and a pdf is provided for your reference.
WX：codehelp
android studio问题-E / Vision：加载可选模块com.google.android.gms.vision.ocr时出错
如何解决android studio问题-E / Vision：加载可选模块com.google.android.gms.vision.ocr时出错？
我在使用Google视觉OCR库的android studio中遇到问题。 这是错误：
W/DynamiteModule: Local module descriptor class for com.google.android.gms.vision.dynamite.ocr not found.
I/DynamiteModule: Considering local module com.google.android.gms.vision.dynamite.ocr:0 and remote module com.google.android.gms.vision.dynamite.ocr:0
W/DynamiteModule: Local module descriptor class for com.google.android.gms.vision.ocr not found.
I/DynamiteModule: Considering local module com.google.android.gms.vision.ocr:0 and remote module com.google.android.gms.vision.ocr:0
E/Vision: Error loading optional module com.google.android.gms.vision.ocr: com.google.android.gms.dynamite.DynamiteModule$LoadingException: No acceptable module found. Local version is 0 and remote version is 0.
你能帮我吗？
解决方法
暂无找到可以解决该程序问题的有效方法，小编努力寻找整理中！
如果你已经找到好的解决方法，欢迎将解决方案带上本链接一起发送给小编。
小编邮箱:dio#foxmail.com (将#修改为@）
awesome computer vision repo
https://blog.csdn.net/guoyunfei20/article/details/88530159
# AwesomeComputerVision
**Multi-Object-Tracking-Paper-List**
https://github.com/SpyderXu/multi-object-tracking-paper-list
**awesome-object-detection**
https://github.com/hoya012/deep_learning_object_detection
**awesome-image-classification**
https://github.com/weiaicunzai/awesome-image-classification
**Visual-Tracking-Paper-List**
https://github.com/foolwood/benchmark_results
**awesome-semantic-segmentation**
https://github.com/mrgloom/awesome-semantic-segmentation
**awesome-human-pose-estimation**
https://github.com/cbsudux/awesome-human-pose-estimation
**awesome-Face-Recognition**
————————————————
版权声明：本文为CSDN博主「guoyunfei20」的原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/guoyunfei20/article/details/88530159
Computer Vision
Homework 5
Computer Vision, Fall 2021
Due Date: 10AM November 15, 2021
Total Points: 18
This homework contains one written assignment, one programming walkthrough
and one programming challenge. All submissions are due at 10:00 a.m. on
November 15, 2021. Follow the submission instructions in the document titled
“Guidelines for Homework Assignments.” Note that there is no credit for late
submissions. So please start working on the homework early.
Written Assignments
Problem 1: A Lambertian surface is illuminated simultaneously by two distant point
sources with equal intensity in the direction s1 and s2. Show that for all normals on
the surface that are visible to both sources, illumination can be viewed as coming
from a single “effective” direction s3. How is s3 related to s1 and s2? Now, if the two
distant sources have unequal intensities I1 and I2, respectively, what is the direction
and intensity of the “effective” source? (4 Points)
Problem 2: The reflectance map can be parameterized in various ways. In class we
have concentrated on using the gradient (p, q) as a means of specifying surface
orientation. In some cases, the Gaussian sphere is more suitable for this purpose.
Each point on the Gaussian sphere corresponds to a particular direction, from the
center of the sphere to that point. The orientation of a surface patch can be specified
by giving the direction of its surface normal. Thus a given surface orientation can be
identified with a particular point on the Gaussian sphere. The reflectance map is
merely a means of associating brightness with orientation.
a. What are the contours of constant brightness on the Gaussian sphere in the
case of a Lambertian surface illuminated by a point source? Hint: See Figure
1a. (2 Points)
b. Show that there are at most two surface orientations that give rise to a given
pair of brightness values when the photometric stereo method is applied to a
Lambertian surface. Assume that two different light sources are used. Hint:
1
See Figure 1b. (2 Points)
Figure 1: The reflectance map can be plotted on the Gaussian sphere. (a) The contours of constant
brightness are particularly simple for a Lambertian surface illuminated by a point source. (b) This
makes it easy to prove that there are at most two solutions to the two-source photometric stereo
problem in this case.
Programming Assignments
This programming assignment has one challenge (with its own subset of milestones
or unit tests). Instructions and summary corresponding to these are given below.
runHw5.m will be your main interface for executing and testing your code.
Parameters for the different programs or unit tests can also be set in that file.
Before submission, make sure you can run all your programs with the command
runHw5(‘all’) with no errors.
MATLAB is optimized for operations involving matrices and vectors. Avoid
using loops (e.g., for, while) in MATLAB whenever possible—looping can result
in long running code. Instead, you should “vectorize [1]” loops to optimize your
code for performance. In many cases, vectorization also results in more
compact code (fewer lines to write!). If you are new to MATLAB, refer to these
articles [1] [2] on techniques to optimize MATLAB code.
2
Challenge 1: Your task is to develop a vision system that recovers the shape, surface
normal and reflectance of an object. For this purpose you will use photometric
stereo.
You will be given 5 images of an object taken using five different light sources. Your
task is to compute the surface normal and albedo for the object. For this purpose,
however, you will need to know the directions and intensities of the five light
sources. Thus, in the first part of the assignment, you will compute the light sources
directions and intensities from 5 images of a sphere and use this information in the
second part to recover the orientation and reflectance.
