def image_callback(self, data):
self.show = self.convert_image(data)
self.time_header = data.header
if self.show is None: return
# image = Image.fromarray(frame)
step1 = time.time()
frame_rgb = cv2.cvtColor(self.show, cv2.COLOR_BGR2RGB)
frame_resized = cv2.resize(
frame_rgb,
(darknet.network_width(netMain), darknet.network_height(netMain)),
interpolation=cv2.INTER_LINEAR)
darknet.copy_image_from_bytes(darknet_image, frame_resized.tobytes())
detections = darknet.detect_image(netMain,
metaMain,
darknet_image,
thresh=0.25)
#detections is in x y w h in real pixel size already
#boxs should receive in format similar to [[584, 268, 160, 316]]
if detections is None: return
## image = cvDrawBoxes(detections, frame_resized)
## image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
## cv2.imshow('Demo', image)
## cv2.waitKey(1)
detections = np.array(detections)
detections = detections[np.where(detections[:, 0] == b'person')]
if detections is None: return
#print("detections",detections)
if len(detections) == 0: return
c = np.delete(detections, [0, 1],
1).squeeze() #remove id and conf, only bbox
#print("c",c)
#r = #resize to coords on original image to compensate for the frame_resized
if len(c.shape) == 0:
boxs = np.array([c.tolist()])
else:
boxs = np.array([list(item) for item in c]) #formating
#print("boxs",boxs)
## boxs = xyxy_to_xywh(transformed)#.astype(np.uint8)
##
boxs[:, 2] = (boxs[:, 2] / yolo_filter_size) * width #w
boxs[:, 3] = (boxs[:, 3] / yolo_filter_size) * height #h
boxs[:,
0] = (boxs[:, 0] / yolo_filter_size) * width - boxs[:, 2] / 2 #x
boxs[:,
1] = (boxs[:, 1] / yolo_filter_size) * height - boxs[:, 3] / 2 #y
print("time for inference =>" + str(time.time() - step1))
#print(darknet.network_width(netMain),darknet.network_height(netMain)) #608 #608
# print("box_num",len(boxs))
features = encoder(self.show, boxs)
# score to 1.0 here).
detections = [
Detection(bbox, 1.0, feature)
for bbox, feature in zip(boxs, features)
]
# Run non-maxima suppression.
boxes = np.array([d.tlwh for d in detections])
scores = np.array([d.confidence for d in detections])
indices = preprocessing.non_max_suppression(boxes, nms_max_overlap,
scores)
detections = [detections[i] for i in indices]
for box in boxes:
bbox = track.to_tlbr()
try:
cv2.rectangle(self.show, (int(bbox[0]), int(bbox[1])),
(int(bbox[2]), int(bbox[3])), (255, 255, 255), 2)
except ValueError:
break
self.posid_pub.publish(Int32(data=len(boxes)))
self.track_pub.publish(self.bridge.cv2_to_imgmsg(self.show, "bgr8"))
def image_callback(self,data):
self.show = self.convert_image(data)
self.time_header = data.header
if self.show is None: return
# image = Image.fromarray(frame)
step1 = time.time()
frame_rgb = cv2.cvtColor(self.show, cv2.COLOR_BGR2RGB)
frame_resized = cv2.resize(frame_rgb,
(darknet.network_width(netMain),
darknet.network_height(netMain)),
interpolation=cv2.INTER_LINEAR)
darknet.copy_image_from_bytes(darknet_image,frame_resized.tobytes())
detections = darknet.detect_image(netMain, metaMain, darknet_image, thresh=0.25)
#detections is in x y w h in real pixel size already
#boxs should receive in format similar to [[584, 268, 160, 316]]
if detections is None: return
## image = cvDrawBoxes(detections, frame_resized)
## image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
## cv2.imshow('Demo', image)
## cv2.waitKey(1)
detections = np.array(detections)
detections = detections[ np.where( detections[:,0]==b'person' ) ]
if detections is None: return
#print("detections",detections)
if len(detections)==0: return
c = np.delete(detections,[0,1],1).squeeze() #remove id and conf, only bbox
#print("c",c)
#r = #resize to coords on original image to compensate for the frame_resized
if len(c.shape)==0:
boxs = np.array( [ c.tolist() ] )
else:
boxs = np.array([list(item) for item in c]) #formating
#print("boxs",boxs)
self.fps_count += 1
self.frame_count += 1
#boxs = xyxy_to_xywh(boxs)#.astype(np.uint8)
boxs = xywh_to_xyxy(boxs)
print(boxs)
boxs[:,2] = (boxs[:,2] /yolo_filter_size) * width #w
boxs[:,3] = (boxs[:,3] /yolo_filter_size) * height #h
boxs[:,0] = (boxs[:,0] /yolo_filter_size) * width #- boxs[:,2]/2#x
boxs[:,1] = (boxs[:,1] /yolo_filter_size) * height #- boxs[:,3]/2#y
print("time for inference =>"+str(time.time()-step1))
#print(darknet.network_width(netMain),darknet.network_height(netMain)) #608 #608
# print("box_num",len(boxs))
for bbox in boxs:
box_width = bbox[2] - bbox[0]
box_height = bbox[3] - bbox[1]
bbox[0] = bbox[0] - int(box_width/2)
bbox[1] = bbox[1] - int(box_height/2)
self.posid_array = Path()
try:
cv2.rectangle(self.show, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])),(255,255,255), 2)
except ValueError:
break
#cv2.putText(self.show, str(track.track_id),(int(bbox[0]), int(bbox[1])),0, 5e-3 * 200, (0,255,0),2)
# 显示实时FPS值
if (time.time() - self.start_time) > self.fps_interval:
