Rice Grain Identifier (RGI)

Introduction:

Python program that does following:

• Identifies different type of rice-grains from a given image.
• Extract their dimensions.
• Save their dimensions in an excel file.

How to run this project:

• Download & subsequently open this repo in VS Code.
• Make sure the sample images are placed in the same directory as the source.py file.
• Open terminal and run following commands in order:
  • pip install -r requirements.txt
  • py source.py

Data Set Acquisition:

The data gathering process for this project included taking pictures of rice grains of several rice types in specific conditions, i.e. 8 to 10 rice grains of the same rice type were placed in a cardboard box along with a reference object (5 rupee coin) & a universal object (in this case, a coke bottle cap) for the purpose of streamlining the identification & categorizing process.

Process:

Step 1 - Read RGB image as greyscale using skimage.io library.

Step 2 - Convert greyscale image to a binary image by taking threshold of the image.

Step 3 - Perform binary closing function (dilation then erosion) with an enlarging structuring element on the resultant image to remove the noise & smooth the image.

Step 4 - After the binary image is smoothed perfectly, label the image & generate bounding box around the reference object & the rice grains using skimage library's measure label & regionprops functions. Traverse the complete labeled image in a loop in order to generate bounding boxes.

Step 5 - Bounding box will provide orientation, dimensions, & diameter of rice grains as well as the reference object, from which the diameter of the coin will be used to compute a pixel-per-cm value, which will be further used to determine height & width of each grain inside the morphed image. The diameter of the coin will be gathered via a loop with eccentricity value less than 0.2, as it will identify a round object inside the image.

Step 6 - Pixel value formula is:

Pixel Value = Coin-Diameter/Total-Pixels

Step 7 - Extracts the measurements (height, width, intensity level, & average color) of rice grains & compute their height & width values using the above-computed pixel value.

Step 8 - Find orientation (angle) of each rice grain, convert it from radians to degree & crop each of the selected rice grain as a separate image.

Step 9 - Use following formula to rotate the cropped image & to make a 90-degree angle of the rice grain:

Rotate image to 90 = 90-degree - (Image Angle - 1)

Step 10 - Pad the cropped image with 0s, to prepare it for cropping without affecting the corners of the rice grains.

Step 11 - Apply scipy’s rotate & interpolation functions on the cropped image.

Step 12 - Repeat step-4 & crop the image according to its bounding box.

Step 13 - Convert the resultant image into a grayscale as save it as a .tiff file with a unique name.

Step 14 - Finally, save the gathered dimensions, new image name, its type, its average color ratio, & its intensity level into an excel file, for each rice grain in an image.

Results:

1. Original Image:

2. After Gray Scaling:

3. After Thresholding:

4. After Morphology Operations:

5. Bouding Box Around Resultant Objects:

6. Cropped Rice Grain:

Important:

• Repo contains following items:
  • Source.py - main program file.
  • Data set.
  • Step-by-step images of the complete process.
  • Sample results excel file.

Note:

Orginally, the program is configured to work with 10 types of rice grains with 5 sample each. However, it can be changed accordingly, as the source file now contains 2 main methods:
1. run_RGI() - The original method, which works with 10 types of rice with 5 images each.
2. run_RGI_with_small_dataset() - An alternate method for this repo, which works with 3 rice types with 1 image each to demonstrate the RGI working.