4-yolov3.py

#%% Define function to extract object coordinates if successful in detection
def where_is_it(frame, outputs):
    frame_h = frame.shape[0]
    frame_w = frame.shape[1]
    bboxes, probs, class_ids = [], [], []
    for preds in outputs: # different detection scales
        hits = np.any(preds[:, 5:] > P_THRESH, axis=1) & (preds[:, 4] > OBJ_THRESH)
        # Save prob and bbox coordinates if both objectness and probability pass respective thresholds 
        for i in np.where(hits)[0]:
            pred = preds[i, :]
            center_x = int(pred[0] * frame_w)
            center_y = int(pred[1] * frame_h)
            width = int(pred[2] * frame_w)
            height = int(pred[3] * frame_h)
            left = int(center_x - width / 2)
            top = int(center_y - height / 2)
            # Append all info
            bboxes.append([left, top, width, height])
            probs.append(float(np.max(pred[5:])))
            class_ids.append(np.argmax(pred[5:]))
    return bboxes, probs, class_ids