class SessionTest:
def __init__(self, flush_pipe_on_read=False):
self.__thread = None
self.__flush_pipe_on_read = flush_pipe_on_read
self.__run_session_on_thread = False
self.__in_pipe = Pipe(self.__in_pipe_process)
self.__out_pipe = Pipe(self.__out_pipe_process)
def __in_pipe_process(self, inference):
a, b = inference.get_input()
inference.set_data([a*a, b*b])
return inference
def __out_pipe_process(self, result):
result, inference = result
inference.set_result(math.sqrt(result))
return inference
def get_in_pipe(self):
return self.__in_pipe
def get_out_pipe(self):
return self.__out_pipe
def use_threading(self, run_on_thread=True):
self.__run_session_on_thread = run_on_thread
def use_session_runner(self, session_runner):
self.__session_runner = session_runner
self.__tf_sess = session_runner.get_session()
self.__x = tf.placeholder(tf.int32, None)
self.__y = tf.placeholder(tf.int32, None)
self.__z = tf.add(self.__x, self.__y)
def run(self):
if self.__thread is None:
self.__thread = Thread(target=self.__run)
self.__thread.start()
def __run(self):
while self.__thread:
if self.__in_pipe.is_closed():
self.__out_pipe.close()
return
self.__in_pipe.pull_wait()
ret, inference = self.__in_pipe.pull(self.__flush_pipe_on_read)
if ret:
self.__session_runner.get_in_pipe().push(
SessionRunnable(self.__job, inference, run_on_thread=self.__run_session_on_thread))
def __job(self, inference):
self.__out_pipe.push(
(self.__tf_sess.run(self.__z,
feed_dict={self.__x: inference.get_data()[0], self.__y: inference.get_data()[1]}),
inference))
return render_template('index.html')
@app.route('/update', methods=['POST', 'GET'])
def controls():
global lower_hsv, upper_hsv
lower_hsv = list(map(int, [request.args.get('lh'), request.args.get('ls'), request.args.get('lv')]))
upper_hsv = list(map(int, [request.args.get('uh'), request.args.get('us'), request.args.get('uv')]))
print(lower_hsv, upper_hsv)
return request.query_string
cap = cv2.VideoCapture(0)
flask_movie.start(bind_ip='0.0.0.0', bind_port=5000)
while True:
_, frame = cap.read()
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
lower_red = np.array(lower_hsv)
upper_red = np.array(upper_hsv)
mask = cv2.inRange(hsv, lower_red, upper_red)
res = cv2.bitwise_and(frame, frame, mask=mask)
color_pipe.push(frame)
mask_pipe.push(mask)
filter_pipe.push(res)
cv2.waitKey(1)
class FNEmbeddingsGenerator:
class Inference(Inference):
def __init__(self, input, return_pipe=None, meta_dict=None):
super().__init__(input, return_pipe, meta_dict)
def __init__(self,
model_name=PRETRAINED_20180408_102900,
graph_prefix=None,
flush_pipe_on_read=False,
face_resize=160):
facenet.load_model(facenet_path.get(model_name))
self.__flush_pipe_on_read = flush_pipe_on_read
self.__thread = None
self.__in_pipe = Pipe(self.__in_pipe_process)
self.__out_pipe = Pipe(self.__out_pipe_process)
self.__run_session_on_thread = False
self.__face_resize = face_resize
if not graph_prefix:
self.__graph_prefix = ''
else:
self.__graph_prefix = graph_prefix + '/'
def __in_pipe_process(self, inference):
resized = inference.get_input()
prewhitened = facenet.prewhiten(resized)
reshaped = prewhitened.reshape(-1, self.__face_resize,
self.__face_resize, 3)
# print(reshaped.shape)
inference.set_data(reshaped)
return inference
def __out_pipe_process(self, result):
result, inference = result
inference.set_result(result)
if inference.get_return_pipe():
return '\0'
return inference
def get_in_pipe(self):
return self.__in_pipe
def get_out_pipe(self):
return self.__out_pipe
def use_threading(self, run_on_thread=True):
self.__run_session_on_thread = run_on_thread
def use_session_runner(self, session_runner):
self.__session_runner = session_runner
self.__tf_sess = session_runner.get_session()
