def detect_image(integer,lokl,eventol,evento2l,the_queuel,the_final_queuel,threshl,hier_threshl,darknet_pathl, data_filel, cfg_filel, weight_filel):
# Inputs:
# =======
# the_queuel(multiprocessing.Queue) the queue where the images waits to be processed.
# hier_thresl (float)
# thresl (float) this parameters define the minimun probability that will be accepted as a detection.
# darknet_pathl (str) path to darknet folder.
# data_filel (str) path to data file.
# cfg_filel (str) path to condiguration file.
# weight_filel (str) path to weight file.
# Returns:
# ========
# outputs (list) every entry list is a dictionary with keys=['right','left','top','bottom','class','prob'] added to a queue
# Load YOLO weight
pyyolo.init(darknet_pathl, data_filel, cfg_filel, weight_filel)#loading darknet in the memory
#while True:
while the_queuel.qsize()>0 or eventol.is_set()==False:
from_queue = the_queuel.get() #note that every item in queuel is a list with the following form [w, h, c, data,frame_id,img_rgb]
outpyyolo=pyyolo.detect(from_queue[0], from_queue[1], from_queue[2], from_queue[3], threshl, hier_threshl)
the_queuel.task_done()
the_final_queuel.put([outpyyolo,from_queue[4],from_queue[5]])
pyyolo.cleanup()
print("tamagno de la cola cuando termino el proceso detector ",integer ," es ", the_queuel.qsize())
print("El proceso detector",integer,"ve que evento1 termino? ",eventol.is_set())
if evento2l.is_set()==False:
evento2l.set()
def end_classification():
pyyolo.cleanup()
####### USED FOR TESTING ########
# fullpath = "/home/carlos/vrlserver/videos/raw/cam1/recording_6"
# for item in range(22,23):
# ir = np.load(fullpath + "/ir_full_vid/ir_frame_" + str(item) + ".npy")
# depth = np.load(fullpath + "/depth_full_vid/depth_frame_" + str(item) + ".npy")
# rgb = np.load(fullpath + "/rgb_full_vid/rgb_frame_" + str(item) + ".npy")
# interaction_detector(rgb,depth,ir)
# end_classification()
def detect_yolo_pyyolo(img_arr, url='', classes=constants.hls_yolo_categories):
# from file
print('----- test original C using a file')
hash = hashlib.sha1()
hash.update(str(time.time()))
img_filename = hash.hexdigest()[:10] + 'pyyolo.jpg'
# img_filename = 'incoming.jpg'
cv2.imwrite(img_filename, img_arr)
outputs = pyyolo.test(img_filename, thresh, hier_thresh)
relevant_bboxes = []
for output in outputs:
print(output)
label = output['class']
xmin = output['left']
ymin = output['top']
xmax = output['right']
ymax = output['bottom']
item = {
'object': label,
'bbox': [xmin, ymin, xmax, ymax],
'confidence': '>' + str(thresh)
}
# item = {'object':label,'bbox':[xmin,ymin,xmax,ymax],'confidence':round(float(confidence),3)}
relevant_bboxes.append(item)
#not sure wht the diff is between second method and first
# camera
# print('----- test python API using a file')
# i = 1
# while i < 2:
# # ret_val, img = cam.read()
# img = cv2.imread(filename)
# img = img.transpose(2,0,1)
# c, h, w = img.shape[0], img.shape[1], img.shape[2]
# # print w, h, c
# data = img.ravel()/255.0
# data = np.ascontiguousarray(data, dtype=np.float32)
# outputs = pyyolo.detect(w, h, c, data, thresh, hier_thresh)
# for output in outputs:
# print(output)
# i = i + 1
# free model
pyyolo.cleanup()
return relevant_bboxes
def detection_yolo(datacfg, cfgfile, weightfile, imgfile):
pyyolo.init(datacfg, cfgfile, weightfile)
# image
catograys = {}
img = cv2.imread(imgfile)
img = img.transpose(2, 0, 1)
c, h, w = img.shape[0], img.shape[1], img.shape[2]
#print c, h, w
data = img.ravel() / 255.0
data = np.ascontiguousarray(data, dtype=np.float32)
outputs = pyyolo.detect(w, h, c, data, thresh, hier_thresh)
#print outputs
res = convertoutputyolo(outputs, w, h)
pyyolo.cleanup()
return res
def main(args):
darknet_path = './darknet'
datacfg = 'cfg/coco.data'
cfgfile = 'cfg/yolov2.cfg'
