utils_cv/tracking/references/fairmot/tracking_utils/utils.py [112:222]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - def ap_per_class(tp, conf, pred_cls, target_cls): """ Compute the average precision, given the recall and precision curves. Method originally from https://github.com/rafaelpadilla/Object-Detection-Metrics. # Arguments tp: True positives (list). conf: Objectness value from 0-1 (list). pred_cls: Predicted object classes (list). target_cls: True object classes (list). # Returns The average precision as computed in py-faster-rcnn. """ # lists/pytorch to numpy tp, conf, pred_cls, target_cls = np.array(tp), np.array(conf), np.array(pred_cls), np.array(target_cls) # Sort by objectness i = np.argsort(-conf) tp, conf, pred_cls = tp[i], conf[i], pred_cls[i] # Find unique classes unique_classes = np.unique(np.concatenate((pred_cls, target_cls), 0)) # Create Precision-Recall curve and compute AP for each class ap, p, r = [], [], [] for c in unique_classes: i = pred_cls == c n_gt = sum(target_cls == c) # Number of ground truth objects n_p = sum(i) # Number of predicted objects if (n_p == 0) and (n_gt == 0): continue elif (n_p == 0) or (n_gt == 0): ap.append(0) r.append(0) p.append(0) else: # Accumulate FPs and TPs fpc = np.cumsum(1 - tp[i]) tpc = np.cumsum(tp[i]) # Recall recall_curve = tpc / (n_gt + 1e-16) r.append(tpc[-1] / (n_gt + 1e-16)) # Precision precision_curve = tpc / (tpc + fpc) p.append(tpc[-1] / (tpc[-1] + fpc[-1])) # AP from recall-precision curve ap.append(compute_ap(recall_curve, precision_curve)) return np.array(ap), unique_classes.astype('int32'), np.array(r), np.array(p) def compute_ap(recall, precision): """ Compute the average precision, given the recall and precision curves. Code originally from https://github.com/rbgirshick/py-faster-rcnn. # Arguments recall: The recall curve (list). precision: The precision curve (list). # Returns The average precision as computed in py-faster-rcnn. """ # correct AP calculation # first append sentinel values at the end mrec = np.concatenate(([0.], recall, [1.])) mpre = np.concatenate(([0.], precision, [0.])) # compute the precision envelope for i in range(mpre.size - 1, 0, -1): mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) # to calculate area under PR curve, look for points # where X axis (recall) changes value i = np.where(mrec[1:] != mrec[:-1])[0] # and sum (\Delta recall) * prec ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) return ap def bbox_iou(box1, box2, x1y1x2y2=False): """ Returns the IoU of two bounding boxes """ N, M = len(box1), len(box2) if x1y1x2y2: # Get the coordinates of bounding boxes b1_x1, b1_y1, b1_x2, b1_y2 = box1[:, 0], box1[:, 1], box1[:, 2], box1[:, 3] b2_x1, b2_y1, b2_x2, b2_y2 = box2[:, 0], box2[:, 1], box2[:, 2], box2[:, 3] else: # Transform from center and width to exact coordinates b1_x1, b1_x2 = box1[:, 0] - box1[:, 2] / 2, box1[:, 0] + box1[:, 2] / 2 b1_y1, b1_y2 = box1[:, 1] - box1[:, 3] / 2, box1[:, 1] + box1[:, 3] / 2 b2_x1, b2_x2 = box2[:, 0] - box2[:, 2] / 2, box2[:, 0] + box2[:, 2] / 2 b2_y1, b2_y2 = box2[:, 1] - box2[:, 3] / 2, box2[:, 1] + box2[:, 3] / 2 # get the coordinates of the intersection rectangle inter_rect_x1 = torch.max(b1_x1.unsqueeze(1), b2_x1) inter_rect_y1 = torch.max(b1_y1.unsqueeze(1), b2_y1) inter_rect_x2 = torch.min(b1_x2.unsqueeze(1), b2_x2) inter_rect_y2 = torch.min(b1_y2.unsqueeze(1), b2_y2) # Intersection area inter_area = torch.clamp(inter_rect_x2 - inter_rect_x1, 0) * torch.clamp(inter_rect_y2 - inter_rect_y1, 0) # Union Area b1_area = ((b1_x2 - b1_x1) * (b1_y2 - b1_y1)) b1_area = ((b1_x2 - b1_x1) * (b1_y2 - b1_y1)).view(-1,1).expand(N,M) b2_area = ((b2_x2 - b2_x1) * (b2_y2 - b2_y1)).view(1,-1).expand(N,M) return inter_area / (b1_area + b2_area - inter_area + 