imagex.go

package main

import (
	"github.com/nfnt/resize"
	"image"
	"io"
	"math"
	"sort"
)

// Function used to convert RAW output from YOLOv8 to an array
// of detected objects. Each object contain the bounding box of
// this object, the type of object and the probability
// Returns array of detected objects in a format [[x1,y1,x2,y2,object_type,probability],..]
func process_output(output []float32, img_width, img_height int64) [][]interface{} {
	boxes := [][]interface{}{}
	for index := 0; index < 8400; index++ {
		class_id, prob := 0, float32(0.0)
		for col := 0; col < 80; col++ {
			if output[8400*(col+4)+index] > prob {
				prob = output[8400*(col+4)+index]
				class_id = col
			}
		}
		if prob < 0.5 {
			continue
		}
		label := yolo_classes[class_id]
		xc := output[index]
		yc := output[8400+index]
		w := output[2*8400+index]
		h := output[3*8400+index]
		x1 := (xc - w/2) / 640 * float32(img_width)
		y1 := (yc - h/2) / 640 * float32(img_height)
		x2 := (xc + w/2) / 640 * float32(img_width)
		y2 := (yc + h/2) / 640 * float32(img_height)
		boxes = append(boxes, []interface{}{float64(x1), float64(y1), float64(x2), float64(y2), label, prob})
	}

	sort.Slice(boxes, func(i, j int) bool {
		return boxes[i][5].(float32) < boxes[j][5].(float32)
	})
	result := [][]interface{}{}
	for len(boxes) > 0 {
		result = append(result, boxes[0])
		tmp := [][]interface{}{}
		for _, box := range boxes {
			if iou(boxes[0], box) < 0.7 {
				tmp = append(tmp, box)
			}
		}
		boxes = tmp
	}
	return result
}

// Function calculates "Intersection-over-union" coefficient for specified two boxes
// https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/.
// Returns Intersection over union ratio as a float number
func iou(box1, box2 []interface{}) float64 {
	return intersection(box1, box2) / union(box1, box2)
}

// Function calculates union area of two boxes
// Returns Area of the boxes union as a float number
func union(box1, box2 []interface{}) float64 {
	box1_x1, box1_y1, box1_x2, box1_y2 := box1[0].(float64), box1[1].(float64), box1[2].(float64), box1[3].(float64)
	box2_x1, box2_y1, box2_x2, box2_y2 := box2[0].(float64), box2[1].(float64), box2[2].(float64), box2[3].(float64)
	box1_area := (box1_x2 - box1_x1) * (box1_y2 - box1_y1)
	box2_area := (box2_x2 - box2_x1) * (box2_y2 - box2_y1)
	return box1_area + box2_area - intersection(box1, box2)
}

// Function calculates intersection area of two boxes
// Returns Area of intersection of the boxes as a float number
func intersection(box1, box2 []interface{}) float64 {
	box1_x1, box1_y1, box1_x2, box1_y2 := box1[0].(float64), box1[1].(float64), box1[2].(float64), box1[3].(float64)
	box2_x1, box2_y1, box2_x2, box2_y2 := box2[0].(float64), box2[1].(float64), box2[2].(float64), box2[3].(float64)
	x1 := math.Max(box1_x1, box2_x1)
	y1 := math.Max(box1_y1, box2_y1)
	x2 := math.Min(box1_x2, box2_x2)
	y2 := math.Min(box1_y2, box2_y2)
	return (x2 - x1) * (y2 - y1)
}

// Function used to convert input image to tensor,
// required as an input to YOLOv8 object detection
// network.
// Returns the input tensor, original image width and height
func prepare_input(buf io.Reader) ([]float32, int64, int64) {
	img, _, _ := image.Decode(buf)
	size := img.Bounds().Size()
	img_width, img_height := int64(size.X), int64(size.Y)
	img = resize.Resize(640, 640, img, resize.Lanczos3)
	red := []float32{}
	green := []float32{}
	blue := []float32{}
	for y := 0; y < 640; y++ {
		for x := 0; x < 640; x++ {
			r, g, b, _ := img.At(x, y).RGBA()
			red = append(red, float32(r/257)/255.0)
			green = append(green, float32(g/257)/255.0)
			blue = append(blue, float32(b/257)/255.0)
		}
	}
	input := append(red, green...)
	input = append(input, blue...)
	return input, img_width, img_height
}