def test_nms(self):
# loop over the images
for (imagePath, boundingBoxes, num_face) in self.images:
# load the image and clone it
print("[x] %d initial bounding boxes" % (len(boundingBoxes)))
image = cv2.imread(imagePath)
orig = image.copy()
# loop over the bounding boxes for each image and draw them
for (startX, startY, endX, endY, _) in boundingBoxes:
cv2.rectangle(orig, (startX, startY), (endX, endY), (0, 0, 255), 2)
# perform non-maximum suppression on the bounding boxes
pick = func.nms(boundingBoxes[:, :4], boundingBoxes[:, 4], 0.3)
print("[x] after applying non-maximum, %d bounding boxes" % (len(pick)))
# loop over the picked bounding boxes and draw them
for i in pick:
(startX, startY, endX, endY) = boundingBoxes[i][:4]
cv2.rectangle(image, (startX, startY), (endX, endY), (0, 255, 0), 2)
# # display the images
# cv2.imshow("Original", orig)
# cv2.imshow("After NMS", image)
# cv2.waitKey(0)
self.assertEqual(len(pick), num_face)
def stage_two(self, imgs, boxes, threshold, nms_threshold):
# no candidate face found.
if boxes.shape[0] == 0:
return boxes
width = imgs.shape[2]
height = imgs.shape[3]
lablels = boxes[:, -1]
boxes = boxes[:, :4]
num_img = imgs.shape[0]
# get candidate faces
candidate_faces = list()
for box, label in zip(boxes, lablels):
im = imgs[label, :, box[1]: box[3], box[0]: box[2]].unsqueeze(0)
im = torch.nn.functional.interpolate(
im, size=(24, 24), mode='bilinear')
candidate_faces.append(im)
candidate_faces = torch.cat(candidate_faces, 0)
# rnet forward pass
p_distribution, box_regs, _ = self.rnet(candidate_faces)
# filter negative boxes
scores = p_distribution[:, 1]
mask = (scores >= threshold)
boxes = boxes[mask]
box_regs = box_regs[mask]
scores = scores[mask]
labels = lablels[mask]
if boxes.shape[0] != 0:
boxes = self._calibrate_box(boxes, box_regs)
boxes = self._convert_to_square(boxes)
boxes = self._refine_boxes(boxes, width, height)
final_boxes = torch.empty(0, dtype=torch.int32, device=self.device)
final_img_labels = torch.empty(0, dtype=torch.int32, device=self.device)
for i in range(num_img):
mask = labels == i
keep = func.nms(boxes[mask].cpu().numpy(),
scores[mask].cpu().numpy(), nms_threshold)
final_boxes = torch.cat([final_boxes, boxes[mask][keep]])
final_img_labels = torch.cat([final_img_labels, labels[mask][keep]])
return torch.cat([final_boxes, final_img_labels.unsqueeze(1 )], -1)
else:
return boxes
def stage_one(self, img, threshold, factor, minsize, nms_threshold):
width = img.shape[2]
height = img.shape[3]
# Compute valid scales
scales = []
cur_width = width
cur_height = height
cur_factor = 1
while cur_width >= 12 and cur_height >= 12:
if 12 / cur_factor >= minsize: # Ignore boxes that smaller than minsize
w = cur_width
h = cur_height
scales.append((w, h, cur_factor))
cur_factor *= factor
cur_width = math.ceil(cur_width * factor)
cur_height = math.ceil(cur_height * factor)
# Get candidate boxesi ph
candidate_boxes = torch.empty((0, 4),
dtype=torch.int32,
device=self.device)
candidate_scores = torch.empty((0), device=self.device)
candidate_offsets = torch.empty((0, 4),
dtype=torch.float32,
device=self.device)
for w, h, f in scales:
resize_img = torch.nn.functional.interpolate(img,
size=(w, h),
mode='bilinear')
p_distribution, box_regs, _ = self.pnet(resize_img)
candidate, scores, offsets = self._generate_bboxes(
p_distribution, box_regs, f, threshold)
candidate_boxes = torch.cat([candidate_boxes, candidate])
candidate_scores = torch.cat([candidate_scores, scores])
candidate_offsets = torch.cat([candidate_offsets, offsets])
# nms
if candidate_boxes.shape[0] != 0:
candidate_boxes = self._calibrate_box(candidate_boxes,
candidate_offsets)
keep = func.nms(candidate_boxes.cpu().numpy(),
candidate_scores.cpu().numpy(),
nms_threshold,
device=self.device)
return candidate_boxes[keep]
else:
return candidate_boxes
def stage_three(self, img, boxes, threshold, nms_threshold):
