#Digikam define pictures as a set how to#

Image sky is recognized as defocused blur because of its part sky and cloud. Image street is easy to be recognized as motion blurred because of it nature. However, there are still some hard situations. However, the recognition is not very sensitive to jugde them as rejected but only pending. It is also success to recognize motion blur by cheking on image like rock_2 or tree_2. For instance, the algoritm can recognize the sharp image like rock, or even difficult case like tree. These images are some different use-cases for blur detection.

I tested with test images in unit test of digikam. Then, I check if these lines are parallel by calculate the standard deviation of angle values and comparing it to the parameter max_stddev. The parameter min_nb_lines is used to determine if a part can be motion blur. As mentioned in the proposal, a small part with many parallel lines is a motion blurred part. Having lines of a part, it is easy to calculate the angle of them.This parameter helps to prevent too short line. HoughLinesP uses more criteria to determine a line than HoughLines includes: min_line_length. Besides, instead of using class Hough transform (cv::HoughLines() ), I use Probabilistic Hough Line Transform (cv::HoughLinesP). This transformation uses parameter theta_resolution and threshold_hough to find lines in an image. Then, I apply Hough Line Transform to determine every line in the part.Then, the image is divided into small parts with a pre-defined size (parameter: part_size_motion_blur).At first, I consider only the clear edges from the edges map, by using a threshold.At last, I determine if a specific pixel is a blur or sharp by using a threshold.Then, I smooth the image by the operator blur and medianblur of OpenCV to get a blurred map.To distinguish sharp pixels of edges and blurred pixels, I apply a log filter. This edges map is not enough to detect defocused blur.So, I can distinguish the sharp region that belonged to the object and the blurred region of the background. It detects not only the main edges but also the small edges inside the object. The advantage of Laplacian is its sensitivity. I used this map to detect defocused blur and motion blur of the image. Based on the algorithm of edges detection Laplacian of OpenCV, I extract an edges map of the image. While motion blur appears when the object or the camera moves when capturing.Īs an observation, I found that defocused blur is the disappearance of edges, while motion blur is the appearance of many parallel edges in a small part of the image. Defocused blur appears when the edges of the object are fuzzy. Hence, there should be detectors for each one due to the difference between their nature. There are 2 types of unit tests: unit test for each factor (for example detectionblur) and unit test by pre-defined use-case.Īlthough these tests can not be passed now, they will be useful in the future.īlur detection (first week : June 07 - June 14)Īs discussed in the proposal, there are 2 types of blur: motion blur and defocused blur.

#Digikam define pictures as a set how to#

This helps me have a tool to evaluate my future work without thinking further about how to test it. I found that it is the most important task in this period. This is the branch that will have my work for GSoC21.Īfter reading enough documentation and mathematics, I start preparing a base for testing each functionality of Image Quality Sorter (IQS). I have spent these 3 weeks reading the huge codebase of digiKam, preparing an environment for coding, and study in-depth the algorithm that I am about to implement.

Thanks to mentors Gilles Caulier, Maik Qualmann, and Thanh Trung Dinh for helping me prepare for the work this summer. I spend my first post describing this important period. Gsoc21-image-quality-sorter Project Report Community BondingĬommunity Bonding is the time for preparation. Mentors : Gilles Caulier, Maik Qualmann, Thanh Trung Dinhĭigikam Image Quality Sorter GitLab development branch My proposal provides my perspective to improve this functionality. However, the current implementation does not have a good performance. It helps users to label image by accepted or pending or rejected. Image Quality Sorter is a tool of digiKam.

3.8 Finish the project (Last week : August 09 - August 16)ĭigikam: Image Quality Sorter Improvement.

3.7 UI for adding customized focus point and showing focus point (Eighth and ninth week : July 25 - August 9).

3.6 Calculation of quality and optimization (Sixth and Seventh week : July 12 - July 25).

3.5 Exposure detection and Compressed detection (Fifth week : July 05 - July 11).

3.4 Noise detection (third and fourth week : June 22- July 04).

3.3 Focuspoint extraction and completion of blur detection (second week : June 14 - June 21).

3.2 Blur detection (first week : June 07 - June 14).

1 Digikam: Image Quality Sorter Improvement.