def detect_controller(self, req):
cv_image = self.bridge.imgmsg_to_cv2(req.input_image, "bgr8")
image = cv2.resize(cv_image, (0, 0), fx=config.scale, fy=config.scale)
positions = face_api.detect_faces(image,
min_score=config.detection_score,
max_idx=config.detection_idx)
encodings = face_api.face_descriptor(image, positions)
image_h, image_w = self.image_shape
boxes = []
for face_position, encoding in zip(self.face_positions, encodings):
face = face_api.Face(face_position[0],
tracker_timeout=config.tracker_timeout)
predicted_id = classifier.predict(encoding)
if predicted_id != 0:
face.details = face_map[predicted_id]
# try to find gender.
if face.details["gender"] == "unknown":
face.details["gender"] = face_api.predict_gender(encoding)
else:
face.details["gender"] = face_api.predict_gender(encoding)
face_map[face.details["id"]] = face.details
if face_map[
face.details["id"]]["size"] < config.classification_size:
face_map[face.details["id"]]["size"] += 1
classifier.add_pair(encoding, face.details["id"])
face_path = os.path.join(_face_dir, face.details["id"])
if not os.path.exists(face_path):
os.mkdir(face_path)
with open(
os.path.join(face_path,
"{}.dump".format(int(time.time()))),
'wb') as fp:
pickle.dump(encoding, fp)
box = Box()
box.x = face.rect.left() / float(image_w)
box.y = face.rect.top() / float(image_h)
box.w = face.rect.width() / float(image_w)
box.h = face.rect.height() / float(image_h)
box.gender = face.details["gender"]
box.label = face.details["id"]
box.name = face.details["name"]
box.age = face.details["age"]
boxes.append(box)
rospy.loginfo("{} faces loaded.".format(box.name))
response = DetectResponse(boxes)
return response
def analysis_show(path, img, cover):
global face_information #申明使用全局变量
global name
if cover:
face_information = detect_faces(path)
try:
if face_information['faces']:
name = search_show(face_information)
return name
except KeyError:
print('no face')
else:
try:
if face_information['faces']:
# x = face_information['faces'][0]['face_rectangle']['left']
# y = face_information['faces'][0]['face_rectangle']['top']
age = face_information['faces'][0]['attributes']['age'][
'value']
emotion = face_information['faces'][0]['attributes']['emotion']
emotion = emotion.copy() #操作复制的一份防止重复pop
emotion1 = max(emotion, key=emotion.get)
emotion.pop(emotion1)
emotion2 = max(emotion, key=emotion.get)
beauty = face_information['faces'][0]['attributes']['beauty']
lock.acquire()
show_info(name, img, age, emotion1, emotion2, beauty)
lock.release()
else:
lock.acquire()
cv2.putText(img, "no face", (100, 50), font, 1.2,
(255, 255, 255),
1) # 照片/添加的文字/左上角坐标/字体/字体大小/颜色/字体粗细
lock.release()
except KeyError:
cv2.putText(img, "no face", (100, 50), font, 1.2, (255, 255, 255),
1)
def process(self, data):
try:
cv_image = self.bridge.imgmsg_to_cv2(data, "bgr8")
image = cv2.resize(cv_image, (0, 0),
fx=config.scale,
fy=config.scale)
self.image_shape = image.shape[:2]
original = image.copy()
if self.frame_limit % config.skip_frames == 0:
self.frame_limit = 0
# Detecting face positions
self.face_positions = face_api.detect_faces(
image,
min_score=config.detection_score,
max_idx=config.detection_idx)
self.frame_limit += 1
if len(self.face_positions) > len(self.detected_faces):
# Make the list empty
self.detected_faces = []
# Compute the 128D vector that describes the face in img identified by
# shape.
encodings = face_api.face_descriptor(image,
self.face_positions)
for face_position, encoding in zip(self.face_positions,
encodings):
# Create object from face_position.
face = face_api.Face(
face_position[0],
tracker_timeout=config.tracker_timeout)
predicted_id = classifier.predict(encoding)
if predicted_id != 0:
face.details = face_map[predicted_id]
# try to find gender.
if face.details["gender"] == "unknown":
face.details["gender"] = face_api.predict_gender(
encoding)
else:
face.details["gender"] = face_api.predict_gender(
encoding)
face_map[face.details["id"]] = face.details
if face_map[face.details["id"]][
"size"] < config.classification_size:
face_map[face.details["id"]]["size"] += 1
classifier.add_pair(encoding, face.details["id"])
face_path = os.path.join(_face_dir, face.details["id"])
if not os.path.exists(face_path):
os.mkdir(face_path)
with open(
os.path.join(
face_path,
"{}.dump".format(int(time.time()))),
'wb') as fp:
pickle.dump(encoding, fp)
# Start correlation tracker for face.
face.tracker.start_track(image, face_position[0])
# Face detection score, The score is bigger for more confident
# detections.
rospy.loginfo(
"Face {}->{:5} , [{}] [score : {:.2f}]".format(
face.details["id"], face.details["name"],
face.details["gender"], face_position[1]))
self.detected_faces.append(face)
else:
for index, face in enumerate(self.detected_faces):
