need to check what happens to im.data

.gitignore

@@ -10,3 +10,6 @@ example/yolov3.cfg
 example/yolov3.weights
 darknet.h
 *.so
+predictions.png
+predictions.jpg
+main

detection.c

@@ -3,20 +3,16 @@
 #include "detection.h"
 
 
-detection * get_detection(detection *dets, int index, int dets_len)
+detection *get_detection(detection *dets, int index, int dets_len) {
-{
     if (index >= dets_len) {
         return NULL;
     }
-
     return dets + index;
 }
 
-float get_detection_probability(detection *det, int index, int prob_len)
+float get_detection_probability(detection *det, int index, int prob_len) {
-{
     if (index >= prob_len) {
         return .0;
     }
-
     return det->prob[index];
 }

detection.go

@@ -26,15 +26,12 @@ type DetectionResult struct {
 }
 
 func makeDetection(img *DarknetImage, det *C.detection, threshold float32, classes int, classNames []string) *Detection {
-
 	if det == nil {
 		return &Detection{}
 	}
-
 	dClassIDs := make([]int, 0)
 	dClassNames := make([]string, 0)
 	dProbs := make([]float32, 0)
-
 	for i := 0; i < int(classes); i++ {
 		dProb := float32(C.get_detection_probability(det, C.int(i), C.int(classes)))
 		if dProb > threshold {
@@ -44,7 +41,6 @@ func makeDetection(img *DarknetImage, det *C.detection, threshold float32, class
 			dProbs = append(dProbs, dProb*100)
 		}
 	}
-
 	fImgW := C.float(img.Width)
 	fImgH := C.float(img.Height)
 	halfRatioW := det.bbox.w / 2.0
@@ -65,7 +61,6 @@ func makeDetection(img *DarknetImage, det *C.detection, threshold float32, class
 		ClassNames:    dClassNames,
 		Probabilities: dProbs,
 	}
-
 	return &out
 }
 

example/main.go

@@ -55,7 +55,6 @@ func main() {
 	if err != nil {
 		panic(err.Error())
 	}
-	_ = src
 
 	imgDarknet, err := darknet.Image2Float32(src)
 	if err != nil {
@@ -63,12 +62,11 @@ func main() {
 	}
 	defer imgDarknet.Close()
 
-	dr, err := n.Detect(&imgDarknet)
+	dr, err := n.Detect(imgDarknet)
 	if err != nil {
 		printError(err)
 		return
 	}
-	_ = dr
 
 	log.Println("Network-only time taken:", dr.NetworkOnlyTimeTaken)
 	log.Println("Overall time taken:", dr.OverallTimeTaken, len(dr.Detections))

image.c

@@ -1,22 +1,12 @@
 #include <darknet.h>
 
-image new_darknet_image() {
+void fill_image_f32(image* im, int w, int h, int c, float* data) {
-    image img;
+    int i;
-    img.w = 0;
+    for (i = 0; i < w*h*c; i++) {
-    img.h = 0;
+        im->data[i] = data[i];
-    img.c = 0;
+    }
-    img.data = NULL;
-    return img;
 }
 
-image prepare_image(image img, int w, int h, int c){
+void set_data_f32_val(float* data, int index, float value) {
-    img.w=w;
+    data[index] = value;
-    img.h=h;
-    img.c=c;
-    return img;
-}
-
-image new_image(int w, int h, int c){
-    image out = make_image(w, h, c);
-    return out;
 }

image.go

@@ -26,34 +26,36 @@ func float_p(arr []float32) *C.float {
 }
 
 // Image2Float32 Returns []float32 representation of image.Image
-func Image2Float32(img image.Image) (DarknetImage, error) {
+func Image2Float32(img image.Image) (*DarknetImage, error) {
 	width := img.Bounds().Dx()
 	height := img.Bounds().Dy()
-	imgwh := width * height
+	ans := []float32{}
-	imgSize := imgwh * 3
+	red := []float32{}
-
+	green := []float32{}
-	ans := make([]float32, imgSize)
+	blue := []float32{}
 	for x := 0; x < width; x++ {
 		for y := 0; y < height; y++ {
 			r, g, b, _ := img.At(y, x).RGBA()
 			rpix, gpix, bpix := float32(r>>8)/float32(255.0), float32(g>>8)/float32(255.0), float32(b>>8)/float32(255.0)
-			ans[y+x*height] = rpix
+			red = append(red, rpix)
-			ans[y+x*height+imgwh] = gpix
+			green = append(green, gpix)
-			ans[y+x*height+imgwh+imgwh] = bpix
+			blue = append(blue, bpix)
 		}
 	}
+	ans = append(ans, red...)
+	ans = append(ans, green...)
+	ans = append(ans, blue...)
 
