YOLOv2 Prediction

yolo_prediction.py

# loop 845 bounding boxes
for i in range(Net_YOLO_pred.shape[0]):
    # get the confidence on the object
    confidence_on_box = Net_YOLO_pred[i][4]
    # find the class index with the highest probability
    probability_list = Net_YOLO_pred[i][5:]
    class_index = probability_list.argmax(axis=0)
    probability_on_class = probability_list[class_index]
    # get the score 
    score = confidence_on_box * probability_on_class
    # draw the bounding box
    if (score > threshold):
        x_center   = Net_YOLO_pred[i][0] * cols
        y_center   = Net_YOLO_pred[i][1] * rows
        width      = Net_YOLO_pred[i][2] * cols
        height     = Net_YOLO_pred[i][3] * rows
        
        left       = int(x_center - width * 0.5)
        top        = int(y_center - height * 0.5)
        right      = int(x_center + width * 0.5)
        bottom     = int(y_center + height * 0.5)
        
        box = patch.Rectangle((int(left), int(top)),
                              width,
                              height,
                              linewidth=2, edgecolor='r', facecolor='none')
                            
        ax2.add_patch(box)