from src.utils import load_tf_ssd_detection_graph,run_inference_for_single_image_through_ssd,post_process_ssd_predictions\
,load_tf_facenet_graph,crop_ssd_prediction,prewhiten,get_face_embeddings,print_recognition_output,draw_detection_box
import configparser
config = configparser.ConfigParser()
config.read('config.ini')
# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_PERSON_DETECTION = config.get("DEFAULT","PATH_TO_PERSON_DETECTION")
if __name__ == "__main__":
with tf.Graph().as_default():
### Creating and Loading the Single Shot Detector ###
image_tensor, tensor_dict = load_tf_ssd_detection_graph(PATH_TO_PERSON_DETECTION, input_graph=None)
sess = tf.Session()
with sess.as_default():
cap = cv2.VideoCapture(0)
if cap.isOpened() is False:
print("Error opening video stream or file")
while cap.isOpened():
_, image = cap.read()
image = image[..., ::-1, :]
image_display = image.copy()
initial_inference_start_time = time.time()
# Both the SSD and Facenet also uses np.uint8 and RGB images for both!
xmin = boxes[1]
ymax = boxes[2]
xmax = boxes[3]
conf_score = cur_det[4]
cv2.rectangle(
image_np, (int(xmin), int(ymin)),
(int(xmax), int(ymax)), (255, 0, 0), 3
) #This is still RGB here,that's why the first element is Red
image_np_bgr = image_np[..., ::-1]
cv2.imshow('face-detection-ssd', image_np_bgr)
print("Press Escape to go to next image")
if cv2.waitKey(1) == 27:
break
cv2.destroyAllWindows()
return
if __name__ == "__main__":
PATH_TO_FROZEN_GRAPH = './model/ssd_models/ssd_mobilenet_v1_focal_loss_face_mark_2.pb'
PATH_TO_IMAGES_DIR = './images/'
main_graph = tf.Graph()
image_tensor, tensor_dict = load_tf_ssd_detection_graph(
PATH_TO_FROZEN_GRAPH, input_graph=main_graph)
run_tf_object_detection_images(main_graph,
image_tensor,
tensor_dict,
path_to_images_dir=PATH_TO_IMAGES_DIR)
]
FINAL_DETECTION_PATH = config.get("DEFAULT",
"PATH_TO_FINAL_DETECTION_DIRECTORY")
FACENET_MODEL_PATH = config.get("DEFAULT", "PATH_TO_FACENET_MODEL")
CLASSIFIER_PATH = config.get("DEFAULT", "PATH_TO_SVM_EMBEDDINGS_CLASSIFIER")
CROP_SSD_PERCENTAGE = float(config.get("DEFAULT", "CROP_SSD_PERCENTAGE"))
IMAGE_SIZE = int(config.get("DEFAULT", "IMAGE_SIZE"))
FACENET_PREDICTION_BATCH_SIZE = int(
config.get("DEFAULT", "FACENET_PREDICTION_BATCH_SIZE"))
if __name__ == "__main__":
with tf.Graph().as_default():
image_tensor, tensor_dict = load_tf_ssd_detection_graph(
PATH_TO_CKPT, input_graph=None)
sess = tf.Session()
with sess.as_default():
### Creating and Loading MTCNN ###
pnet, rnet, onet = create_mtcnn(sess, None)
### Creating and Loading the Facenet Graph ###
images_placeholder, embeddings, phase_train_placeholder = load_tf_facenet_graph(
FACENET_MODEL_PATH)
for image_id, SOURCE_IM_PATH in enumerate(SOURCE_IM_PATH_ARRAY):
initial_inference_start_time = time.time()
image = cv2.imread(SOURCE_IM_PATH)
image_np = (cv2.cvtColor(image, cv2.COLOR_BGR2RGB)).astype(
xmin = boxes[1]
ymax = boxes[2]
xmax = boxes[3]
conf_score = cur_det[4]
cv2.rectangle(
image_np, (int(xmin), int(ymin)),
(int(xmax), int(ymax)), (255, 0, 0), 3
) #This is still RGB here,that's why the first element is Red
image_np_bgr = image_np[..., ::-1]
cv2.imshow('face-detection-ssd', image_np_bgr)
if cv2.waitKey(1) == 27:
break
cap.release()
cv2.destroyAllWindows()
return
if __name__ == "__main__":
PATH_TO_FACE_DETECTION = config.get("DEFAULT", "PATH_TO_FACE_DETECTION")
# PATH_TO_VIDEO = '/Users/petertanugraha/Projects/tf-pose-estimation/test_video/uoft_lecture_trimmed.mp4'
main_graph = tf.Graph()
image_tensor, tensor_dict = load_tf_ssd_detection_graph(
PATH_TO_FACE_DETECTION, input_graph=main_graph)
run_tf_object_detection_video(main_graph,
image_tensor,
tensor_dict,
path_to_video=None)
color_pallete = (int(color_pallete[0]), int(color_pallete[1]),
int(color_pallete[2]))
else:
color_pallete = (0, 0, 255)
self.color_pallete = color_pallete
if __name__ == "__main__":
# Global variable that will hold all the "Recognized Human in the whole lifetime of the current recording session"
Identified_Human_List = []
with tf.Graph().as_default():
### Loading Face Detector ###
Face_Detection_Graph = tf.Graph()
image_tensor, tensor_dict = load_tf_ssd_detection_graph(
PATH_TO_FACE_DETECTION, input_graph=Face_Detection_Graph)
face_detection_sess = tf.Session(graph=Face_Detection_Graph)
### Loading Person Detector ###
person_image_tensor, person_tensor_dict = load_tf_ssd_detection_graph(
PATH_TO_PERSON_DETECTION, input_graph=None)
main_sess = tf.Session()
### Loading the SVM Classifier for Face ID classification ###
with open(CLASSIFIER_PATH_SVM, 'rb') as infile:
(model, class_names) = pickle.load(infile)
### Loading the KNN Classifier for Face Recognition Classifier ###
with open(CLASSIFIER_PATH_KNN, 'rb') as infile:
knn_model = pickle.load(infile)