Added support for 3D boxes with function weighted_boxes_fusion_3d

IDMIPPM · IDMIPPM · commit c789f0581623 · 2020-06-16T18:35:40.000+03:00
diff --git a/README.md b/README.md
@@ -72,6 +72,10 @@ boxes, scores, labels = weighted_boxes_fusion([boxes_list], [scores_list], [labe
 
 More examples can be found in [example.py](./example.py)
 
+#### 3D version
+
+There is support for 3D boxes in WBF method with `weighted_boxes_fusion_3d` function. Check example of usage in [example_3d.py](./example_3d.py)
+
 ## Accuracy and speed comparison
 
 Comparison was made for ensemble of 5 different object detection models predictions trained on [Open Images Dataset](https://storage.googleapis.com/openimages/web/index.html) (500 classes).
diff --git a/ensemble_boxes/__init__.py b/ensemble_boxes/__init__.py
@@ -6,3 +6,4 @@
 from .ensemble_boxes_nms import nms_method
 from .ensemble_boxes_nms import nms
 from .ensemble_boxes_nms import soft_nms
+from .ensemble_boxes_wbf_3d import weighted_boxes_fusion_3d
diff --git a/ensemble_boxes/ensemble_boxes_wbf_3d.py b/ensemble_boxes/ensemble_boxes_wbf_3d.py
@@ -0,0 +1,222 @@
+# coding: utf-8
+__author__ = 'ZFTurbo: https://kaggle.com/zfturbo'
+
+
+import warnings
+import numpy as np
+from numba import jit
+
+
+@jit(nopython=True)
+def bb_intersection_over_union_3d(A, B) -> float:
+    xA = max(A[0], B[0])
+    yA = max(A[1], B[1])
+    zA = max(A[2], B[2])
+    xB = min(A[3], B[3])
+    yB = min(A[4], B[4])
+    zB = min(A[5], B[5])
+
+    interVol = max(0, xB - xA) * max(0, yB - yA) * max(0, zB - zA)
+    if interVol == 0:
+        return 0.0
+
+    # compute the volume of both the prediction and ground-truth rectangular boxes
+    boxAVol = (A[3] - A[0]) * (A[4] - A[1]) * (A[5] - A[2])
+    boxBVol = (B[3] - B[0]) * (B[4] - B[1]) * (B[5] - B[2])
+
+    iou = interVol / float(boxAVol + boxBVol - interVol)
+    return iou
+
+
+def prefilter_boxes(boxes, scores, labels, weights, thr):
+    # Create dict with boxes stored by its label
+    new_boxes = dict()
+
+    for t in range(len(boxes)):
+
+        if len(boxes[t]) != len(scores[t]):
+            print('Error. Length of boxes arrays not equal to length of scores array: {} != {}'.format(len(boxes[t]), len(scores[t])))
+            exit()
+
+        if len(boxes[t]) != len(labels[t]):
+            print('Error. Length of boxes arrays not equal to length of labels array: {} != {}'.format(len(boxes[t]), len(labels[t])))
+            exit()
+
+        for j in range(len(boxes[t])):
+            score = scores[t][j]
+            if score < thr:
+                continue
+            label = int(labels[t][j])
+            box_part = boxes[t][j]
+            x1 = float(box_part[0])
+            y1 = float(box_part[1])
+            z1 = float(box_part[2])
+            x2 = float(box_part[3])
+            y2 = float(box_part[4])
+            z2 = float(box_part[5])
+
+            # Box data checks
+            if x2 < x1:
+                warnings.warn('X2 < X1 value in box. Swap them.')
+                x1, x2 = x2, x1
+            if y2 < y1:
+                warnings.warn('Y2 < Y1 value in box. Swap them.')
+                y1, y2 = y2, y1
+            if z2 < z1:
+                warnings.warn('Z2 < Z1 value in box. Swap them.')
+                z1, z2 = z2, z1
+            if x1 < 0:
+                warnings.warn('X1 < 0 in box. Set it to 0.')
+                x1 = 0
+            if x1 > 1:
+                warnings.warn('X1 > 1 in box. Set it to 1. Check that you normalize boxes in [0, 1] range.')