self.__images_placeholder = self.__tf_sess.graph.get_tensor_by_name(
self.__graph_prefix + "input:0")
self.__embeddings = self.__tf_sess.graph.get_tensor_by_name(
self.__graph_prefix + "embeddings:0")
self.__phase_train_placeholder = self.__tf_sess.graph.get_tensor_by_name(
self.__graph_prefix + "phase_train:0")
self.__embedding_size = self.__embeddings.get_shape()[1]
def run(self):
if self.__thread is None:
self.__thread = Thread(target=self.__run)
self.__thread.start()
def __run(self):
while self.__thread:
if self.__in_pipe.is_closed():
self.__out_pipe.close()
return
self.__in_pipe.pull_wait()
ret, inference = self.__in_pipe.pull(self.__flush_pipe_on_read)
if ret:
self.__session_runner.get_in_pipe().push(
SessionRunnable(
self.__job,
inference,
run_on_thread=self.__run_session_on_thread))
def __job(self, inference):
self.__out_pipe.push(
(self.__tf_sess.run(self.__embeddings,
feed_dict={
self.__images_placeholder:
inference.get_data(),
self.__phase_train_placeholder: False
}), inference))
def stop(self):
self.__thread = None
def save_for_serving(self, save_path, model_version):
path = os.path.join(save_path, str(model_version))
builder = tf.saved_model.builder.SavedModelBuilder(path)
prediction_signature = tf.saved_model.signature_def_utils.build_signature_def(
inputs={
'images':
tf.saved_model.utils.build_tensor_info(
self.__images_placeholder),
'phase':
tf.saved_model.utils.build_tensor_info(
self.__phase_train_placeholder)
},
outputs={
'embeddings':
tf.saved_model.utils.build_tensor_info(self.__embeddings)
},
method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME)
legacy_init_op = tf.group(tf.tables_initializer(),
name='legacy_init_op')
builder.add_meta_graph_and_variables(
self.__tf_sess, [tf.saved_model.tag_constants.SERVING],
signature_def_map={
'calculate_embeddings': prediction_signature,
})
builder.save()
print("model saved successfullt")
class EmbeddingGenerator:
class Inference(Inference):
def __init__(self, input, return_pipe=None, meta_dict=None):
super().__init__(input, return_pipe, meta_dict)
def __init__(self, face_size=160):
self.generator = FNEmbeddingsGenerator(face_resize=face_size)
self.generator.use_threading()
self.generator_ip = self.generator.get_in_pipe()
self.generator_op = self.generator.get_out_pipe()
self.detector = FaceDetectorMTCNN(face_resize = face_size)
self.detector.use_threading()
self.detector_ip = self.detector.get_in_pipe()
self.detector_op = self.detector.get_out_pipe()
self.__thread = None
self.__in_pipe = Pipe(self.__in_pipe_process)
self.__out_pipe = Pipe(self.__out_pipe_process)
self.__run_session_on_thread = False
self.__face_size=face_size
def __in_pipe_process(self, inference):
return inference
def __out_pipe_process(self, result):
result, inference = result
inference.set_result(result)
if inference.get_return_pipe():
return '\0'
return inference
def get_in_pipe(self):
return self.__in_pipe
def get_out_pipe(self):
return self.__out_pipe
def use_session_runner(self, session_runner):
self.session_runner = session_runner
self.generator.use_session_runner(session_runner)
self.detector.use_session_runner(session_runner)
def step_1(self):
while self.__thread:
self.detector_ip.push_wait()
self.__in_pipe.pull_wait()
ret, inf = self.__in_pipe.pull()
if not ret:
continue
image = inf.get_input()
image = imutils.resize(image, width=None)
inference = FaceDetectorMTCNN.Inference(image)
inference.set_meta('EmbeddingGenerator.Inference', inf)
self.detector_ip.push(inference)
self.detector.stop()
def step_2(self):
while self.__thread:
self.generator_ip.push_wait()
self.detector_op.pull_wait()
ret, inference = self.detector_op.pull(True)
if ret:
faces = inference.get_result()
# print("faces: ", faces)
inf = inference.get_meta('EmbeddingGenerator.Inference')