# no candidate face found.
if boxes.shape[0] == 0:
return boxes, torch.empty(0, device=self.device, dtype=torch.int32)
width = img.shape[2]
height = img.shape[3]
boxes = self._convert_to_square(boxes)
boxes = self._refine_boxes(boxes, width, height)
# get candidate faces
candidate_faces = list()
for box in boxes:
im = img[:, :, box[1]:box[3], box[0]:box[2]]
im = torch.nn.functional.interpolate(im,
size=(48, 48),
mode='bilinear')
candidate_faces.append(im)
candidate_faces = torch.cat(candidate_faces, 0)
p_distribution, box_regs, landmarks = self.onet(candidate_faces)
# filter negative boxes
scores = p_distribution[:, 1]
mask = (scores >= threshold)
boxes = boxes[mask]
box_regs = box_regs[mask]
scores = scores[mask]
landmarks = landmarks[mask]
if boxes.shape[0] > 0:
# compute face landmark points
landmarks = self._calibrate_landmarks(boxes, landmarks)
landmarks = torch.stack([landmarks[:, :5], landmarks[:, 5:10]], 2)
boxes = self._calibrate_box(boxes, box_regs)
boxes = self._refine_boxes(boxes, width, height)
# nms
keep = func.nms(boxes.cpu().numpy(),
scores.cpu().numpy(),
nms_threshold,
device=self.device)
boxes = boxes[keep]
landmarks = landmarks[keep]
return boxes, landmarks
def stage_two(self, img, boxes, threshold, nms_threshold):
# no candidate face found.
if boxes.shape[0] == 0:
return boxes
width = img.shape[2]
height = img.shape[3]
boxes = self._convert_to_square(boxes)
boxes = self._refine_boxes(boxes, width, height)
# get candidate faces
candidate_faces = list()
for box in boxes:
im = img[:, :, box[1]:box[3], box[0]:box[2]]
im = torch.nn.functional.interpolate(im,
size=(24, 24),
mode='bilinear')
candidate_faces.append(im)
candidate_faces = torch.cat(candidate_faces, 0)
# rnet forward pass
p_distribution, box_regs, _ = self.rnet(candidate_faces)
# filter negative boxes
scores = p_distribution[:, 1]
mask = (scores >= threshold)
boxes = boxes[mask]
box_regs = box_regs[mask]
scores = scores[mask]
if boxes.shape[0] > 0:
boxes = self._calibrate_box(boxes, box_regs)
# nms
keep = func.nms(boxes.cpu().numpy(),
scores.cpu().numpy(),
nms_threshold,
device=self.device)
boxes = boxes[keep]
return boxes
def stage_three(self, imgs, boxes, threshold, nms_threshold):