-	imgDarknet := DarknetImage{
+	imgDarknet := &DarknetImage{
 		Width:  width,
 		Height: height,
-		// image:  C.new_darknet_image(),
+		image:  C.make_image(C.int(width), C.int(height), 3),
 	}
+	// for i := range ans {
+	// 	C.set_data_f32_val(imgDarknet.image.data, C.int(i), C.float(ans[i]))
+	// }
+	C.fill_image_f32(&imgDarknet.image, C.int(width), C.int(height), 3, float_p(ans))
 
-	// imgDarknet.image = C.prepare_image(imgDarknet.image, C.int(width), C.int(height), 3)
+	// imgDarknet.image = C.load_image_color(C.CString("~/Downloads/mega.jpg"), 416, 416)
-	// imgDarknet.image.data = float_p(ans)
-	// imgDarknet.image = C.resize_image(imgDarknet.image, 416, 416) // Do we need resize? (detection function actually does it)
-
-	imgDarknet.image = C.load_image_color(C.CString("/home/dimitrii/Downloads/mega.jpg"), 416, 416)
-
 	return imgDarknet, nil
 }

image.h

@@ -2,6 +2,5 @@
 
 #include <darknet.h>
 
-extern image new_darknet_image();
+extern void fill_image_f32(image *im, int w, int h, int c, float* data);
-extern image prepare_image(image img, int w, int h, int c);
+extern void set_data_f32_val(float* data, int index, float value);
-extern image new_image(int w, int h, int c);

network.c

@@ -6,13 +6,23 @@
 
 #include "detection.h"
 
-struct network_box_result perform_network_detect(network *n, image *img, int classes, float thresh, float hier_thresh, float nms) {
+struct network_box_result perform_network_detect(network *n, image *img, int classes, float thresh, float hier_thresh, float nms, int letter_box) {
-    image sized = letterbox_image(*img, n->w, n->h);
+    image sized;
+    int ww = img->w;
+    int hh =  img->h;
+    int cc =  img->c;
+    if (letter_box) {
+        // printf("using letter %d %d %d %d %d %d\n", letter_box, n->w, n->h, img->w, img->h, img->c);
+        sized = letterbox_image(*img, n->w, n->h);
+    } else {
+        // printf("not using letter: %d %d %d %d %d %d\n", letter_box, n->w, n->h, img->w, img->h, img->c);
+        sized = resize_image(*img, n->w, n->h);
+    }
     struct network_box_result result = { NULL };
     float *X = sized.data;
-    int letter_box = 0;
     network_predict_ptr(n, X);
     result.detections = get_network_boxes(n, img->w, img->h, thresh, hier_thresh, 0, 1, &result.detections_len, letter_box);
+    // printf("Clang number of detections: %d\n", result.detections_len);
     if (nms) {
         do_nms_sort(result.detections, result.detections_len, classes, nms);
     }

network.go

@@ -63,11 +63,8 @@ func (n *YOLONetwork) Init() error {
 	// C.set_batch_network(n.cNet, 1)
 	C.srand(2222222)
 
-	// Currently, hierarchal threshold is always 0.5.
+	n.hierarchalThreshold = 0.5
-	n.hierarchalThreshold = .5
+	n.nms = 0.45
-
-	// Currently NMS is always 0.4.
-	n.nms = .45
 
 	metadata := C.get_metadata(nCfg)
 	n.Classes = int(metadata.classes)
@@ -93,7 +90,7 @@ func (n *YOLONetwork) Detect(img *DarknetImage) (*DetectionResult, error) {
 		return nil, errNetworkNotInit
 	}
 	startTime := time.Now()
-	result := C.perform_network_detect(n.cNet, &img.image, C.int(n.Classes), C.float(n.Threshold), C.float(n.hierarchalThreshold), C.float(n.nms))
+	result := C.perform_network_detect(n.cNet, &img.image, C.int(n.Classes), C.float(n.Threshold), C.float(n.hierarchalThreshold), C.float(n.nms), C.int(0))
 	endTime := time.Now()
 	defer C.free_detections(result.detections, result.detections_len)
 	ds := makeDetections(img, result.detections, int(result.detections_len), n.Threshold, n.Classes, n.ClassNames)

network.h

@@ -8,4 +8,4 @@ struct network_box_result {
 };
 
 extern int get_network_layer_classes(network *n, int index);
-extern struct network_box_result perform_network_detect(network *n, image *img, int classes, float thresh, float hier_thresh, float nms);
+extern struct network_box_result perform_network_detect(network *n, image *img, int classes, float thresh, float hier_thresh, float nms, int letter_box);