inf.set_meta('bbox', list())
if faces:
face_imgs = np.empty((len(faces), self.__face_size, self.__face_size, 3))
for i in range(len(faces)):
inf.get_meta('bbox').append(faces[i]['rect'])
face_imgs[i,:,:,:] = faces[i]['face']
inf.set_meta('face_image', face_imgs[0])
inference = FNEmbeddingsGenerator.Inference(input=face_imgs)
inference.set_meta('EmbeddingGenerator.Inference', inf)
self.generator_ip.push(inference)
else:
self.__out_pipe.push((None, inf))
self.detector.stop()
self.generator.stop()
def step_3(self):
while self.__thread:
self.generator_op.pull_wait()
ret, inference = self.generator_op.pull(True)
embedding = None
if ret:
embedding = inference.get_result()
inference = inference.get_meta('EmbeddingGenerator.Inference')
# print("shape emb",embedding.shape)
self.__out_pipe.push((embedding, inference))
self.generator.stop()
def __run(self):
self.session_runner.start()
self.generator.run()
self.detector.run()
Thread(target=self.step_1).start()
Thread(target=self.step_2).start()
Thread(target=self.step_3).start()
def run(self):
self.__thread = Thread(target=self.__run)
self.__thread.start()
def stop(self):
self.__thread = None
class FaceDetectorMTCNN():
class Inference(Inference):
def __init__(self, input, return_pipe=None, meta_dict=None):
super().__init__(input, return_pipe, meta_dict)
def __init__(self,
graph_prefix=None,
flush_pipe_on_read=False,
face_prob_score=0.835,
face_resize=160):
self.__face_prob_score = face_prob_score
self.__flush_pipe_on_read = flush_pipe_on_read
self.__thread = None
self.__in_pipe = Pipe(self.__in_pipe_process)
self.__out_pipe = Pipe(self.__out_pipe_process)
self.__face_resize = face_resize
self.__run_session_on_thread = False
if not graph_prefix:
self.__graph_prefix = ''
else:
self.__graph_prefix = graph_prefix + '/'
def __in_pipe_process(self, inference):
img = inference.get_input()
inference.set_data(img)
return inference
def __out_pipe_process(self, result):
result, inference = result
inference.set_result(result)
if inference.get_return_pipe():
return '\0'
return inference
def get_in_pipe(self):
return self.__in_pipe
def get_out_pipe(self):
return self.__out_pipe
def use_threading(self, run_on_thread=True):
self.__run_session_on_thread = run_on_thread
def use_session_runner(self, session_runner):
self.__session_runner = session_runner
self.__tf_sess = session_runner.get_session()
self.__pnet, self.__rnet, self.__onet = detect_face.create_mtcnn(
self.__tf_sess, align_path.get())
def run(self):
if self.__thread is None:
self.__thread = Thread(target=self.__run)
self.__thread.start()
def __run(self):
while self.__thread:
if self.__in_pipe.is_closed():
self.__out_pipe.close()
return
self.__in_pipe.pull_wait()
ret, inference = self.__in_pipe.pull(self.__flush_pipe_on_read)
if ret:
self.__job(inference)
def __job(self, inference):
img = inference.get_data()
faces = []
img_size = np.asarray(img.shape)[0:2]
bounding_boxes, _ = detect_face.detect_face(img, minsize, self.__pnet,
self.__rnet, self.__onet,
threshold, factor)
if not len(bounding_boxes) == 0:
for face in bounding_boxes:
if face[4] > self.__face_prob_score:
det = np.squeeze(face[0:4])
bb = np.zeros(4, dtype=np.int32)
bb[0] = np.maximum(det[0] - margin / 2, 0)
bb[1] = np.maximum(det[1] - margin / 2, 0)
bb[2] = np.minimum(det[2] + margin / 2, img_size[1])
bb[3] = np.minimum(det[3] + margin / 2, img_size[0])
cropped = img[bb[1]:bb[3], bb[0]:bb[2], :]
resized = cv2.resize(
cropped, (self.__face_resize, self.__face_resize),
interpolation=cv2.INTER_CUBIC)
faces.append({
'face': resized,
'rect': [bb[0], bb[1], bb[2], bb[3]]
})
# print("len of faces", len(faces))
self.__out_pipe.push((faces, inference))
def stop(self):
self.__thread = None
def save_for_serving(self, save_path, model_version):