1e-16) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - utils_cv/tracking/references/fairmot/utils/utils.py [46:156]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - def ap_per_class(tp, conf, pred_cls, target_cls): """ Compute the average precision, given the recall and precision curves. Method originally from https://github.com/rafaelpadilla/Object-Detection-Metrics. # Arguments tp: True positives (list). conf: Objectness value from 0-1 (list). pred_cls: Predicted object classes (list). target_cls: True object classes (list). # Returns The average precision as computed in py-faster-rcnn. """ # lists/pytorch to numpy tp, conf, pred_cls, target_cls = np.array(tp), np.array(conf), np.array(pred_cls), np.array(target_cls) # Sort by objectness i = np.argsort(-conf) tp, conf, pred_cls = tp[i], conf[i], pred_cls[i] # Find unique classes unique_classes = np.unique(np.concatenate((pred_cls, target_cls), 0)) # Create Precision-Recall curve and compute AP for each class ap, p, r = [], [], [] for c in unique_classes: i = pred_cls == c n_gt = sum(target_cls == c) # Number of ground truth objects n_p = sum(i) # Number of predicted objects if (n_p == 0) and (n_gt == 0): continue elif (n_p == 0) or (n_gt == 0): ap.append(0) r.append(0) p.append(0) else: # Accumulate FPs and TPs fpc = np.cumsum(1 - tp[i]) tpc = np.cumsum(tp[i]) # Recall recall_curve = tpc / (n_gt + 1e-16) r.append(tpc[-1] / (n_gt + 1e-16)) # Precision precision_curve = tpc / (tpc + fpc) p.append(tpc[-1] / (tpc[-1] + fpc[-1])) # AP from recall-precision curve ap.append(compute_ap(recall_curve, precision_curve)) return np.array(ap), unique_classes.astype('int32'), np.array(r), np.array(p) def compute_ap(recall, precision): """ Compute the average precision, given the recall and precision curves. Code originally from https://github.com/rbgirshick/py-faster-rcnn. # Arguments recall: The recall curve (list). precision: The precision curve (list). # Returns The average precision as computed in py-faster-rcnn. """ # correct AP calculation # first append sentinel values at the end mrec = np.concatenate(([0.], recall, [1.])) mpre = np.concatenate(([0.], precision, [0.])) # compute the precision envelope for i in range(mpre.size - 1, 0, -1): mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) # to calculate area under PR curve, look for points # where X axis (recall) changes value i = np.where(mrec[1:] != mrec[:-1])[0] # and sum (\Delta recall) * prec ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) return ap def bbox_iou(box1, box2, x1y1x2y2=False): """ Returns the IoU of two bounding boxes """ N, M = len(box1), len(box2) if x1y1x2y2: # Get the coordinates of bounding boxes b1_x1, b1_y1, b1_x2, b1_y2 = box1[:, 0], box1[:, 1], box1[:, 2], box1[:, 3] b2_x1, b2_y1, b2_x2, b2_y2 = box2[:, 0], box2[:, 1], box2[:, 2], box2[:, 3] else: # Transform from center and width to exact coordinates b1_x1, b1_x2 = box1[:, 0] - box1[:, 2] / 2, box1[:, 0] + box1[:, 2] / 2 b1_y1, b1_y2 = box1[:, 1] - box1[:, 3] / 2, box1[:, 1] + box1[:, 3] / 2 b2_x1, b2_x2 = box2[:, 0] - box2[:, 2] / 2, box2[:, 0] + box2[:, 2] / 2 b2_y1, b2_y2 = box2[:, 1] - box2[:, 3] / 2, box2[:, 1] + box2[:, 3] / 2 # get the coordinates of the intersection rectangle inter_rect_x1 = torch.max(b1_x1.unsqueeze(1), b2_x1) inter_rect_y1 = torch.max(b1_y1.unsqueeze(1), b2_y1) inter_rect_x2 = torch.min(b1_x2.unsqueeze(1), b2_x2) inter_rect_y2 = torch.min(b1_y2.unsqueeze(1), b2_y2) # Intersection area inter_area = torch.clamp(inter_rect_x2 - inter_rect_x1, 0) * torch.clamp(inter_rect_y2 - inter_rect_y1, 0) # Union Area b1_area = ((b1_x2 - b1_x1) * (b1_y2 - b1_y1)) b1_area = ((b1_x2 - b1_x1) * (b1_y2 - b1_y1)).view(-1,1).expand(N,M) b2_area = ((b2_x2 - b2_x1) * (b2_y2 - b2_y1)).view(1,-1).expand(N,M) return inter_area / (b1_area + b2_area - inter_area + 1e-16) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -