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 __init__(self):
if pyyolo is not None:
darknet_path = './darknet'
datacfg = 'cfg/coco.data'
cfgfile = 'cfg/yolo.cfg'
weightfile = 'yolo.weights'
pyyolo.init(darknet_path, datacfg, cfgfile, weightfile)
self.hier_thresh = 0.1
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 __init__(self):
""" Initialize pyyolo and darknet for object detection .
:param object: base class inheritance
:type object: class:`Object`
"""
C = config.Config()
x = str(os.path.realpath(Path(__file__).parent)).split("/")
weightfile_templates = os.path.join("/", *x[:-1],
C.WEIGHTFILE_TEMPLATES)
self.thresh = C.THRESH
self.hier_thresh = C.HIER_THRESH
# initialize pyyolo
darknet_path = os.path.join("/", *x[:-1], C.DARKNET_PATH)
datacfg = os.path.join("/", *x[:-1], C.DATACFG)
cfgfile = os.path.join("/", *x[:-1], C.CFGFILE)
pyyolo.init(darknet_path, datacfg, cfgfile, weightfile_templates)
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()
# Create output folder
if os.path.isdir(output_dir):
for file in glob.iglob(os.path.join(output_dir, image_extension)):
os.remove(file)
else:
os.makedirs(output_dir, mode=0o777, exist_ok=True)
# Open video stream
cap = cv2.VideoCapture(video_file) #opening the cam
ret_val, img = cap.read()
h, w, c = img.shape
# ratio=np.min([540/float(h), 960/float(w)])
# Load YOLO weight
nn1=pyyolo.init(darknet_path, data_file1, cfg_file1, weight_file1)#loading darknet in the memory
nn2=pyyolo.init(darknet_path, data_file2, cfg_file2, weight_file2)
time_start=datetime.now()
while (cap.isOpened()):
if frame_id % 100==0:
print("Processing frame: ", frame_id)
ret_val, img = cap.read()
frame_id+=1
if not ret_val:
break
#if frame_id % 5 != 0:
# continue
img_rgb=cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
import time
import cv2
from trendi import constants
#get yolo net and keep it in mem
#datacfg = 'cfg/coco.data'
#datacfg = '/data/jeremy/pyyolo/darknet/cfg/coco.data'
datacfg = '/data/jeremy/darknet_orig/cfg/hls.data'
cfgfile = '/data/jeremy/darknet_orig/cfg/yolo-voc_544.cfg'
#cfgfile = '/data/jeremy/pyyolo/darknet/cfg/tiny-yolo.cfg'
#weightfile = '/data/jeremy/pyyolo/tiny-yolo.weights'
weightfile = '/data/jeremy/darknet_orig/bb_hls1/yolo-voc_544_95000.weights'
thresh = 0.24
hier_thresh = 0.5
pyyolo.init(datacfg, cfgfile, weightfile)
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)
from flask import Flask, request, jsonify, abort
# import json, urllib, sys, cv2, shutil, time
import json, urllib, sys, shutil, time
from io import BytesIO
from PIL import Image
import numpy as np
import pyyolo
import IOfuncs as iof
darknet_path = '../pyyolo/darknet'
datacfg = 'cfg/coco.data'
cfgfile = 'cfg/yolo.cfg'
weightfile = '../../weights/yolo.weights'
thresh = 0.24
hier_thresh = 0.5
pyyolo.init(darknet_path, datacfg, cfgfile, weightfile)
application = Flask(__name__)
# # WHITELIST
# # Limit use of API to IPs in whitelist.txt
# @application.before_request
# def limit_remote_addr():
# whitelist = list()
# for line in open('whitelist.txt'):
# line = line.strip()
# whitelist.append(line)
# if request.remote_addr not in whitelist:
# print "[ABORT] IP not on whitelist:", request.remote_addr
# abort(403)
def worker(camId):
pyyolo.init(darknet_path, datacfg, cfgfile, weightfile)
Aravis.enable_interface("Fake")
try:
cam = Aravis.Camera.new(camId)
print("Camera found")
except:
print("No camera found")
exit()
thresh = 0.45
hier_thresh = 0.5