+                x1 = 1
+            if x2 < 0:
+                warnings.warn('X2 < 0 in box. Set it to 0.')
+                x2 = 0
+            if x2 > 1:
+                warnings.warn('X2 > 1 in box. Set it to 1. Check that you normalize boxes in [0, 1] range.')
+                x2 = 1
+            if y1 < 0:
+                warnings.warn('Y1 < 0 in box. Set it to 0.')
+                y1 = 0
+            if y1 > 1:
+                warnings.warn('Y1 > 1 in box. Set it to 1. Check that you normalize boxes in [0, 1] range.')
+                y1 = 1
+            if y2 < 0:
+                warnings.warn('Y2 < 0 in box. Set it to 0.')
+                y2 = 0
+            if y2 > 1:
+                warnings.warn('Y2 > 1 in box. Set it to 1. Check that you normalize boxes in [0, 1] range.')
+                y2 = 1
+            if z1 < 0:
+                warnings.warn('Z1 < 0 in box. Set it to 0.')
+                z1 = 0
+            if z1 > 1:
+                warnings.warn('Z1 > 1 in box. Set it to 1. Check that you normalize boxes in [0, 1] range.')
+                z1 = 1
+            if z2 < 0:
+                warnings.warn('Z2 < 0 in box. Set it to 0.')
+                z2 = 0
+            if z2 > 1:
+                warnings.warn('Z2 > 1 in box. Set it to 1. Check that you normalize boxes in [0, 1] range.')
+                z2 = 1
+            if (x2 - x1) * (y2 - y1) * (z2 - z1) == 0.0:
+                warnings.warn("Zero volume box skipped: {}.".format(box_part))
+                continue
+
+            b = [int(label), float(score) * weights[t], x1, y1, z1, x2, y2, z2]
+            if label not in new_boxes:
+                new_boxes[label] = []
+            new_boxes[label].append(b)
+
+    # Sort each list in dict by score and transform it to numpy array
+    for k in new_boxes:
+        current_boxes = np.array(new_boxes[k])
+        new_boxes[k] = current_boxes[current_boxes[:, 1].argsort()[::-1]]
+
+    return new_boxes
+
+
+def get_weighted_box(boxes, conf_type='avg'):
+    """
+    Create weighted box for set of boxes
+    :param boxes: set of boxes to fuse
+    :param conf_type: type of confidence one of 'avg' or 'max'
+    :return: weighted box
+    """
+
+    box = np.zeros(8, dtype=np.float32)
+    conf = 0
+    conf_list = []
+    for b in boxes:
+        box[2:] += (b[1] * b[2:])
+        conf += b[1]
+        conf_list.append(b[1])
+    box[0] = boxes[0][0]
+    if conf_type == 'avg':
+        box[1] = conf / len(boxes)
+    elif conf_type == 'max':
+        box[1] = np.array(conf_list).max()
+    box[2:] /= conf
+    return box
+
+
+def find_matching_box(boxes_list, new_box, match_iou):
+    best_iou = match_iou
+    best_index = -1
+    for i in range(len(boxes_list)):
+        box = boxes_list[i]
+        if box[0] != new_box[0]:
+            continue
+        iou = bb_intersection_over_union_3d(box[2:], new_box[2:])
+        if iou > best_iou:
+            best_index = i
+            best_iou = iou
+
+    return best_index, best_iou
+
+
+def weighted_boxes_fusion_3d(boxes_list, scores_list, labels_list, weights=None, iou_thr=0.55, skip_box_thr=0.0, conf_type='avg', allows_overflow=False):
+    '''
+    :param boxes_list: list of boxes predictions from each model, each box is 6 numbers.
