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| | | #include "network.h" |
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| | | #include "region_layer.h" |
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| | | #include "detection_layer.h" |
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| | | #include "cost_layer.h" |
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| | | #include "utils.h" |
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| | | |
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| | | char *voc_names[] = {"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"}; |
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| | | |
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| | | void draw_swag(image im, float *box, int side, int objectness, char *label, float thresh) |
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| | | void draw_swag(image im, float *predictions, int side, int num, char *label, float thresh) |
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| | | { |
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| | | int classes = 20; |
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| | | int elems = 4+classes+objectness; |
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| | | int j; |
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| | | int r, c; |
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| | | int i,n; |
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| | | |
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| | | for(r = 0; r < side; ++r){ |
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| | | for(c = 0; c < side; ++c){ |
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| | | j = (r*side + c) * elems; |
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| | | float scale = 1; |
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| | | if(objectness) scale = 1 - box[j++]; |
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| | | int class = max_index(box+j, classes); |
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| | | if(scale * box[j+class] > thresh){ |
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| | | int width = sqrt(scale*box[j+class])*5 + 1; |
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| | | printf("%f %s\n", scale * box[j+class], voc_names[class]); |
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| | | for(i = 0; i < side*side; ++i){ |
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| | | int row = i / side; |
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| | | int col = i % side; |
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| | | for(n = 0; n < num; ++n){ |
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| | | int p_index = side*side*classes + i*num + n; |
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| | | int box_index = side*side*(classes + num) + (i*num + n)*4; |
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| | | int class_index = i*classes; |
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| | | float scale = predictions[p_index]; |
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| | | int class = max_index(predictions+class_index, classes); |
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| | | float prob = scale * predictions[class_index + class]; |
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| | | if(prob > thresh){ |
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| | | int width = sqrt(prob)*5 + 1; |
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| | | printf("%f %s\n", prob, voc_names[class]); |
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| | | float red = get_color(0,class,classes); |
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| | | float green = get_color(1,class,classes); |
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| | | float blue = get_color(2,class,classes); |
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| | | box b = float_to_box(predictions+box_index); |
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| | | b.x = (b.x + col)/side; |
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| | | b.y = (b.y + row)/side; |
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| | | b.w = b.w*b.w; |
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| | | b.h = b.h*b.h; |
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| | | |
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| | | j += classes; |
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| | | float x = box[j+0]; |
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| | | float y = box[j+1]; |
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| | | x = (x+c)/side; |
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| | | y = (y+r)/side; |
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| | | float w = box[j+2]; //*maxwidth; |
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| | | float h = box[j+3]; //*maxheight; |
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| | | h = h*h; |
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| | | w = w*w; |
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| | | |
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| | | int left = (x-w/2)*im.w; |
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| | | int right = (x+w/2)*im.w; |
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| | | int top = (y-h/2)*im.h; |
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| | | int bot = (y+h/2)*im.h; |
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| | | int left = (b.x-b.w/2)*im.w; |
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| | | int right = (b.x+b.w/2)*im.w; |
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| | | int top = (b.y-b.h/2)*im.h; |
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| | | int bot = (b.y+b.h/2)*im.h; |
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| | | draw_box_width(im, left, top, right, bot, width, red, green, blue); |
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| | | } |
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| | | } |
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| | | if(weightfile){ |
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| | | load_weights(&net, weightfile); |
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| | | } |
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| | | detection_layer layer = get_network_detection_layer(net); |
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| | | region_layer layer = net.layers[net.n-1]; |
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| | | set_batch_network(&net, 1); |
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| | | srand(2222222); |
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| | | clock_t time; |
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| | | time=clock(); |
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| | | float *predictions = network_predict(net, X); |
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| | | printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); |
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| | | draw_swag(im, predictions, 7, layer.objectness, "predictions", thresh); |
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| | | draw_swag(im, predictions, layer.side, layer.n, "predictions", thresh); |
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| | | show_image(sized, "resized"); |
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| | | free_image(im); |
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| | | free_image(sized); |
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| | | #ifdef OPENCV |
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