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Update tf_iou.py #89

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Jun 18, 2018
Merged

Update tf_iou.py #89

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44 changes: 22 additions & 22 deletions examples/utils/tf_iou.py
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Original file line number Diff line number Diff line change
Expand Up @@ -66,28 +66,28 @@ def pred_bbox():
detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
for image_path in test_imgs:
image = Image.open(image_path)
image_np = load_image_into_numpy_array(image)
# the array based representation of the image will be used later in order to prepare the
# result image with boxes and labels on it.
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
# Actual detection.
(boxes, scores, classes, num) = sess.run(
[detection_boxes, detection_scores, detection_classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
### 256 here is the image size from Label Maker, adjust it according to your input image size.
bboxe = (boxes*256).astype(np.int)
bboxe = np.squeeze(bboxe)
score = np.squeeze(((scores*100).transpose()).astype(np.int))
### only keep the bbox that prediction score is higher than 50.
bboxes = bboxe[score > 50]
if bboxes.any():
bboxes_ls = bboxes.tolist()
for bbox in bboxes_ls:
# pred_bboxes.append([image_path[-18:],bbox])
pred_bboxes.append(bbox)
for image_path in test_imgs:
image = Image.open(image_path)
image_np = load_image_into_numpy_array(image)
# the array based representation of the image will be used later in order to prepare the
# result image with boxes and labels on it.
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
# Actual detection.
(boxes, scores, classes, num) = sess.run(
[detection_boxes, detection_scores, detection_classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
### 256 here is the image size from Label Maker, adjust it according to your input image size.
bboxe = (boxes*256).astype(np.int)
bboxe = np.squeeze(bboxe)
score = np.squeeze(((scores*100).transpose()).astype(np.int))
### only keep the bbox that prediction score is higher than 50.
bboxes = bboxe[score > 50]
if bboxes.any():
bboxes_ls = bboxes.tolist()
for bbox in bboxes_ls:
# pred_bboxes.append([image_path[-18:],bbox])
pred_bboxes.append(bbox)
return pred_bboxes

def gr_bbox():
Expand Down