+    It has 3 dimensions (models_number, model_preds, 6)
+    Order of boxes: x1, y1, z1, x2, y2 z2. We expect float normalized coordinates [0; 1]
+    :param scores_list: list of scores for each model
+    :param labels_list: list of labels for each model
+    :param weights: list of weights for each model. Default: None, which means weight == 1 for each model
+    :param iou_thr: IoU value for boxes to be a match
+    :param skip_box_thr: exclude boxes with score lower than this variable
+    :param conf_type: how to calculate confidence in weighted boxes. 'avg': average value, 'max': maximum value
+    :param allows_overflow: false if we want confidence score not exceed 1.0
+
+    :return: boxes: boxes coordinates (Order of boxes: x1, y1, z1, x2, y2, z2).
+    :return: scores: confidence scores
+    :return: labels: boxes labels
+    '''
+
+    if weights is None:
+        weights = np.ones(len(boxes_list))
+    if len(weights) != len(boxes_list):
+        print('Warning: incorrect number of weights {}. Must be: {}. Set weights equal to 1.'.format(len(weights), len(boxes_list)))
+        weights = np.ones(len(boxes_list))
+    weights = np.array(weights)
+
+    if conf_type not in ['avg', 'max']:
+        print('Error. Unknown conf_type: {}. Must be "avg" or "max". Use "avg"'.format(conf_type))
+        conf_type = 'avg'
+
+    filtered_boxes = prefilter_boxes(boxes_list, scores_list, labels_list, weights, skip_box_thr)
+    if len(filtered_boxes) == 0:
+        return np.zeros((0, 6)), np.zeros((0,)), np.zeros((0,))
+
+    overall_boxes = []
+    for label in filtered_boxes:
+        boxes = filtered_boxes[label]
+        new_boxes = []
+        weighted_boxes = []
+
+        # Clusterize boxes
+        for j in range(0, len(boxes)):
+            index, best_iou = find_matching_box(weighted_boxes, boxes[j], iou_thr)
+            if index != -1:
+                new_boxes[index].append(boxes[j])
+                weighted_boxes[index] = get_weighted_box(new_boxes[index], conf_type)
+            else:
+                new_boxes.append([boxes[j].copy()])
+                weighted_boxes.append(boxes[j].copy())
+
+        # Rescale confidence based on number of models and boxes
+        for i in range(len(new_boxes)):
+            if not allows_overflow:
+                weighted_boxes[i][1] = weighted_boxes[i][1] * min(weights.sum(), len(new_boxes[i])) / weights.sum()
+            else:
+                weighted_boxes[i][1] = weighted_boxes[i][1] * len(new_boxes[i]) / weights.sum()
+        overall_boxes.append(np.array(weighted_boxes))
+
+    overall_boxes = np.concatenate(overall_boxes, axis=0)
+    overall_boxes = overall_boxes[overall_boxes[:, 1].argsort()[::-1]]
+    boxes = overall_boxes[:, 2:]
+    scores = overall_boxes[:, 1]
+    labels = overall_boxes[:, 0]
+    return boxes, scores, labels
diff --git a/example_3d.py b/example_3d.py
@@ -0,0 +1,142 @@
+# coding: utf-8
+__author__ = 'ZFTurbo: https://kaggle.com/zfturbo'
+
+
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d.art3d import Poly3DCollection
+from ensemble_boxes import *
+
+
+def plot_cube(ax, cube_definition, lbl, thickness):
+    cube_definition_array = [
+        np.array(list(item))
+        for item in cube_definition
+    ]
+
+    points = []
+    points += cube_definition_array
+    vectors = [
+        cube_definition_array[1] - cube_definition_array[0],
+        cube_definition_array[2] - cube_definition_array[0],
+        cube_definition_array[3] - cube_definition_array[0]
+    ]
+
+    points += [cube_definition_array[0] + vectors[0] + vectors[1]]
+    points += [cube_definition_array[0] + vectors[0] + vectors[2]]