path = os.path.join(save_path, str(model_version))
builder = tf.saved_model.builder.SavedModelBuilder(path)
prediction_signature = tf.saved_model.signature_def_utils.build_signature_def(
inputs={
'images':
tf.saved_model.utils.build_tensor_info(
self.__images_placeholder),
'phase':
tf.saved_model.utils.build_tensor_info(
self.__phase_train_placeholder)
},
outputs={
'embeddings':
tf.saved_model.utils.build_tensor_info(self.__embeddings)
},
method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME)
legacy_init_op = tf.group(tf.tables_initializer(),
name='legacy_init_op')
builder.add_meta_graph_and_variables(
self.__tf_sess, [tf.saved_model.tag_constants.SERVING],
signature_def_map={
'calculate_embeddings': prediction_signature,
})
builder.save()
print("model saved successfullt")
def controls():
global upper_cutoff, lower_cutoff
uc = request.args.get('upper_cutoff')
if uc:
upper_cutoff = int(uc)
lc = request.args.get('lower_cutoff')
if lc:
lower_cutoff = int(lc)
return request.query_string
fmovie.start(bind_ip='0.0.0.0', bind_port=5000)
k = RealSenseCamera(serial_no='834412071881')
k.start()
while True:
image_dict = k.get_feed()[1]
color_image = image_dict['color_image']
depth_image = image_dict['depth_image']
HMV = HeatMapVisualizer(upper_cutoff=upper_cutoff, lower_cutoff=lower_cutoff)
depth_map = HMV.get_image_map(depth_image)
depth_pipe.push(depth_map)
color_pipe.push(color_image)
# cv2.imwrite('images/depth_image'+str(img_number).zfill(5)+'.png', depth_map)
# cv2.imwrite('images/color_image'+str(img_number).zfill(5)+'.png', color_image)
# img_number += 1
class RealSenseCamera(object):
def __init__(self, serial_no, fps=30, hxw=(640, 480)):
self.pipe = Pipe(limit=1)
self.serial_no = serial_no
self.pipeline = rs.pipeline()
self.config = rs.config()
self.fps = fps
self.hxw = hxw
self.obj = None
def __start(self):
# initialise camera
print('ser = ', self.serial_no)
self.config.enable_device(self.serial_no)
self.config.enable_stream(rs.stream.depth, self.hxw[0], self.hxw[1], rs.format.z16, self.fps)
self.config.enable_stream(rs.stream.color, self.hxw[0], self.hxw[1], rs.format.bgr8, self.fps)
self.pipeline.start(self.config)
align_to = rs.stream.color
align = rs.align(align_to)
try:
while True:
# Wait for a coherent pair of frames: depth and color
frames = self.pipeline.wait_for_frames()
aligned_frames = align.process(frames)
depth_frame = aligned_frames.get_depth_frame().as_depth_frame()
color_frame = frames.get_color_frame()
# Validate that both frames are valid
if not depth_frame or not color_frame:
continue
# Convert images to numpy arrays
depth_image = np.asanyarray(depth_frame.get_data())
color_image = np.asanyarray(color_frame.get_data())
self.pipe.push_wait()
self.pipe.push({"color_image": color_image, "depth_image": depth_image})
finally:
# Stop streaming
self.pipeline.stop()
def start(self):
Thread(target=self.__start).start()
def get_feed(self):
self.pipe.pull_wait()
feed = self.pipe.pull()
return feed
def stop_feed(self):
self.pipeline.stop()
def skip_Nframes(self, count):
counter = 0
while counter < count:
feed = self.get_feed()
counter += 1
class TFObjectDetector:
class Inference(Inference):
def __init__(self, input, label_map, return_pipe=None, meta_dict=None):
super().__init__(input, return_pipe, meta_dict)
self.__label_map = label_map
def get_label_map(self):
return self.__label_map
@staticmethod
def __get_dir_path():
return dirname(realpath(__file__))
@staticmethod
def __download_model(model_path, download_base, model_file):
print("downloading model...", model_path)
try:
os.mkdir(model_path)
except:
pass
opener = urllib.request.URLopener()
opener.retrieve(download_base + model_file, model_path + model_file)
print("finished downloading. extracting...")
tar_file = tarfile.open(model_path + model_file)
for file in tar_file.getmembers():
file_name = os.path.basename(file.name)
if 'frozen_inference_graph.pb' in file_name:
tar_file.extract(file, model_path)
print("finished extracting.")
@staticmethod
def __fetch_model_path(model_name):
dir_path = pretrained_path.get()
model_path = dir_path + '/'
model_file = model_name + '.tar.gz'
download_base = 'http://download.tensorflow.org/models/object_detection/'
path_to_frozen_graph = model_path + model_name + '/frozen_inference_graph.pb'
if not path.exists(path_to_frozen_graph):
TFObjectDetector.__download_model(model_path, download_base,
model_file)
return path_to_frozen_graph
@staticmethod
def __fetch_category_indices(label_map):
label_map += '.pbtxt'
path_to_labels = os.path.join(data_path.get(), label_map)
class_count = 90
label_map = label_map_util.load_labelmap(path_to_labels)
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes=class_count, use_display_name=True)
category_index = label_map_util.create_category_index(categories)
category_dict = {}
for item in category_index.values():
category_dict[item['id']] = item['name']
category_dict[item['name']] = item['id']
return category_index, category_dict
@staticmethod
def upload_label_map(label_map_path, force=False):
path_to_labels = os.path.join(data_path.get(), label_map_path)
if path.exists(path_to_labels):
if not force:
raise Exception(
"label map exists, skipping. use force=True to overwrite.")
copy2(data_path.get(), label_map_path)
def __init__(self,
model_name=PRETRAINED_ssd_mobilenet_v1_coco_2017_11_17,
label_map='mscoco_label_map',
image_shape=None,
graph_prefix=None,
flush_pipe_on_read=False):
self.__category_index, self.__category_dict = self.__fetch_category_indices(
label_map)
self.__path_to_frozen_graph = self.__fetch_model_path(model_name)
self.__flush_pipe_on_read = flush_pipe_on_read
self.__image_shape = image_shape
self.__thread = None
self.__in_pipe = Pipe(self.__in_pipe_process)
self.__out_pipe = Pipe(self.__out_pipe_process)
self.__run_session_on_thread = False
if not graph_prefix:
self.__graph_prefix = ''
else:
self.__graph_prefix = graph_prefix + '/'
def __in_pipe_process(self, inference):
images = inference.get_input()
images = np.array(images)
if len(images.shape) == 3:
data = np.expand_dims(images, axis=0)
inference.set_meta('expand_dims', True)
else:
data = images
inference.set_meta('expand_dims', False)
inference.set_data(data)
return inference
def __out_pipe_process(self, result):
result, inference = result
if inference.get_meta('expand_dims'):
num_detections = int(result['num_detections'][0])
detection_classes = result['detection_classes'][
0][:num_detections].astype(np.uint8)
detection_boxes = result['detection_boxes'][0][:num_detections]
detection_scores = result['detection_scores'][0][:num_detections]
if 'detection_masks' in result:
detection_masks = result['detection_masks'][0][:num_detections]
else:
detection_masks = None
results = InferedDetections(inference.get_input(),
inference.get_label_map(),
num_detections,
detection_boxes,
detection_classes,
detection_scores,
masks=detection_masks,
is_normalized=True,
get_category_fnc=self.get_category,
annotator=self.annotate)
else:
results = []
for i in range(len(result['num_detections'])):
num_detections = int(result['num_detections'][i])
detection_classes = result['detection_classes'][
i][:num_detections].astype(np.uint8)
detection_boxes = result['detection_boxes'][i][:num_detections]
detection_scores = result['detection_scores'][
i][:num_detections]