#cam.set_region (0,0,2048,1536)
#cam.set_region (0,0,512,512)
#cam.set_frame_rate (10.0)
#cam.set_exposure_time(500000)
#cam.set_pixel_format (Aravis.PIXEL_FORMAT_MONO_8)
#print(dir(cam))
stream = cam.create_stream(None, None)
payload = cam.get_payload()
[x, y, width, height] = cam.get_region()
print("Camera vendor : %s" % (cam.get_vendor_name()))
print("Camera model : %s" % (cam.get_model_name()))
print("Camera id : %s" % (cam.get_device_id()))
print("ROI : %dx%d at %d,%d" % (width, height, x, y))
print("Payload : %d" % (payload))
print("Pixel format : %s" % (cam.get_pixel_format_as_string()))
#cv2.namedWindow('Live Stream', flags=0)
cv2.namedWindow(camId, flags=0)
#rint(dir(stream))
#for i in range(0,50):
#stream.push_buffer (Aravis.Buffer.new_allocate (payload))
cam.start_acquisition()
lastTime = time.time()
transposeTime = 0
i = 0
while (True):
stream.push_buffer(Aravis.Buffer.new_allocate(payload))
buffer = stream.pop_buffer()
transposeTime = time.time()
b = ctypes.cast(buffer.data, ctypes.POINTER(ctypes.c_uint8))
im = np.ctypeslib.as_array(b, (height, width))
#im = im.copy()
#print("shape: ", im.shape)
#cv2.imshow("Live Stream", im.copy())
gray = im.copy()
cv2.imshow(camId, gray)
#im = np.zeros((3,gray.shape[0],gray.shape[1]))
#im[1,:,:] = gray
#im = cv2.cvtColor(gray,cv2.COLOR_GRAY2RGB)
im = np.stack((im, ) * 3, -1)
im = im.transpose(2, 0, 1)
c, h, w = im.shape[0], im.shape[1], im.shape[2]
#print(im.shape)
#cv2.imshow(camId, im.copy())
im = im.ravel() / 255.0
#print(im.shape)
data = np.ascontiguousarray(im, dtype=np.float32)
#print("TRANS-FPS: ", 1.0/(time.time()-transposeTime))
predictions = pyyolo.detect(w, h, c, data, thresh, hier_thresh)
# { 'left':0, 'right': 767, 'top': 0, 'bottom': x, class': x, prob: x, }
for output in predictions:
left, right, top, bottom, what, prob = output['left'], output[
'right'], output['top'], output['bottom'], output[
'class'], output['prob']
print(output)
if (what == 'car'):
d_from_top = top
d_from_bottom = bottom
d_from_left = left
d_from_right = right
d_height = top
d_width = left
if (d_height > 0.5):
print(output)
#trigger other cameras
cv2.waitKey(1)
print("CAM: ", camId, " Frame: ", i, " FPS: ",
1.0 / (time.time() - lastTime), "RES: ", h, " x ", w)
lastTime = time.time()
i += 1
cam.stop_acquisition()
def worker(camId):
pyyolo.init(darknet_path, datacfg, cfgfile, weightfile)
CAM_NAME = CAM_CONFIG[camId]['name']
WINDOW_NAME = CAM_CONFIG[camId]['window']
IS_ROTATE = CAM_CONFIG[camId]['rotate']
h = Harvester()
h.add_cti_file('/opt/mvIMPACT_Acquire/lib/x86_64/mvGenTLProducer.cti')
h.update_device_info_list()
try:
cam = h.create_image_acquisition_manager(serial_number=CAM_NAME)
print("Camera found")
except:
print("Camera Not Found")
exit()
cam.start_image_acquisition()
lastTime = time.time()
transposeTime = 0
i = 0
numberCars = 0
lastSnapshot = None
cv2.namedWindow(WINDOW_NAME, flags=0)
while (True):
buffer = cam.fetch_buffer()
image = buffer.payload.components[0].data
small = cv2.resize(image,
dsize=(320, 200),
interpolation=cv2.INTER_CUBIC)
clone = small.copy()
rgb = cv2.cvtColor(clone, cv2.COLOR_BayerRG2RGB)
#img = rgb.transpose(2,0,1)
img = np.rot90(rgb)
print(rgb.shape)
c, h, w = img.shape[0], img.shape[1], img.shape[2]
#c, h, w = img.shape[2], img.shape[1], img.shape[0]
data = img.ravel() / 255.0
#data = np.ascontiguousarray(data, dtype=np.float32)
predictions = pyyolo.detect(w, h, c, data, thresh, hier_thresh)