weightfile = '../yolov2.weights'
filename = darknet_path + '/data/person.jpg'
pyyolo.init(darknet_path, datacfg, cfgfile, weightfile)
'''Initializes and cleanup ros node'''
ir = image_retriever()
try:
rospy.spin()
except KeyboardInterrupt:
print "Shutting down ROS Image feature detector module"
# free model
pyyolo.cleanup()
def main(args):
rp = rospkg.RosPack()
darknet_path = '/home/nvidia/pyyolo/darknet'
datacfg = os.path.join(rp.get_path(NAME), 'data/cfg', 'coco.data')
cfgfile = os.path.join(rp.get_path(NAME), 'data/cfg', 'yolov2.cfg')
weightfile = os.path.join(rp.get_path(NAME), 'data', 'yolov2.weights')
pyyolo.init(darknet_path, datacfg, cfgfile, weightfile)
'''Initializes and cleanup ros node'''
ir = Detector()
try:
rospy.spin()
except KeyboardInterrupt:
print "Shutting down ROS Image feature detector module"
# free model
pyyolo.cleanup()
def GigeStreamer(cam_id):
camera_id = cam_id
pyyolo.init(darknet_path, datacfg, cfgfile, weightfile)
Aravis.enable_interface(camera_id) # using arv-fake-gv-camera-0.6
camera = Aravis.Camera.new(None)
stream = camera.create_stream(None, None)
payload = camera.get_payload()
for i in range(0, 50):
stream.push_buffer(Aravis.Buffer.new_allocate(payload))
print("Starting acquisition")
camera.start_acquisition()
while True:
buffer = stream.try_pop_buffer()
print(buffer)
if buffer:
frame = convert(buffer)
stream.push_buffer(buffer) #push buffer back into stream
cv2.imshow("frame", frame)
# img = cv2.imread(filename)
img = frame.transpose(2, 0, 1) # img = img.transpose(2,0,1)
c, h, w = img.shape[0], img.shape[1], img.shape[2]
data = img.ravel() / 255.0
data = np.ascontiguousarray(data, dtype=np.float32)
# perform_recognition()
outputs = pyyolo.detect(w, h, c, data, thresh, hier_thresh)
for output in outputs:
print(output)
ch = cv2.waitKey(1) & 0xFF
if ch == 27 or ch == ord('q'):
break
elif ch == ord('s'):
cv2.imwrite("imagename.png", frame)
camera.stop_acquisition()
pyyolo.cleanup()
# ret_val = cv2.imwrite(filename,img)
# print(ret_val)
pyyolo.init(darknet_path, datacfg, cfgfile, weightfile)
# from file
print('----- test original C using a file')
outputs = pyyolo.test(filename, thresh, hier_thresh, 0)
for output in outputs:
print(output)
# camera
print('----- test python API using a file')
i = 1
while i < 2:
# ret_val, img = cam.read()
img = cv2.imread(filename)
img = cv2.resize(img, (0, 0), fx=0.5, fy=0.5)
img = img.transpose(2, 0, 1)
c, h, w = img.shape[0], img.shape[1], img.shape[2]
# print w, h, c
data = img.ravel() / 255.0
data = np.ascontiguousarray(data, dtype=np.float32)
outputs = pyyolo.detect(w, h, c, data, thresh, hier_thresh)
for output in outputs:
print(output)
i = i + 1
# free model
pyyolo.cleanup()
def clean_up():
pyyolo.cleanup()
cam = cv2.VideoCapture(filename)
ret_val, img = cam.read()
print(ret_val)
if not ret_val:
sys.exit()
pyyolo.init(darknet_path, datacfg, cfgfile, weightfile)
fourcc = cv2.VideoWriter_fourcc(*'XVID')
outVideo = cv2.VideoWriter('outputRoboySkyfall.mp4',fourcc, 20.0, (800,533))
print('----- test python API using a file')
while True:
ok,img = cam.read()
frame = imutils.resize(img, width=800)
if not img.any():
sys.exit()
img = img.transpose(2,0,1)
c, h, w = img.shape[0], img.shape[1], img.shape[2]
print w, h, c
data = img.ravel()/255.0
data = np.ascontiguousarray(data, dtype=np.float32)
outputs = pyyolo.detect(w, h, c, data, thresh, hier_thresh)
for output in outputs:
print(output)
p1 = (output['left'],output['top'])
p2 = (output['right'],output['bottom'])
print(p1,p2)
cv2.rectangle(frame,p1,p2,(0,0,255,10))
outVideo.write(frame)
# free model
pyyolo.cleanup()
def __del__(self):
if pyyolo is not None:
pyyolo.cleanup()
def __del__(self):
""" Class destructor used for Clean up pyyolo object detector
"""
print("Cleaning up")
pyyolo.cleanup()
