Hello Mavii learners in this post, you will learn “how to create a fabric recognition system” using computer vision (OpenCV) and Histogram of Oriented Gradients (HOG) feature extraction method in python.
Complete tutorial video
Fabric patterns and design are the main elements in the textile fashion industry. The importance of fabric design is matters for consumers because they prefer their clothes are more catchy and attractive. In the computer vision (OpenCV), the feature extraction functionalities help to enhance the fabric quality and provide the aspired result in design excellence. Many industries are using a feature analysis and justification system for increasing their product quality and perfection on the production line. The responsibility to keep the design quality well is not entirely possible by human sight. For example, suppose the picture of flowers on the fabric is not well printed, which means product getting rejected.
Step 1: Take a one fabric material image as an original fabric and load in a system using the OpenCV image processing function.
Step 2: Capture the original fabric material surface design or pattern data from the histogram of oriented gradients (HOG) pattern recognition or analyzer function.
Step 3: When the hog pattern data is getting capture, then normalize capture value into a 1D dimensional array using Scikit-image exposure function for correlation histogram comparison.
Step 4: In this step, we have to repeat the same steps 1 to 3 for testing fabric material to analyse patterns.
Step 5: The last step, here we are examining the hog pattern data of both original and testing fabric material using the correlation function.
Case 1: if the result value is near to zero (0), which means fabric patterns are matching.
Case 2: if the result value is the higher then zero (0) like 0.5, 0.4, and 0.9, Etc. This means fabric patterns are not matching.
In today’s post, you learned how to create a program on product design analysis using computer vision (OpenCV) to use image processing methods, Histogram of oriented gradient (HOG) for pattern recognition, and Scipy correlation function in python. Preliminary design quality inspection focused on the appearance of the final product. The fabric design is essential for manufacturers so that it can successfully meet the production demands. It is challenging to find errors in design or pattern with hand-operated but, with computer vision (OpenCV), we can detect the blunder in fabric design, which indirectly benefits to measure the quality of the cloths and decrease refusal losses.
We deliver our most useful content for you. I hope you have enjoyed this blog post on product shape analysis. Here, I have tried to explain things to you in a way you’d be able to understand. If you have any suggestions or questions then please feel free to ask in the comment section below. That’s the end of our post. Happy commenting and follow us on other social media for more and the latest updates.
Click here to download the source code