#im = np.zeros((3,small.shape[1],small.shape[0]))
#im[0,:,:] = np.rot90(small)
#im[1,:,:] = np.rot90(small)
#im[2,:,:] = np.rot90(small)
#im = rgb
print(rgb.shape)
#c, h, w = im.shape[0], im.shape[1], im.shape[2]
# im = im.transpose(2,0,1)
#c, h, w = im.shape[0], im.shape[1], im.shape[2]
#c, h, w = 1, image.shape[0], image.shape[1]
#im = image.copy()
#data = im.ravel()/255.0
#print(data.shape)
#data = np.ascontiguousarray(data, dtype=np.float32)
#print(data.shape)
for output in predictions:
left, right, top, bottom, what, prob = output['left'], output[
'right'], output['top'], output['bottom'], output[
'class'], output['prob']
#print(output)
#lastSnapshot = snapshot.copy()
#cv2.imshow("Snapshots", lastSnapshot)
if (what == 'car'):
print(output)
numberCars += 1
cv2.rectangle(rgb, (50, 50), (100, 150), (0, 255, 0), 5)
if (camId == "CAM_2"):
urllib.request.urlopen(TRIGGER_FAR_FLASH_URL).read()
urllib.request.urlopen(TRIGGER_CLOSE_FLASH_URL).read()
urllib.request.urlopen(TRIGGER_TRUCK_FLASH_URL).read()
if IS_ROTATE:
cv2.imshow(WINDOW_NAME, np.rot90(rgb))
else:
cv2.imshow(WINDOW_NAME, rgb)
cv2.waitKey(1)
buffer.queue()
print("Count: ", numberCars, " Frame: ", i, " FPS: ",
1.0 / (time.time() - lastTime))
lastTime = time.time()
i += 1
cam.stop_image_acquisition()
cam.destroy()
def _init_model(self):
LOG.info('init general python yolo model')
pyyolo.init(self.params['darknet_path'],
os.path.join('../', self.params['datacfg']),
os.path.join('../', self.params['cfgfile']),
self.params['weightfile'])
images = np.vstack([x])
y_proba = weight.predict(images, batch_size=1)
y_classes = y_proba.argmax(axis=-1)
y_percent = y_proba[0][y_classes[0]]
if y_percent < 0.65:
continue
folder_dest = os.path.join(RESULTS_DIR,
LABELS[y_classes[0]],
maker + '|' + model)
if not os.path.exists(folder_dest):
os.makedirs(folder_dest)
new_file = '0' * (6 -
len(str(index[y_classes[0]]))) + str(
index[y_classes[0]]) + ".jpg"
new_img_dest = os.path.join(folder_dest, new_file)
img.save(new_img_dest)
index[y_classes[0]] = index[y_classes[0]] + 1
except:
print("Filter Error: %s" %
os.path.join(images_dir, imagefile))
if __name__ == '__main__':
pyyolo.init(DARKNET_PATH, DATA_CFG, CFG_FILE, WEIGHT_FILE)
#filter_image()
load_image('DBA-NZE144G_0234941.jpg')
def detector():
# Initialize publisher ROS node
pub = rospy.Publisher('predictions', Predictions, queue_size=10)
pub1 = rospy.Publisher('peoplecount', Peoplecount, queue_size=10)
pub2 = rospy.Publisher('videostream', Image, queue_size=1)
rospy.init_node('detector', anonymous=True)
# Ceate sort object
mot_tracker = Sort()
# Define paths for yolo files
darknet_path = '/home/ubuntu/catkin_ws/src/jetsontx1_cvmodule/src/pyyolo/darknet' # Only './darknet' is dependent on location of rosrun command
datacfg = 'cfg/coco.data'
cfgfile = 'cfg/yolov3-tiny.cfg'
weightfile = '../yolov3-tiny.weights' #'/media/ubuntu/SDcard/yoloWeights/yolov2-tiny.weights' this also works but it loads way slower
filename = darknet_path + '/data/person.jpg'
# Image resolution parameters
(width,
height) = (1280, 720
) # Use (672,376) for VGA and (1280,720) for HD720 resolution
# Initialize visualization
fourcc = cv2.VideoWriter_fourcc(*'MJPG')
video = cv2.VideoWriter('predictionstest.avi', fourcc, 10, (width, height))
# Initialize pyyolo
pyyolo.init(darknet_path, datacfg, cfgfile, weightfile)
# Initialize face detector
face_cascade = cv2.CascadeClassifier(
'/usr/share/OpenCV/haarcascades/haarcascade_frontalface_default.xml')