# no candidate face found.
if boxes.shape[0] == 0:
return boxes, torch.empty(0, device=self.device, dtype=torch.int32)
width = imgs.shape[2]
height = imgs.shape[3]
labels = boxes[:, -1]
boxes = boxes[:, :4]
num_img = imgs.shape[0]
# get candidate faces
candidate_faces = list()
for box, label in zip(boxes, labels):
im = imgs[label, :, box[1]: box[3], box[0]: box[2]].unsqueeze(0)
im = torch.nn.functional.interpolate(
im, size=(48, 48), mode='bilinear')
candidate_faces.append(im)
candidate_faces = torch.cat(candidate_faces, 0)
p_distribution, box_regs, landmarks = self.onet(candidate_faces)
# filter negative boxes
scores = p_distribution[:, 1]
mask = (scores >= threshold)
boxes = boxes[mask]
box_regs = box_regs[mask]
scores = scores[mask]
landmarks = landmarks[mask]
labels =labels[mask]
if boxes.shape[0] != 0:
# compute face landmark points
landmarks = self._calibrate_landmarks(boxes, landmarks)
landmarks = torch.stack([landmarks[:, :5], landmarks[:, 5:10]], 2)
boxes = self._calibrate_box(boxes, box_regs)
boxes = self._refine_boxes(boxes, width, height)
final_boxes = torch.empty(0, dtype=torch.int32, device=self.device)
final_img_labels = torch.empty(0, dtype=torch.int32, device=self.device)
final_landmarks = torch.empty(0, dtype=torch.int32, device=self.device)
for i in range(num_img):
# nms
mask = labels == i
keep = func.nms(boxes[mask].cpu().numpy(),
scores[mask].cpu().numpy(), nms_threshold)
final_boxes = torch.cat([final_boxes, boxes[mask][keep]])
final_img_labels = torch.cat([final_img_labels, labels[mask][keep]])
# compute face landmark points
landm = landmarks [mask][keep]
final_landmarks = torch.cat([final_landmarks, landm])
return torch.cat([final_boxes, final_img_labels.unsqueeze(1 )], -1), final_landmarks
else:
return boxes, landmarks
def stage_one(self, imgs, threshold, factor, minsize, nms_threshold):
"""Stage one of mtcnn detection.
Args:
imgs (torch.FloatTensro): Output of "_preprocess" method.
threshold (float): The minimum probability of reserve bounding boxes.
factor (float): Image pyramid scaling ratio.
minsize (int): The minimum size of reserve bounding boxes.
nms_threshold (float): retain boxes that satisfy overlap <= thresh
Returns:
torch.IntTensor: All bounding boxes with image label output by stage one detection. [n, 5]
"""
width = imgs.shape[-2]
height = imgs.shape[-1]
num_img = imgs.shape[0]
# Compute valid scales
scales = []
cur_width = width
cur_height = height
cur_factor = 1
while cur_width >= 12 and cur_height >= 12:
if 12 / cur_factor >= minsize: # Ignore boxes that smaller than minsize
w = cur_width
h = cur_height
scales.append((w, h, cur_factor))
cur_factor *= factor
cur_width = math.ceil(cur_width * factor)
cur_height = math.ceil(cur_height * factor)
# Get candidate boxesi ph
candidate_boxes = torch.empty(0, dtype=torch.int32, device=self.device)
candidate_scores = torch.empty(0, device=self.device)
candidate_offsets = torch.empty(
0, dtype=torch.float32, device=self.device)
all_img_labels = torch.empty(0, dtype=torch.int32, device=self.device)
for w, h, f in scales:
resize_img = torch.nn.functional.interpolate(
imgs, size=(w, h), mode='bilinear')
p_distribution, box_regs, _ = self.pnet(resize_img)
candidate, scores, offsets, img_labels = self._generate_bboxes(
p_distribution, box_regs, f, threshold)
candidate_boxes = torch.cat([candidate_boxes, candidate])
candidate_scores = torch.cat([candidate_scores, scores])
candidate_offsets = torch.cat([candidate_offsets, offsets])
all_img_labels = torch.cat([all_img_labels, img_labels])
if candidate_boxes.shape[0] != 0:
candidate_boxes = self._calibrate_box(
candidate_boxes, candidate_offsets)
candidate_boxes = self._convert_to_square(candidate_boxes)
candidate_boxes = self._refine_boxes(
candidate_boxes, width, height)
final_boxes = torch.empty(0, dtype=torch.int32, device=self.device)
final_img_labels = torch.empty(0, dtype=torch.int32, device=self.device)
for i in range(num_img):
mask = all_img_labels == i
keep = func.nms(candidate_boxes[mask].cpu().numpy(),
candidate_scores[mask].cpu().numpy(), nms_threshold)
final_boxes = torch.cat([final_boxes, candidate_boxes[mask][keep]])
final_img_labels = torch.cat([final_img_labels, all_img_labels[mask][keep]])
return torch.cat([final_boxes, final_img_labels.unsqueeze(1 )], -1)
else:
return candidate_boxes