if 'detection_masks' in result:
detection_masks = result['detection_masks'][
i][:num_detections]
else:
detection_masks = None
results.append(
InferedDetections(inference.get_input()[i],
inference.get_label_map(),
num_detections,
detection_boxes,
detection_classes,
detection_scores,
masks=detection_masks,
is_normalized=True,
get_category_fnc=self.get_category,
annotator=self.annotate))
inference.set_result(results)
if inference.get_return_pipe():
return '\0'
return inference
def get_in_pipe(self):
return self.__in_pipe
def get_out_pipe(self):
return self.__out_pipe
def use_threading(self, run_on_thread=True):
self.__run_session_on_thread = run_on_thread
def use_session_runner(self, session_runner):
self.__session_runner = session_runner
self.__tf_sess = session_runner.get_session()
with self.__tf_sess.graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(self.__path_to_frozen_graph, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name=self.__graph_prefix)
tf_default_graph = self.__tf_sess.graph
self.__image_tensor = tf_default_graph.get_tensor_by_name(
self.__graph_prefix + 'image_tensor:0')
tensor_names = {
output.name
for op in tf_default_graph.get_operations()
for output in op.outputs
}
self.__tensor_dict = {}
for key in [
'num_detections', 'detection_boxes', 'detection_classes',
'detection_scores', 'detection_masks'
]:
tensor_name = self.__graph_prefix + key + ':0'
if tensor_name in tensor_names:
self.__tensor_dict[key] = tf_default_graph.get_tensor_by_name(
tensor_name)
if 'detection_masks' in self.__tensor_dict:
# The following processing is only for single image
detection_boxes = tf.squeeze(self.__tensor_dict['detection_boxes'],
[0])
detection_masks = tf.squeeze(self.__tensor_dict['detection_masks'],
[0])
# Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.
real_num_detection = tf.cast(
self.__tensor_dict['num_detections'][0], tf.int32)
detection_boxes = tf.slice(detection_boxes, [0, 0],
[real_num_detection, -1])
detection_masks = tf.slice(detection_masks, [0, 0, 0],
[real_num_detection, -1, -1])
detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
detection_masks, detection_boxes, self.__image_shape[0],
self.__image_shape[1])
detection_masks_reframed = tf.cast(
tf.greater(detection_masks_reframed, 0.5), tf.uint8)
# Follow the convention by adding back the batch dimension
self.__tensor_dict['detection_masks'] = tf.expand_dims(
detection_masks_reframed, 0)
def run(self):
if self.__thread is None:
self.__thread = Thread(target=self.__run)
self.__thread.start()
def __run(self):
while self.__thread:
if self.__in_pipe.is_closed():
self.__out_pipe.close()
return
self.__in_pipe.pull_wait()
ret, inference = self.__in_pipe.pull(self.__flush_pipe_on_read)
if ret:
self.__session_runner.get_in_pipe().push(
SessionRunnable(
self.__job,
inference,
run_on_thread=self.__run_session_on_thread))
def __job(self, inference):
self.__out_pipe.push((self.__tf_sess.run(
self.__tensor_dict,
feed_dict={self.__image_tensor: inference.get_data()}), inference))
def get_category(self, category):
return self.__category_dict[category]
@staticmethod
def annotate(inferred_detections):
annotated = inferred_detections.image.copy()
vis_util.visualize_boxes_and_labels_on_image_array(
annotated,
inferred_detections.get_boxes_tlbr(),
inferred_detections.get_classes().astype(np.int32),
inferred_detections.get_scores(),
TFObjectDetector.__fetch_category_indices(
inferred_detections.get_label_map())[0],
instance_masks=inferred_detections.get_masks(),
use_normalized_coordinates=True,
line_thickness=1)
return annotated