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Fix Yolo: swap width, height; Change box coord order; parsing fix #802

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Oct 31, 2019
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Fix Yolo: swap width, height; Change box coord order; parsing fix #802

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26 changes: 10 additions & 16 deletions utils/open_model_zoo/yolov3/interp.py
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Original file line number Diff line number Diff line change
Expand Up @@ -73,25 +73,25 @@ def parse_yolo_region(self, blob: 'np.ndarray', original_shape: list, params: di
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']
x = (col + predictions[box_index + 0 * side_square]) / params['side'] * 416
y = (row + predictions[box_index + 1 * side_square]) / params['side'] * 416
# 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])
w_exp = exp(predictions[box_index + 2 * side_square])
except OverflowError:
continue

w = w_exp * params['anchors'][2 * n] / 416
h = h_exp * params['anchors'][2 * n + 1] / 416
w = w_exp * params['anchors'][2 * n]
h = h_exp * params['anchors'][2 * n + 1]

for j in range(classes):
class_index = self.entry_index(params['side'], coords, classes, n * side_square + i,
coords + 1 + j)
Expand All @@ -105,8 +105,8 @@ def parse_yolo_region(self, blob: 'np.ndarray', original_shape: list, params: di
w=w,
class_id=j,
confidence=confidence,
h_scale=orig_im_h,
w_scale=orig_im_w))
h_scale=(orig_im_h/416),
w_scale=(orig_im_w/416)))


for detection in detections:
Expand All @@ -115,12 +115,7 @@ def parse_yolo_region(self, blob: 'np.ndarray', original_shape: list, params: di
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)
original_shape = (height, width)

# 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]
Expand Down Expand Up @@ -148,7 +143,6 @@ def parse_yolo_region(self, blob: 'np.ndarray', original_shape: list, params: di
parser.sort_objects()

objects = []

for obj in parser.objects:
if obj['confidence'] >= parser.PROB_THRESHOLD:
label = obj['class_id']
Expand All @@ -157,4 +151,4 @@ def parse_yolo_region(self, blob: 'np.ndarray', original_shape: list, params: di
ymin = obj['ymin']
ymax = obj['ymax']

results.add_box(xmax, ymax, xmin, ymin, label, frame_number)
results.add_box(xmin, ymin, xmax, ymax, label, frame_number)