You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
161 lines
6.8 KiB
Python
161 lines
6.8 KiB
Python
from math import exp
|
|
|
|
|
|
class Parser:
|
|
IOU_THRESHOLD = 0.4
|
|
PROB_THRESHOLD = 0.5
|
|
|
|
def __init__(self):
|
|
self.objects = []
|
|
|
|
def scale_bbox(self, x, y, h, w, class_id, confidence, h_scale, w_scale):
|
|
xmin = int((x - w / 2) * w_scale)
|
|
ymin = int((y - h / 2) * h_scale)
|
|
xmax = int(xmin + w * w_scale)
|
|
ymax = int(ymin + h * h_scale)
|
|
|
|
return dict(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, class_id=class_id, confidence=confidence)
|
|
|
|
def entry_index(self, side, coord, classes, location, entry):
|
|
side_power_2 = side ** 2
|
|
n = location // side_power_2
|
|
loc = location % side_power_2
|
|
return int(side_power_2 * (n * (coord + classes + 1) + entry) + loc)
|
|
|
|
def intersection_over_union(self, box_1, box_2):
|
|
width_of_overlap_area = min(box_1['xmax'], box_2['xmax']) - max(box_1['xmin'], box_2['xmin'])
|
|
height_of_overlap_area = min(box_1['ymax'], box_2['ymax']) - max(box_1['ymin'], box_2['ymin'])
|
|
if width_of_overlap_area < 0 or height_of_overlap_area < 0:
|
|
area_of_overlap = 0
|
|
else:
|
|
area_of_overlap = width_of_overlap_area * height_of_overlap_area
|
|
box_1_area = (box_1['ymax'] - box_1['ymin']) * (box_1['xmax'] - box_1['xmin'])
|
|
box_2_area = (box_2['ymax'] - box_2['ymin']) * (box_2['xmax'] - box_2['xmin'])
|
|
area_of_union = box_1_area + box_2_area - area_of_overlap
|
|
if area_of_union == 0:
|
|
return 0
|
|
return area_of_overlap / area_of_union
|
|
|
|
|
|
def sort_objects(self):
|
|
self.objects = sorted(self.objects, key=lambda obj : obj['confidence'], reverse=True)
|
|
|
|
for i in range(len(self.objects)):
|
|
if self.objects[i]['confidence'] == 0:
|
|
continue
|
|
for j in range(i + 1, len(self.objects)):
|
|
if self.intersection_over_union(self.objects[i], self.objects[j]) > self.IOU_THRESHOLD:
|
|
self.objects[j]['confidence'] = 0
|
|
|
|
def parse_yolo_region(self, blob: 'np.ndarray', original_shape: list, params: dict) -> list:
|
|
|
|
# YOLO magic numbers
|
|
# See: https://github.com/opencv/open_model_zoo/blob/acf297c73db8cb3f68791ae1fad4a7cc4a6039e5/demos/python_demos/object_detection_demo_yolov3_async/object_detection_demo_yolov3_async.py#L61
|
|
num = 3
|
|
coords = 4
|
|
classes = 80
|
|
# -----------------
|
|
|
|
_, _, out_blob_h, out_blob_w = blob.shape
|
|
assert out_blob_w == out_blob_h, "Invalid size of output blob. It sould be in NCHW layout and height should " \
|
|
"be equal to width. Current height = {}, current width = {}" \
|
|
"".format(out_blob_h, out_blob_w)
|
|
|
|
# ------ Extracting layer parameters --
|
|
orig_im_h, orig_im_w = original_shape
|
|
predictions = blob.flatten()
|
|
side_square = params['side'] * params['side']
|
|
|
|
# ------ Parsing YOLO Region output --
|
|
for i in range(side_square):
|
|
row = i // params['side']
|
|
col = i % params['side']
|
|
for n in range(num):
|
|
# -----entry index calcs------
|
|
obj_index = self.entry_index(params['side'], coords, classes, n * side_square + i, coords)
|
|
# -----entry index calcs------
|
|
scale = predictions[obj_index]
|
|
if scale < self.PROB_THRESHOLD:
|
|
continue
|
|
box_index = self.entry_index(params['side'], coords, classes, n * side_square + i, 0)
