def inference(model,
image,
target_shape,
conf_thresh=0.5,
iou_thresh=0.4,
mode=1):
image = np.array(image)[:, :, ::-1].copy()
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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)
image_transposed = image_exp.transpose((0, 3, 1, 2))
y_bboxes_output, y_cls_output = pytorch_inference(model, image_transposed)
# 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 class_id == 0:
color = (0, 255, 0)
else:
if mode:
color = (0, 0, 255)
else:
color = (255, 0, 0)
cv2.rectangle(image, (xmin, ymin), (xmax, ymax), color, 2)
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])
return (output_info, image)
def inference(image,
conf_thresh=0.5,
iou_thresh=0.5,
target_shape=(360, 360),
draw_result=True,
show_result=False):
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)
image_transposed = image_exp.transpose((0, 3, 1, 2))
y_bboxes_output, y_cls_output = pytorch_inference(model, image_transposed)
y_bboxes = decode_bbox(anchors_exp, y_bboxes_output)[0]
y_cls = y_cls_output[0]
bbox_max_scores = np.max(y_cls, axis=1)
bbox_max_score_classes = np.argmax(y_cls, axis=1)
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]
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:
if class_id == 0:
color = (0, 255, 0)
else:
color = (255, 0, 0)
cv2.rectangle(image, (xmin, ymin), (xmax, ymax), color, 2)
cv2.putText(image, "%s: %.2f" % (id2class[class_id], conf),
(xmin + 2, ymin - 2), cv2.FONT_HERSHEY_SIMPLEX, 0.8,
color, 2)
output_info.append([class_id, conf, xmin, ymin, xmax, ymax])
if show_result:
Image.fromarray(image).show()
return output_info
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)
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)
image_transposed = image_exp.transpose((0, 3, 1, 2))
y_bboxes_output, y_cls_output = pytorch_inference(model, image_transposed)
# 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:
if class_id == 0:
color = (0, 255, 0)
else:
color = (255, 0, 0)
cv2.rectangle(image, (xmin, ymin), (xmax, ymax), color, 2)
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])
if show_result:
Image.fromarray(image).show()
return output_info
def reconocimientoFacial(image,
max_distancia_centro,
conf_thresh=0.5,
iou_thresh=0.5,
target_shape=(360, 360),
draw_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.
'''
#convertimos la imagen que llega
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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)
image_transposed = image_exp.transpose((0, 3, 1, 2))
y_bboxes_output, y_cls_output = pytorch_inference(model, image_transposed)
# 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,
)
#variables para calcular la cara del centro
center_face_X = 0
center_face_Y = 0
distancia_al_centro = math.inf
cara_detectada = False
#inicilizamos a no mascarilla, que es true
#mascarilla es false, al final invertimos el resultado
class_id = True
for idx in keep_idxs:
cara_detectada = True
conf = float(bbox_max_scores[idx])
bbox = y_bboxes[idx]
#coordenadas minimas y maximas de la cara
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)
#calculamos el centro de la ara concreta
centerX = round((xmax + xmin) / 2)
centerY = round((ymax + ymin) / 2)
distancia = math.sqrt(((centerX-(width/2))**2)+((centerY-(height/2))**2))
#guardamos para quedarnos con la más cercana al centro
if distancia < distancia_al_centro:
distancia_al_centro = distancia
center_face_X = centerX
center_face_Y = centerY
class_id = bbox_max_score_classes[idx]
#dibujamos recuado en la cara detectada
if draw_result:
if class_id == 0:
color = (0, 255, 0)
else:
color = (255, 0, 0)
cv2.rectangle(image, (xmin, ymin), (xmax, ymax), color, 2)
cv2.putText(image, "%s: %.2f" % (id2class[class_id], conf), (xmin + 2, ymin - 2),
cv2.FONT_HERSHEY_SIMPLEX, 0.8, color)
#inicialiamos la variable que detecta si la cara esta en el circulo a false
locked = False
#dibujamos el circulo y el centro de la cara con distancia mas cercana al centro
if distancia_al_centro != math.inf and draw_result:
if distancia_al_centro < max_distancia_centro:
locked = True
color = (0, 255, 0)
else:
locked = False
color = (255, 0, 0)
cv2.rectangle(image,(center_face_X-10, center_face_Y), (center_face_X+10, center_face_Y), color, 2)
cv2.rectangle(image,(center_face_X, center_face_Y-10), (center_face_X, center_face_Y+10), color, 2)
cv2.circle(image, (int(width/2), int(height/2)), int(max_distancia_centro) , color, 2)
center_face_X = center_face_X-(width/2)
center_face_Y = center_face_Y-(height/2)
#inverimos class id para que false sea no mascarilla
return [cara_detectada, image, center_face_X, center_face_Y, locked, not class_id]
