def inference(self, image, conf_thresh=0.5, iou_thresh=0.4, target_shape=(160, 160)):
'''
Main function of detection inference
:param image: 3D numpy array of image
:param conf_thresh: the min threshold of classification probabity.
:param iou_thresh: the IOU threshold of NMS
:param target_shape: the model input size.
:return:
'''
# image = np.copy(image)
height, width, _ = image.shape
image_resized = cv2.resize(image, target_shape)
image_np = image_resized / 255.0 # 归一化到0~1
image_exp = np.expand_dims(image_np, axis=0)
y_bboxes_output, y_cls_output = tf_inference(self.sess, self.graph, image_exp)
# remove the batch dimension, for batch is always 1 for inference.
y_bboxes = decode_bbox(self.anchors_exp, y_bboxes_output)[0]
y_cls = y_cls_output[0]
# To speed up, do single class NMS, not multiple classes NMS.
bbox_max_scores = np.max(y_cls, axis=1)
bbox_max_score_classes = np.argmax(y_cls, axis=1)
# keep_idx is the alive bounding box after nms.
keep_idxs = single_class_non_max_suppression(y_bboxes,
bbox_max_scores,
conf_thresh=conf_thresh,
iou_thresh=iou_thresh,
)
resp = face_detection_pb2.FaceDetResponse()
for idx in keep_idxs:
conf = float(bbox_max_scores[idx])
class_id = bbox_max_score_classes[idx]
bbox = y_bboxes[idx]
# clip the coordinate, avoid the value exceed the image boundary.
xmin = max(0, int(bbox[0] * width))
ymin = max(0, int(bbox[1] * height))
xmax = min(int(bbox[2] * width), width)
ymax = min(int(bbox[3] * height), height)
det_obj = face_detection_pb2.DetectedObj(lx=xmin, ly=ymin, rx=xmax, ry=ymax, score=conf)
resp.detObjs.append(det_obj)
return resp
def inference(image,
conf_thresh=0.5,
iou_thresh=0.4,
target_shape=(160, 160),
draw_result=True,
show_result=True):
'''
Main function of detection inference
:param image: 3D numpy array of image
:param conf_thresh: the min threshold of classification probabity.
:param iou_thresh: the IOU threshold of NMS
:param target_shape: the model input size.
:param draw_result: whether to daw bounding box to the image.
:param show_result: whether to display the image.
:return:
'''
# image = np.copy(image)
image_resized = cv2.resize(image, target_shape)
image_np = image_resized / 255.0 # 归一化到0~1
image_exp = np.expand_dims(image_np, axis=0)
y_bboxes_output, y_cls_output = tf_inference(sess, graph, image_exp)
# remove the batch dimension, for batch is always 1 for inference.
y_bboxes = decode_bbox(anchors_exp, y_bboxes_output)[0]
y_cls = y_cls_output[0]
# To speed up, do single class NMS, not multiple classes NMS.
bbox_max_scores = np.max(y_cls, axis=1)
bbox_max_score_classes = np.argmax(y_cls, axis=1)
# keep_idx is the alive bounding box after nms.
keep_idxs = single_class_non_max_suppression(
y_bboxes,
bbox_max_scores,
conf_thresh=conf_thresh,
iou_thresh=iou_thresh,
)
for idx in keep_idxs:
conf = float(bbox_max_scores[idx])
class_id = bbox_max_score_classes[idx]
if class_id == 0: #有口罩
return 0
return 1
conf_thresh = 0.5
iou_thresh = 0.4
target_shape = (260, 260)
draw_result = True
show_result = True
output_info = []
height, width, _ = image.shape
image_resized = cv2.resize(image, target_shape)
image_np = image_resized / 255.0
image_exp = np.expand_dims(image_np, axis=0)
cv2.imshow('image',image_exp)
y_bboxes_output, y_cls_output = tf_inference(sess, graph, image_exp)
# remove the batch dimension, for batch is always 1 for inference.
y_bboxes = decode_bbox(anchors_exp, y_bboxes_output)[0]
y_cls = y_cls_output[0]
# To speed up, do single class NMS, not multiple classes NMS.
bbox_max_scores = np.max(y_cls, axis=1)
bbox_max_score_classes = np.argmax(y_cls, axis=1)
# keep_idx is the alive bounding box after nms.
keep_idxs = single_class_non_max_suppression(y_bboxes,
bbox_max_scores,
conf_thresh=conf_thresh,
iou_thresh=iou_thresh,
)
def inference(image,
conf_thresh=0.5,
iou_thresh=0.5,
target_shape=(260, 260),
draw_result=True,
show_result=False
):
'''
Main function of detection inference
:param image: 3D numpy array of image
:param conf_thresh: the min threshold of classification probabity.
:param iou_thresh: the IOU threshold of NMS
:param target_shape: the model input size.
:param draw_result: whether to daw bounding box to the image.
:param show_result: whether to display the image.
:return:
'''
'''
people=names
name=""
# image = np.copy(image)
output_info = []'''
height, width, _ = image.shape
image_resized = cv2.resize(image, target_shape)
image_np = image_resized / 255.0 # 归一化到0~1
image_exp = np.expand_dims(image_np, axis=0)
y_bboxes_output, y_cls_output = tf_inference(sess, graph, image_exp)
# remove the batch dimension, for batch is always 1 for inference.
y_bboxes = decode_bbox(anchors_exp, y_bboxes_output)[0]
y_cls = y_cls_output[0]
# To speed up, do single class NMS, not multiple classes NMS.
bbox_max_scores = np.max(y_cls, axis=1)
bbox_max_score_classes = np.argmax(y_cls, axis=1)
# keep_idx is the alive bounding box after nms.
keep_idxs = single_class_non_max_suppression(y_bboxes,
bbox_max_scores,
conf_thresh=conf_thresh,
iou_thresh=iou_thresh,
)
for idx in keep_idxs:
conf = float(bbox_max_scores[idx])
class_id = bbox_max_score_classes[idx]
bbox = y_bboxes[idx]
# clip the coordinate, avoid the value exceed the image boundary.
xmin = max(0, int(bbox[0] * width))
ymin = max(0, int(bbox[1] * height))
xmax = min(int(bbox[2] * width), width)
ymax = min(int(bbox[3] * height), height)
if draw_result:
loded_pic=[]
if class_id == 0:
color = (0, 255, 0)
else:
color = (255, 0, 0)
''' if people_count!=len(keep_idxs):
people_count=len(keep_idxs)
for p in people_file:
temp=face_recognition.load_image_file(p)
loded_pic.append(face_recognition.face_encodings(temp)[0])
#face_encodings 여러개 되는지 체크하기
unknown_face_encoding = face_recognition.face_encodings(image[:,: ,::-1],known_face_locations=[(ymin,xmax,ymax,xmin)])[0]
results = face_recognition.compare_faces(loded_pic, unknown_face_encoding)
#print("len"+str(len(loded_pic)))
for x,compare in enumerate(results):
if compare==True :
name=people[x]
break'''
cv2.rectangle(image, (xmin, ymin), (xmax, ymax), color, 2)
'''try:
cv2.imshow('image', image[ymin:ymax,xmin:xmax ,::-1])
except:
continue'''
cv2.putText(image, "%s: %.2f" % (id2class[class_id], conf), (xmin + 2, ymin - 2),cv2.FONT_HERSHEY_SIMPLEX, 0.8, color)
#output_info.append([class_id, conf, xmin, ymin, xmax, ymax,name])
if show_result:
Image.fromarray(image).show()
return
