def nms(y_pred_conf, y_pred_loc, prob):
"""
Non-Maximum Suppression (NMS)
Performs NMS on all boxes of each class where predicted probability > CONF_THRES
For all boxes exceeding IOU threshold, select the box with highest confidence
Returns a lsit of box coordinates post-NMS
Arguments:
* y_pred_conf: Class predictions, numpy array of shape (num_feature_map_cells * num_defaul_boxes,)
* y_pred_loc: Bounding box coordinates, numpy array of shape (num_feature_map_cells * num_defaul_boxes * 4,)
These coordinates are normalized coordinates relative to center of feature map cell
* prob: Class probabilities, numpy array of shape (num_feature_map_cells * num_defaul_boxes,)
Returns:
* boxes: Numpy array of boxes, with shape (num_boxes, 6). shape[0] is interpreted as:
[x1, y1, x2, y2, class, probability], where x1/y1/x2/y2 are the coordinates of the
upper-left and lower-right corners. Box coordinates assume the image size is IMG_W x IMG_H.
Remember to rescale box coordinates if your target image has different dimensions.
"""
# Keep track of boxes for each class
class_boxes = {} # class -> [(x1, y1, x2, y2, prob), (...), ...]
with open('signnames.csv', 'r') as f:
for line in f:
cls, _ = line.split(',')
class_boxes[float(cls)] = []
# Go through all possible boxes and perform class-based greedy NMS (greedy based on class prediction confidence)
y_idx = 0
for fm_size in FM_SIZES:
fm_h, fm_w = fm_size # feature map height and width
for row in range(fm_h):
for col in range(fm_w):
for db in DEFAULT_BOXES:
# Only perform calculations if class confidence > CONF_THRESH and not background class
if prob[y_idx] > CONF_THRESH and y_pred_conf[y_idx] > 0.:
# Calculate absolute coordinates of predicted bounding box
xc, yc = col + 0.5, row + 0.5 # center of current feature map cell
center_coords = np.array([xc, yc, xc, yc])
abs_box_coords = center_coords + y_pred_loc[
y_idx * 4:y_idx * 4 +
4] # predictions are offsets to center of fm cell
# Calculate predicted box coordinates in actual image
scale = np.array([
IMG_W / fm_w, IMG_H / fm_h, IMG_W / fm_w,
IMG_H / fm_h
])
box_coords = abs_box_coords * scale
box_coords = [int(round(x)) for x in box_coords]
# Compare this box to all previous boxes of this class
cls = y_pred_conf[y_idx]
cls_prob = prob[y_idx]
box = (*box_coords, cls, cls_prob)
if len(class_boxes[cls]) == 0:
class_boxes[cls].append(box)
else:
suppressed = False # did this box suppress other box(es)?
overlapped = False # did this box overlap with other box(es)?
for other_box in class_boxes[cls]:
iou = calc_iou(box[:4], other_box[:4])
if iou > NMS_IOU_THRESH:
overlapped = True
# If current box has higher confidence than other box
if box[5] > other_box[5]:
class_boxes[cls].remove(other_box)
suppressed = True
if suppressed or not overlapped:
class_boxes[cls].append(box)
y_idx += 1
# Gather all the pruned boxes and return them
boxes = []
for cls in class_boxes.keys():
for class_box in class_boxes[cls]:
boxes.append(class_box)
boxes = np.array(boxes)
return boxes
def nms(y_pred_conf, y_pred_loc, prob):
"""
Non-Maximum Suppression (NMS)
Performs NMS on all boxes of each class where predicted probability > CONF_THRES
For all boxes exceeding IOU threshold, select the box with highest confidence
Returns a lsit of box coordinates post-NMS
Arguments:
* y_pred_conf: Class predictions, numpy array of shape (num_feature_map_cells * num_defaul_boxes,)
* y_pred_loc: Bounding box coordinates, numpy array of shape (num_feature_map_cells * num_defaul_boxes * 4,)
These coordinates are normalized coordinates relative to center of feature map cell
* prob: Class probabilities, numpy array of shape (num_feature_map_cells * num_defaul_boxes,)
Returns:
* boxes: Numpy array of boxes, with shape (num_boxes, 6). shape[0] is interpreted as:
[x1, y1, x2, y2, class, probability], where x1/y1/x2/y2 are the coordinates of the
upper-left and lower-right corners. Box coordinates assume the image size is IMG_W x IMG_H.
Remember to rescale box coordinates if your target image has different dimensions.
"""
# Keep track of boxes for each class
class_boxes = {} # class -> [(x1, y1, x2, y2, prob), (...), ...]
with open('signnames.csv', 'r') as f:
for line in f:
cls, _ = line.split(',')
class_boxes[float(cls)] = []
# Go through all possible boxes and perform class-based greedy NMS (greedy based on class prediction confidence)
y_idx = 0
for fm_size in FM_SIZES:
fm_h, fm_w = fm_size # feature map height and width
for row in range(fm_h):
for col in range(fm_w):
for db in DEFAULT_BOXES:
# Only perform calculations if class confidence > CONF_THRESH and not background class
if prob[y_idx] > CONF_THRESH and y_pred_conf[y_idx] > 0.:
# Calculate absolute coordinates of predicted bounding box
xc, yc = col + 0.5, row + 0.5 # center of current feature map cell
center_coords = np.array([xc, yc, xc, yc])
abs_box_coords = center_coords + y_pred_loc[y_idx*4 : y_idx*4 + 4] # predictions are offsets to center of fm cell
# Calculate predicted box coordinates in actual image
scale = np.array([IMG_W/fm_w, IMG_H/fm_h, IMG_W/fm_w, IMG_H/fm_h])
box_coords = abs_box_coords * scale
box_coords = [int(round(x)) for x in box_coords]
# Compare this box to all previous boxes of this class
cls = y_pred_conf[y_idx]
cls_prob = prob[y_idx]
box = (*box_coords, cls, cls_prob)
if len(class_boxes[cls]) == 0:
class_boxes[cls].append(box)
else:
suppressed = False # did this box suppress other box(es)?
overlapped = False # did this box overlap with other box(es)?
for other_box in class_boxes[cls]:
iou = calc_iou(box[:4], other_box[:4])
if iou > NMS_IOU_THRESH:
overlapped = True
# If current box has higher confidence than other box
if box[5] > other_box[5]:
class_boxes[cls].remove(other_box)
suppressed = True
if suppressed or not overlapped:
class_boxes[cls].append(box)
y_idx += 1
# Gather all the pruned boxes and return them
boxes = []
for cls in class_boxes.keys():
for class_box in class_boxes[cls]:
boxes.append(class_box)
boxes = np.array(boxes)
return boxes