+    points += [cube_definition_array[0] + vectors[1] + vectors[2]]
+    points += [cube_definition_array[0] + vectors[0] + vectors[1] + vectors[2]]
+
+    points = np.array(points)
+
+    edges = [
+        [points[0], points[3], points[5], points[1]],
+        [points[1], points[5], points[7], points[4]],
+        [points[4], points[2], points[6], points[7]],
+        [points[2], points[6], points[3], points[0]],
+        [points[0], points[2], points[4], points[1]],
+        [points[3], points[6], points[7], points[5]]
+    ]
+
+    faces = Poly3DCollection(edges, linewidths=thickness + 1)
+    if lbl == 0:
+        faces.set_edgecolor((1, 0, 0))
+    else:
+        faces.set_edgecolor((0, 0, 1))
+    faces.set_facecolor((0, 0, 1, 0.1))
+
+    ax.add_collection3d(faces)
+    ax.scatter(points[:, 0], points[:, 1], points[:, 2], s=0)
+
+
+def show_boxes(boxes_list, scores_list, labels_list, image_size=800):
+    image = np.zeros((image_size, image_size, 3), dtype=np.uint8)
+    image[...] = 255
+    fig = plt.figure()
+    ax = fig.add_subplot(111, projection='3d')
+
+    for i in range(len(boxes_list)):
+        for j in range(len(boxes_list[i])):
+            x1 = int(image_size * boxes_list[i][j][0])
+            y1 = int(image_size * boxes_list[i][j][1])
+            z1 = int(image_size * boxes_list[i][j][2])
+            x2 = int(image_size * boxes_list[i][j][3])
+            y2 = int(image_size * boxes_list[i][j][4])
+            z2 = int(image_size * boxes_list[i][j][5])
+            lbl = labels_list[i][j]
+            cube_definition = [
+                (x1, y1, z1), (x1, y2, z1), (x2, y1, z1), (x1, y1, z2)
+            ]
+            plot_cube(ax, cube_definition, lbl, int(4 * scores_list[i][j]))
+
+    plt.show()
+
+
+def example_wbf_3d_2_models(iou_thr=0.55, draw_image=True):
+    """
+    This example shows how to ensemble boxes from 2 models using WBF_3D method
+    :return: 
+    """
+
+    boxes_list = [
+        [
+            [0.00, 0.51, 0.41, 0.81, 0.91, 0.78],
+            [0.10, 0.31, 0.45, 0.71, 0.61, 0.85],
+            [0.01, 0.32, 0.55, 0.83, 0.93, 0.95],
+            [0.02, 0.53, 0.11, 0.11, 0.94, 0.55],
+            [0.03, 0.24, 0.34, 0.12, 0.35, 0.67],
+        ],
+        [
+            [0.04, 0.56, 0.36, 0.84, 0.92, 0.82],
+            [0.12, 0.33, 0.46, 0.72, 0.64, 0.75],
+            [0.38, 0.66, 0.55, 0.79, 0.95, 0.90],
+            [0.08, 0.49, 0.15, 0.21, 0.89, 0.67],
+        ],
+    ]
+    scores_list = [
+        [
+            0.9,
+            0.8,
+            0.2,
+            0.4,
+            0.7,
+        ],
+        [
+            0.5,
+            0.8,
+            0.7,
+            0.3,
+        ]
+    ]
+    labels_list = [
+        [
+            0,
+            1,
+            0,
+            1,
+            1,
+        ],
+        [
+            1,
+            1,
+            1,
+            0,
+        ]
+    ]
+    weights = [2, 1]
+    if draw_image:
+        show_boxes(boxes_list, scores_list, labels_list)
+
+    boxes, scores, labels = weighted_boxes_fusion_3d(boxes_list, scores_list, labels_list, weights=weights, iou_thr=iou_thr, skip_box_thr=0.0)
+
+    if draw_image:
+        show_boxes([boxes], [scores], [labels.astype(np.int32)])
+
+    print(len(boxes))
+    print(boxes)
+
+
+if __name__ == '__main__':
+    draw_image = True
+    example_wbf_3d_2_models(iou_thr=0.2, draw_image=draw_image)
+
diff --git a/setup.py b/setup.py
@@ -5,7 +5,7 @@
 
 setup(
     name='ensemble_boxes',
-    version='1.0.3',
+    version='1.0.4',
     author='Roman Solovyev (ZFTurbo)',
     packages=['ensemble_boxes', ],
     url='https://github.com/ZFTurbo/Weighted-Boxes-Fusion',