# 计算这个interval过程中的帧数，若interval为1秒，则为FPS
self.realtime_fps = self.fps_count / (time.time() - self.start_time)
self.fps_count = 0 # 帧数清零
self.start_time = time.time()
fps_label = "FPS:"+str(self.realtime_fps)
cv2.putText(self.show, fps_label, (width-160, 25), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 3)
# 显示目前的运行时长及总帧数
if self.frame_count == 1:
self.run_timer = time.time()
run_time = time.time() - self.run_timer
time_frame_label = "Frame:"+str(self.frame_count)
self.posid_pub.publish(Int32(data=len(boxs)))
self.track_pub.publish(self.bridge.cv2_to_imgmsg(self.show, "bgr8"))
######## FAST YOLO wrapper
import os
cwd = os.path.dirname(__file__)
print(cwd)
configPath = cwd + "/external_yolov3/darknet/cfg/yolov3-spp.cfg"
#weightPath = cwd+"/external_yolov3/darknet/yolov3-spp.weights"
weightPath = cwd + "/external_yolov3/darknet/yolov3-spp-thermal.weights"
metaPath = cwd + "/external_yolov3/darknet/cfg/coco_accel.data"
netMain = darknet.load_net_custom(configPath.encode("ascii"),
weightPath.encode("ascii"), 0,
1) # batch size = 1
metaMain = darknet.load_meta(metaPath.encode("ascii"))
darknet_image = darknet.make_image(darknet.network_width(netMain),
darknet.network_height(netMain), 3)
###########
def convertBack(x, y, w, h):
xmin = int(round(x - (w / 2)))
xmax = int(round(x + (w / 2)))
ymin = int(round(y - (h / 2)))
ymax = int(round(y + (h / 2)))
return xmin, ymin, xmax, ymax
def cvDrawBoxes(detections, img):
for detection in detections:
x, y, w, h = detection[2][0],\
def image_callback(self,data):
self.show = self.convert_image(data)
self.time_header = data.header
if self.show is None: return
# image = Image.fromarray(frame)
step1 = time.time()
frame_rgb = cv2.cvtColor(self.show, cv2.COLOR_BGR2RGB)
frame_resized = cv2.resize(frame_rgb,
(darknet.network_width(netMain),
darknet.network_height(netMain)),
interpolation=cv2.INTER_LINEAR)
darknet.copy_image_from_bytes(darknet_image,frame_resized.tobytes())
detections = darknet.detect_image(netMain, metaMain, darknet_image, thresh=0.25)
#detections is in x y w h in real pixel size already
#boxs should receive in format similar to [[584, 268, 160, 316]]
if detections is None: return
## image = cvDrawBoxes(detections, frame_resized)
## image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
## cv2.imshow('Demo', image)
## cv2.waitKey(1)
detections = np.array(detections)
detections = detections[ np.where( detections[:,0]==b'person' ) ]
if detections is None: return
#print("detections",detections)
if len(detections)==0: return
c = np.delete(detections,[0,1],1).squeeze() #remove id and conf, only bbox
#print("c",c)
#r = #resize to coords on original image to compensate for the frame_resized
if len(c.shape)==0:
boxs = np.array( [ c.tolist() ] )
else:
boxs = np.array([list(item) for item in c]) #formating
#print("boxs",boxs)
self.fps_count += 1
self.frame_count += 1
## boxs = xyxy_to_xywh(transformed)#.astype(np.uint8)
##
boxs[:,2] = (boxs[:,2] /yolo_filter_size) * width #w
boxs[:,3] = (boxs[:,3] /yolo_filter_size) * height #h
boxs[:,0] = (boxs[:,0] /yolo_filter_size) * width - boxs[:,2]/2#x
boxs[:,1] = (boxs[:,1] /yolo_filter_size) * height - boxs[:,3]/2#y
print("time for inference =>"+str(time.time()-step1))
#print(darknet.network_width(netMain),darknet.network_height(netMain)) #608 #608
# print("box_num",len(boxs))
features = encoder(self.show,boxs)
# score to 1.0 here).
detections = [Detection(bbox, 1.0, feature) for bbox, feature in zip(boxs, features)]
# Run non-maxima suppression.
boxes = np.array([d.tlwh for d in detections])
scores = np.array([d.confidence for d in detections])
indices = preprocessing.non_max_suppression(boxes, nms_max_overlap, scores)
detections = [detections[i] for i in indices]
for track in tracker.tracks:
if not track.is_confirmed() or track.time_since_update > 1:
continue
self.posid_array = Path()
bbox = track.to_tlbr()
try:
cv2.rectangle(self.show, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])),(255,255,255), 2)
except ValueError:
break
cv2.putText(self.show, str(track.track_id),(int(bbox[0]), int(bbox[1])),0, 5e-3 * 200, (0,255,0),2)
# 显示实时FPS值
if (time.time() - self.start_time) > self.fps_interval:
# 计算这个interval过程中的帧数，若interval为1秒，则为FPS
self.realtime_fps = self.fps_count / (time.time() - self.start_time)
self.fps_count = 0 # 帧数清零
self.start_time = time.time()
fps_label = 'FPS:{0:.2f}'.format(self.realtime_fps)
cv2.putText(self.show, fps_label, (width-160, 25), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 3)
# 显示目前的运行时长及总帧数
if self.frame_count == 1:
self.run_timer = time.time()
run_time = time.time() - self.run_timer
time_frame_label = '[Time:{0:.2f} | Frame:{1}]'.format(run_time, self.frame_count)
cv2.putText(self.show, time_frame_label, (5, height-15), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 3)
self.posid_pub.publish(Int32(data=len(boxes)))
self.track_pub.publish(self.bridge.cv2_to_imgmsg(self.show, "bgr8"))