# Update tracker , if quality is low ,
# face will be removed from list.
if not face.update_tracker(image):
self.detected_faces.pop(index)
face.details = face_map[str(face.details["id"])]
face.draw_face(original)
if config.show_window:
cv2.imshow("image", original)
key = cv2.waitKey(1) & 0xFF
if key == ord('q'):
cv2.destroyAllWindows()
rospy.signal_shutdown("q key pressed")
except CvBridgeError as e:
rospy.logerr(e)
def synchronisedImageCallback(self, imageRGB, imageDepth):
try:
# rospy.rostime.wallsleep(0.1)
self.time = rospy.get_rostime()
cv_image = self.bridge.imgmsg_to_cv2(imageRGB, "bgr8")
image = cv2.resize(cv_image, (0, 0),
fx=config.scale,
fy=config.scale)
self.image_shape = image.shape[:2]
image_h, image_w = self.image_shape
original = image.copy()
if self.frame_limit % config.skip_frames == 0:
self.frame_limit = 0
# Detecting face positions
self.face_positions = face_api.detect_faces(
image,
min_score=config.detection_score,
max_idx=config.detection_idx)
self.frame_limit += 1
if len(self.face_positions) > len(self.detected_faces):
# Make the list empty
self.detected_faces = []
# Compute the 128D vector that describes the face in img identified by
# shape.
encodings = face_api.face_descriptor(image,
self.face_positions)
cpt = 0
for face_position, encoding in zip(self.face_positions,
encodings):
# Create object from face_position.
face = face_api.Face(
face_position[0],
tracker_timeout=config.tracker_timeout)
predicted_id = classifier.predict(encoding)
if predicted_id != 0:
try:
face.details = face_map[predicted_id]
except:
print "an key error has ocurred (line ~121)"
return
# try to find gender.
if face.details["gender"] == "unknown":
face.details["gender"] = face_api.predict_gender(
encoding)
else:
face.details["gender"] = face_api.predict_gender(
encoding)
face_map[face.details["id"]] = face.details
if face_map[face.details["id"]][
"size"] < config.classification_size:
face_map[face.details["id"]]["size"] += 1
classifier.add_pair(encoding, face.details["id"])
face_path = os.path.join(_face_dir, face.details["id"])
if not os.path.exists(face_path):
os.mkdir(face_path)
with open(
os.path.join(
face_path,
"{}.dump".format(int(time.time()))),
'wb') as fp:
pickle.dump(encoding, fp)
# Start correlation tracker for face.
face.tracker.start_track(image, face_position[0])
# Face detection score, The score is bigger for more confident
# detections.
rospy.loginfo(
"Face {}->{:5} , [{}] [score : {:.2f}], [xmin: {} xmax: {} ymin: {} ymax: {}]"
.format(
face.details["id"], face.details["name"],
face.details["gender"], face_position[1],
face.rect.left() / config.scale,
face.rect.width() / config.scale +
face.rect.left() / config.scale,
face.rect.top() / config.scale,
face.rect.height() / config.scale +
face.rect.top() / config.scale))
face.details["score"] = face_position[1]
rospy.loginfo("SCORE {}".format(face.details["score"]))
cpt = cpt + 1
self.detected_faces.append(face)
else:
cpt = 0
for index, face in enumerate(self.detected_faces):
# Update tracker , if quality is low ,
# face will be removed from list.
if not face.update_tracker(image):
self.detected_faces.pop(index)
face.details = face_map[str(face.details["id"])]
face.draw_face(original)
cpt = cpt + 1
if cpt > 0:
listBB2D = BoundingBoxes2D()
for index, face in enumerate(self.detected_faces):
msgFace = FaceMsg()
msgBB = BoundingBox2D()
msgBB.xmin = face.rect.left() / config.scale
msgBB.xmax = face.rect.width(
) / config.scale + face.rect.left() / config.scale
msgBB.ymin = face.rect.top() / config.scale
msgBB.ymax = face.rect.height(
) / config.scale + face.rect.top() / config.scale
msgBB.Class = "face"
msgFace.gender = face.details["gender"]
msgFace.id = face.details["id"]
msgFace.name = face.details["name"]
if face.details["age"] >= 0:
msgFace.age = face.details["age"]
else:
msgFace.age = 0
msgFace.genderProbability = abs(face.details["score"] /
2.7)
msgBB.probability = msgFace.genderProbability
#msgFace.boundingBoxe = msgBB
listBB2D.boundingBoxes.append(msgBB)
listBB2D.header.stamp = imageDepth.header.stamp
self.msg.faces.append(msgFace)
try:
rospy.wait_for_service("/get_3d_bounding_boxes", 0.01)
serv = rospy.ServiceProxy("/get_3d_bounding_boxes",
GetBoundingBoxes3D)
resp = serv(listBB2D, imageDepth,
imageDepth.header.frame_id, config.output_fame)
for index, BB3D in enumerate(
resp.boundingBoxes3D.boundingBoxes):
self.msg.faces[index].boundingBox = BB3D
self.msg.header.stamp = imageDepth.header.stamp
self.msg.header.frame_id = config.output_fame
self.faces_pub.publish(self.msg)
#self.rgb_pub.publish(data)
#self.depth_pub.publish(Depth)
self.msg.faces = []
self.BB3Dfaces = []
except Exception as e:
rospy.logerr("ERROR with frame to box: " + str(e))
if config.show_window:
cv2.imshow("image", original)
key = cv2.waitKey(1) & 0xFF
if key == ord('q'):
cv2.destroyAllWindows()
rospy.signal_shutdown("q key pressed")
except CvBridgeError as e:
rospy.logerr(e)
self.flagClassificationInProgress = False