The 11 images, sphere0…sphere5, and vase1…vase5 are provided to you.
Before you begin, pay attention to the following assumptions you can make about
the capture settings:
● The surfaces of all objects (including the sphere) are Lambertian. This means
there are only diffuse peaks in the reflectance maps (no specular
components).
● For the images, assume orthographic projections.
● Image files with the same indices are taken using the same light source. For
example, sphere1 and vase1 are taken using light source number 1 only.
● The objects maintain the same position, orientation and scale through the
different images – the only difference is the light source. For example, the
sphere in sphere0…sphere5 has the same coordinates and the same radius.
● The light sources are not in singular configuration, i.e., the S-matrix that you
will compute should not be singular.
● You may NOT assume that the light sources are of equal intensities. This
means that you need to recover not only the directions of the light sources
but also their intensities.
● The background in the image is black (0 pixel value) in all images.
The task is divided into four parts, each corresponding to a program you need to
write and submit.
a. First you need to find the locations of the sphere and its radius. For this
purpose you will use the image sphere0, which is taken using many light
sources (so that the entire front hemisphere is visible).
3
Write a program findSphere that locates the sphere in an image and
computes its center and radius.
[center, radius] = findSphere(input_img)
Assuming an orthographic project, the sphere projects into a circle on the
image plane. Find the location of the circle by computing its centroid. Also
estimate the area of the circle and from this, compute the radius of the circle.
You may use the Image Processing Toolbox functions im2bw and
regionprops.
(1 points)
b. Now you need to compute the directions and intensities of the light sources.
For this purpose you should use the images sphere1…sphere5.
Derive a formula to compute the normal vector to the sphere’s surface at a
given point, knowing the point’s coordinates (in the image coordinate frame),
and the center and radius of the sphere’s projection onto the image plane
(again, assume an orthographic projection). This formula should give you the
resulting normal vector in a 3-D coordinate system, originating at the
sphere’s center, having its x-axis and y-axis parallel respectively to the x-axis
and the y-axis of the image, and z-axis chosen such as to form an orthonormal
right-hand coordinate system. Don’t forget to include your formula in your
README file.
Write a program computeLightDirections that uses this formula, along
with the parameters computed in (a), to find the normal to the brightest
surface spot on the sphere in each of the 5 images. Assume that this is the
direction of the corresponding light source (Why is it safe to assume this?
State this in the README).
Finally, for the intensity of the light source, use the magnitude (brightness) of
the brightness pixel found in the corresponding image. Scale the direction
vector so that its length equals this value.
light_dirs_5x3 = computeLightDirections(center, radius,
img_cell)
Center and radius are the resulted computed in (a). img_cell contains the
5 images of the sphere. The resulting light_dirs_5x3 is a 5x3 matrix. Row i
4
of light_dirs_5x3 contains the x-, y-, and z-components of the vector
computed for light source i. (2 points)
c. Write a program computeMask to compute a binary foreground mask for the
object. A pixel in the mask has a value 1 if it belongs to the object and 0 if it
belongs to the background. Distinguishing between the foreground and
background is simple: if a pixel is zero in all 5 images, then it is background.
Otherwise, it is foreground.
mask = computeMask(img_cell)
The img_cell contains the 5 images of an object and mask is the binary
image mask. (1 points)
d. Write a program computeNormals that, given 5 images of an object,
computes the normals and albedo to that object’s surface.
[normal, albedo_img] = computeNormals(light_dirs, img_cell,
mask)
You may want to use the formula given in the class lecture notes. Be careful
here! Make sure to take into account the different intensities of the light
source.
Photometric stereo requires the object to be lit by at least 3 light sources.
However, in our case, we have a total of 5 light sources. The lighting has been
arranged in such a way that all visible surface points on an object are lit by at
least 3 light sources. Therefore, while computing the surface normal at a
point, choose the 3 light sources for which the point appears brightest. Be
careful – choosing the wrong light sources will result in erroneous surface
normal. (You may also decide to choose more than 3 light sources to compute
the surface normal. This results in an over-determined linear system and can
provide robust estimates. However, such a computation is not mandatory.)
Do not compute the surface normal for the background. You can assume that
the surface normal in this region is looking toward the camera. Use the mask
generated in the previous program to identify whether a given pixel
corresponds to the object or the background.
Scale the albedo up or down to fit in the range 0…1 and show them in the
output image. Thus each pixel in the output image should be the pixel’s
albedo scaled by a constant factor. (6 points)
5
At this point you can use the outputs of your program to reconstruct the
surface of the object. demoSurfaceReconstruction and
reconstructSurf.m demonstrate how to use the Frankot-Chellappa
algorithm to compute the 3D shape from surface normals. You can use the
MATLAB function surf to visualize the reconstructed surface. Surface
reconstruction is provided as a demo--no submission is required
关于Azure Computer Vision 之 Smart Crop 智能裁剪图片和azure automation的问题我们已经讲解完毕，感谢您的阅读，如果还想了解更多关于3D Computer Vision、android studio问题-E / Vision：加载可选模块com.google.android.gms.vision.ocr时出错、awesome computer vision repo、Computer Vision等相关内容，可以在本站寻找。
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