JohnSnowLabs / spark-nlp / 13883000244

package com.johnsnowlabs.nlp.annotators.cv.util.transform

import java.awt.image.BufferedImage

import java.awt.{Color, Graphics2D}

import scala.collection.mutable.ListBuffer

import scala.collection.mutable.ArrayBuffer

import ImageResizeUtils.resizeBufferedImage

private[johnsnowlabs] object Phi3vUtils {

  // padding image

  def padding_336(image: BufferedImage): BufferedImage = {

    // Get the current width and height of the image

    // Calculate the target height (multiple of 336)

    // Calculate the padding for top and bottom

    // No padding for left and right

    // Create a new BufferedImage with the padded dimensions

    // Create Graphics2D object to draw the padded image

    // Set white background for the padding (fill)

    // Draw the original image onto the center of the new padded image

    // Dispose of the Graphics2D context

    // Return the new padded image

    paddedImage

  }

  def transposeImage(img: BufferedImage): BufferedImage = {

    transposedImage

  }

  def calc_padded_size(width: Int, height: Int, padding_unit: Int = 336): (Int, Int) = {

  }

  def HDTransform(img: BufferedImage, hdNum: Int = 16): BufferedImage = {

    // Transpose the image if width is smaller than height

    var transformedImg = img

    }

    // Calculate the scaling factor

    }

    // New dimensions

    // Resize the image

    // Apply padding to make the image 336x336

    // Transpose back if needed

    }

    transformedImg

  }

  // Function to extract a subimage and reset position information

  def getNewSubimage(

      image: BufferedImage,

      x: Int,

      y: Int,

      width: Int,

      height: Int): BufferedImage = {

    // Create a new BufferedImage to store the subimage

    // Create a Graphics2D object to draw the subimage

    // Draw the original image's subimage into the new BufferedImage

    // Dispose the graphics context to free up resources

    // Return the new subimage with reset position information

    subImage

  }

  // Function to calculate the shapes (height and width of the image)

  def calculateShapes(images: List[BufferedImage]): Array[Array[Int]] = {

  }

  // Function to calculate the number of image tokens

//  def calculateImageTokens(shapes: List[(Int, Int)]): List[Int] = {

//    shapes.map { case (h, w) =>

//      ((h / 336) * (w / 336) + 1) * 144 + 1 + ((h / 336 + 1) * 12)

//    }

//  }

  def calculateImageTokens(shapes: Array[Array[Int]]): List[Int] = {

  }

  // Function to reshape the images (assuming each image is already HD transformed)

//  def reshapeImages(

//      images: List[BufferedImage],

//      shapes: List[(Int, Int)]): List[List[BufferedImage]] = {

//    images.zip(shapes).map { case (img, (h, w)) =>

//      val numH = h / 336

//      val numW = w / 336

//      val reshapedImages = new ListBuffer[BufferedImage]

//

//      // Splitting the image into 336x336 crops

//      for (i <- 0 until numH; j <- 0 until numW) {

//        val crop = getNewSubimage(img, j * 336, i * 336, 336, 336)

//        reshapedImages += crop

//      }

//      reshapedImages.toList

//    }

//  }

  def reshapeImages(

      images: List[BufferedImage],

      shapes: Array[Array[Int]]): List[List[BufferedImage]] = {

      // Splitting the image into 336x336 crops

      }

    }

  }

  // Function to concatenate global and local images (manually)

  def concatenateImages(

      globalImage: BufferedImage,

      localImages: List[BufferedImage]): BufferedImage = {

    // Draw global image first

    // Draw each local image next to the global image

    }

    concatenatedImage

  }

  // Function to pad the images to a specified number of crops (maxNumCrops)

  def padToMaxNumCrops(image: BufferedImage, maxNumCrops: Int): BufferedImage = {

    // If the number of crops is less than maxNumCrops, pad with white

    // Fill with white background

    // Draw the original image onto the white background

    paddedImage

  }

  // Main function that processes the HD transformed images

  def processHdImages(

      hdImages: List[BufferedImage],

      numCrops: Int): (List[BufferedImage], Array[Array[Int]], List[Int]) = {

    // Step 1: Create global images (resize to 336x336)

    // val resizeGlobal =

    // Step 2: Calculate shapes [(h, w)] where h, w are multiples of 336

    // Step 3: Calculate number of image tokens

    // Step 4: Reshape the HD images into 336x336 crops

    // Step 5: Concatenate global and local images

    val concatenatedImages =

      }

    // Step 6: Pad to max_num_crops if necessary

  }

  // Function to normalize pixel values of an image crop

  def normalizeImageCrop(

      imgCrop: Array[Array[Array[Int]]],

      mean: Array[Double],

      std: Array[Double]): Array[Array[Array[Float]]] = {

    // Create a 3D array for normalized values

          // Normalize the pixel value: (value - mean) / std

        }

      }

    }

    normalizedCrop

  }

  // Helper function to convert a BufferedImage crop to a 3D array (3, 336, 336) for RGB channels

  def imageCropToArray(imgCrop: BufferedImage): Array[Array[Array[Int]]] = {

    // Create a 3D array for RGB channels

    }

    cropArray

  }

  // Function to split an image into 336x336 crops, convert to a 3D array, and normalize if required

  def splitImageToCrops(

      image: BufferedImage,

      cropSize: Int = 336,

      normalize: Boolean = false,

      mean: Array[Double] = Array(0.48145466, 0.4578275, 0.40821073),

      std: Array[Double] = Array(0.26862954, 0.26130258, 0.27577711))

      : (Array[Array[Array[Array[Float]]]], Int) = {

    // Number of crops along height and width

    // Store the crops in a 4D array (numCrops, 3, 336, 336)

        // Extract a crop of 336x336

        // Convert the crop to a 3D array (3, 336, 336)

        // Normalize the crop if the option is enabled

        val normalizedCrop = if (normalize) {

        } else {

          // Convert Int array to Double array if normalization is off

        }

      }

    }

    // Convert ArrayBuffer to an array

  }

  // Function to convert processedImages (BufferedImages) into a 5D array (b, h//336 * w//336, 3, 336, 336)

  def processedImagesTo5DArray(

      processedImages: List[BufferedImage],

      normalize: Boolean = false,

      mean: Array[Double] = Array(0.48145466, 0.4578275, 0.40821073),

      std: Array[Double] = Array(0.26862954, 0.26130258, 0.27577711))

      : (Array[Array[Array[Array[Array[Float]]]]]) = {

    // Store the 5D array (b, h//336 * w//336, 3, 336, 336)

    // Process each image in the batch

      // Split the image into crops, convert each crop into a 3D array, and normalize if required

        splitImageToCrops(img, normalize = normalize, mean = mean, std = std)

    }

    // Convert ArrayBuffer to array (b, numCrops, 3, 336, 336)

  }

}