|
|
|
|
# Network produces location predictions in absolute coordinates of feature maps.
|
|
# Scale it to relative coordinates.
|
|
x = (col + predictions[box_index + 0 * side_square]) / params['side']
|
|
y = (row + predictions[box_index + 1 * side_square]) / params['side']
|
|
# Value for exp is very big number in some cases so following construction is using here
|
|
try:
|
|
w_exp = exp(predictions[box_index + 2 * side_square])
|
|
h_exp = exp(predictions[box_index + 3 * side_square])
|
|
except OverflowError:
|
|
continue
|
|
|
|
w = w_exp * params['anchors'][2 * n] / 416
|
|
h = h_exp * params['anchors'][2 * n + 1] / 416
|
|
for j in range(classes):
|
|
class_index = self.entry_index(params['side'], coords, classes, n * side_square + i,
|
|
coords + 1 + j)
|
|
confidence = scale * predictions[class_index]
|
|
if confidence < self.PROB_THRESHOLD:
|
|
continue
|
|
|
|
self.objects.append(self.scale_bbox(x=x,
|
|
y=y,
|
|
h=h,
|
|
w=w,
|
|
class_id=j,
|
|
confidence=confidence,
|
|
h_scale=orig_im_h,
|
|
w_scale=orig_im_w))
|
|
|
|
|
|
for detection in detections:
|
|
frame_number = detection['frame_id']
|
|
height = detection['frame_height']
|
|
width = detection['frame_width']
|
|
detection = detection['detections']
|
|
|
|
original_shape = (width, height)
|
|
|
|
resized_width = width / 416
|
|
resized_height = height / 416
|
|
|
|
resized_shape = (resized_width, resized_height)
|
|
|
|
# https://github.com/opencv/open_model_zoo/blob/master/demos/python_demos/object_detection_demo_yolov3_async/object_detection_demo_yolov3_async.py#L72
|
|
anchors = [10,13,16,30,33,23,30,61,62,45,59,119,116,90,156,198,373,326]
|
|
conv_6 = {'side': 13, 'mask': [6,7,8]}
|
|
conv_14 = {'side': 26, 'mask': [3,4,5]}
|
|
conv_22 = {'side': 52, 'mask': [0,1,2]}
|
|
|
|
yolo_params = {'detector/yolo-v3/Conv_6/BiasAdd/YoloRegion': conv_6,
|
|
'detector/yolo-v3/Conv_14/BiasAdd/YoloRegion': conv_14,
|
|
'detector/yolo-v3/Conv_22/BiasAdd/YoloRegion': conv_22}
|
|
|
|
for conv_net in yolo_params.values():
|
|
mask = conv_net['mask']
|
|
masked_anchors = []
|
|
for idx in mask:
|
|
masked_anchors += [anchors[idx * 2], anchors[idx * 2 + 1]]
|
|
|
|
conv_net['anchors'] = masked_anchors
|
|
|
|
parser = Parser()
|
|
|
|
for name, blob in detection.items():
|
|
parser.parse_yolo_region(blob, original_shape, yolo_params[name])
|
|
|
|
parser.sort_objects()
|
|
|
|
objects = []
|
|
|
|
for obj in parser.objects:
|
|
if obj['confidence'] >= parser.PROB_THRESHOLD:
|
|
label = obj['class_id']
|
|
xmin = obj['xmin']
|
|
xmax = obj['xmax']
|
|
ymin = obj['ymin']
|
|
ymax = obj['ymax']
|
|
|
|
results.add_box(xmax, ymax, xmin, ymin, label, frame_number)
|