From 364621c81e899ffaf2de512ba24d25969d8e15c3 Mon Sep 17 00:00:00 2001
From: Hao Tang <297942868@qq.com>
Date: Wed, 24 Jun 2020 11:40:32 -0700
Subject: [PATCH] Update util.py

add py_nms, py_box_overlap and average_precision
---
 utils/util.py | 118 ++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 118 insertions(+)

diff --git a/utils/util.py b/utils/util.py
index 6650b51..457cc78 100644
--- a/utils/util.py
+++ b/utils/util.py
@@ -101,6 +101,124 @@ def npy2submission(set_name, save_path, bbox_dir, prep_dir, postfix='detection')
     submission.to_csv(save_path, sep=',', index=False, header=True)
 
 
+def average_precision(labels, y_pred):
+    # Compute number of objects
+    true_objects = len(np.unique(labels))
+    pred_objects = len(np.unique(y_pred))
+    print("Number of true objects:", true_objects)
+    print("Number of predicted objects:", pred_objects)
+
+    # Compute intersection between all objects
+    intersection = np.histogram2d(labels.flatten(), y_pred.flatten(), bins=(true_objects, pred_objects))[0]
+
+    # Compute areas (needed for finding the union between all objects)
+    area_true = np.histogram(labels, bins = true_objects)[0]
+    area_pred = np.histogram(y_pred, bins = pred_objects)[0]
+    area_true = np.expand_dims(area_true, -1)
+    area_pred = np.expand_dims(area_pred, 0)
+
+    # Compute union
+    union = area_true + area_pred - intersection
+
+    # Exclude background from the analysis
+    intersection = intersection[1:,1:]
+    union = union[1:,1:]
+    union[union == 0] = 1e-9
+
+    # Compute the intersection over union
+    iou = intersection / union
+    dice = 2 * intersection / (union + intersection)
+
+    # Precision helper function
+    def precision_at(threshold, iou):
+        matches = iou > threshold
+        true_positives = np.sum(matches, axis=1) == 1   # Correct objects
+        false_positives = np.sum(matches, axis=0) == 0  # Missed objects
+        false_negatives = np.sum(matches, axis=1) == 0  # Extra objects
+        tp, fp, fn = np.sum(true_positives), np.sum(false_positives), np.sum(false_negatives)
+        return tp, fp, fn
+
+    # Loop over IoU thresholds
+    prec = []
+    # print("Thresh\tTP\tFP\tFN\tPrec.")
+    for t in np.arange(0.5, 1.0, 0.05):
+        tp, fp, fn = precision_at(t, iou)
+        p = tp / float(tp + fp + fn)
+        # print("{:1.3f}\t{}\t{}\t{}\t{:1.3f}".format(t, tp, fp, fn, p))
+        prec.append(p)
+    # print("AP\t-\t-\t-\t{:1.3f}".format(np.mean(prec)))
+    return prec, np.max(dice, axis=1)
+
+
+def py_nms(dets, thresh):
+    # Check the input dtype
+    if isinstance(dets, torch.Tensor):
+        if dets.is_cuda:
+            dets = dets.cpu()
+        dets = dets.data.numpy()
+        
+    z = dets[:, 1]
+    y = dets[:, 2]
+    x = dets[:, 3]
+    d = dets[:, 4]
+    h = dets[:, 5]
+    w = dets[:, 6]
+    scores = dets[:, 0]
+
+    areas = d * h * w
+    order = scores.argsort()[::-1]
+    keep = []
+    while order.size > 0:
+        i = order[0]
+        keep.append(i)
+
+        xx0 = np.maximum(x[i] - w[i] / 2., x[order[1:]] - w[order[1:]] / 2.)
+        yy0 = np.maximum(y[i] - h[i] / 2., y[order[1:]] - h[order[1:]] / 2.)
+        zz0 = np.maximum(z[i] - d[i] / 2., z[order[1:]] - d[order[1:]] / 2.)
+        xx1 = np.minimum(x[i] + w[i] / 2., x[order[1:]] + w[order[1:]] / 2.)
+        yy1 = np.minimum(y[i] + h[i] / 2., y[order[1:]] + h[order[1:]] / 2.)
+        zz1 = np.minimum(z[i] + d[i] / 2., z[order[1:]] + d[order[1:]] / 2.)
+
+        inter_w = np.maximum(0.0, xx1 - xx0)
+        inter_h = np.maximum(0.0, yy1 - yy0)
+        inter_d = np.maximum(0.0, zz1 - zz0)
+        intersect = inter_w * inter_h * inter_d
+        overlap = intersect / (areas[i] + areas[order[1:]] - intersect)
+
+        inds = np.where(overlap <= thresh)[0]
+        order = order[inds + 1]
+
+    return torch.from_numpy(dets[keep]), torch.LongTensor(keep)
+
+
+def py_box_overlap(boxes1, boxes2):
+    overlap = np.zeros((len(boxes1), len(boxes2)))
+
+    z1, y1, x1 = boxes1[:, 0], boxes1[:, 1], boxes1[:, 2]
+    d1, h1, w1 = boxes1[:, 3], boxes1[:, 4], boxes1[:, 5]
+    areas1 = d1 * h1 * w1
+
+    z2, y2, x2 = boxes2[:, 0], boxes2[:, 1], boxes2[:, 2]
+    d2, h2, w2 = boxes2[:, 3], boxes2[:, 4], boxes2[:, 5]
+    areas2 = d2 * h2 * w2
+
+    for i in range(len(boxes1)):
+        xx0 = np.maximum(x1[i] - w1[i] / 2., x2 - w2 / 2.)
+        yy0 = np.maximum(y1[i] - h1[i] / 2., y2 - h2 / 2.)
+        zz0 = np.maximum(z1[i] - d1[i] / 2., z2 - d2 / 2.)
+        xx1 = np.minimum(x1[i] + w1[i] / 2., x2 + w2 / 2.)
+        yy1 = np.minimum(y1[i] + h1[i] / 2., y2 + h2 / 2.)
+        zz1 = np.minimum(z1[i] + d1[i] / 2., z2 + d2 / 2.)
+
+        inter_w = np.maximum(0.0, xx1 - xx0)
+        inter_h = np.maximum(0.0, yy1 - yy0)
+        inter_d = np.maximum(0.0, zz1 - zz0)
+        intersect = inter_w * inter_h * inter_d
+        overlap[i] = intersect / (areas1[i] + areas2 - intersect)
+
+    return overlap
+    
+    
 def center_box_to_coord_box(bboxes):
     """
     Convert bounding box using center of rectangle and side lengths representation to