smile_cascade = cv2.CascadeClassifier(
'/usr/share/OpenCV/haarcascades/haarcascade_smile.xml')
# Create a Camera object
zed = sl.Camera()
# Create a InitParameters object and set configuration parameters
init_params = sl.InitParameters()
init_params.camera_resolution = sl.RESOLUTION.HD720 # Use HD1080, HD720 or VGA video mode
init_params.camera_fps = 15 # Set fps at 30
# Open the camera
err = zed.open(init_params)
if err != sl.ERROR_CODE.SUCCESS:
exit(1)
zed.set_camera_settings(sl.VIDEO_SETTINGS.EXPOSURE, 50)
image = sl.Mat()
point_cloud = sl.Mat()
colours = 255 * np.random.rand(32,
3) # For drawing different colours on BB
runtime_parameters = sl.RuntimeParameters()
while not rospy.is_shutdown():
start = time.time()
# Grab an image, a RuntimeParameters object must be given to grab()
if zed.grab(runtime_parameters) == sl.ERROR_CODE.SUCCESS:
# A new image is available if grab() returns SUCCESS
zed.retrieve_image(image, sl.VIEW.LEFT)
data = image.get_data()
gray_picture = cv2.cvtColor(
data, cv2.COLOR_BGR2GRAY
) # Make picture gray for face/smile detection
# Retrieve colored point cloud. Point cloud is aligned on the left image.
zed.retrieve_measure(point_cloud, sl.MEASURE.XYZRGBA)
Data = data.transpose(2, 0, 1)
start5 = time.time()
Data = Data.ravel() / 255.0
end5 = time.time()
Data = np.ascontiguousarray(Data, dtype=np.float32)
start1 = time.time()
outputs = pyyolo.detect(
width, height, 4, Data, 0.5, 0.8
) #pyyolo.detect returns: [{'right':, 'bottom':, 'top':, 'class':, 'prob':, 'left':}]
end1 = time.time()
print("Section cycle time: ", end1 - start1)
dets = np.empty((0, 5), int)
count = 0
for output in outputs:
if output['class'] == 'person': #track only people
count = count + 1 # Count detected people
dets = np.append(dets, [[
output['left'], output['top'], output['right'],
output['bottom'], output['prob']
]],
axis=0)
people = Peoplecount()
people.tot_detected_people = count
pub1.publish(people)
preds = Predictions()
trackers = mot_tracker.update(
dets
) #update tracking ID. mot_tracker.update() returns: [[x1,y1,x2,y2,id]]
for d in trackers:
d = d.astype(np.int32)
# Create a dictionary to store info for publishing
detectinfo = {
'left': d[0],
'top': d[1],
'right': d[2],
'bottom': d[3],
'class': 'person',
'ID': int(d[4])
}
euclidean_distance(detectinfo, point_cloud)
relative_coordinates(detectinfo, width)
start3 = time.time()
facesmile_detect(detectinfo, gray_picture, data, face_cascade,
smile_cascade)
end3 = time.time()
#print("facesmile detect cycle time: ", end3 - start3)
#print detectinfo['smile']
pred = Prediction()
#pred.probabilities.append()
pred.classes.append(detectinfo['class'])
pred.xmin = detectinfo['left']
pred.ymin = detectinfo['top']
pred.xmax = detectinfo['right']
pred.ymax = detectinfo['bottom']
pred.id = detectinfo['ID']
pred.face = detectinfo['face']
pred.smile = detectinfo['smile']
pred.distance = detectinfo['distance']
pred.angle = detectinfo['angle']
pred.xcoord = detectinfo['x']
pred.ycoord = detectinfo['y']
preds.predictions.append(pred)
label = detectinfo['class'] + " " + str(detectinfo['ID'])
cv2.rectangle(data, (d[0], d[1]), (d[2], d[3]),
(colours[d[4] % 32, 0], colours[d[4] % 32, 1],
colours[d[4] % 32, 2]), 2)
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(data, label, (d[2], d[1] + 25), font, 1,
(0, 0, 255), 1, cv2.LINE_AA)
pub.publish(preds)
msg_frame = CvBridge().cv2_to_imgmsg(data, "8UC4")
pub2.publish(msg_frame)
#video.write(data[:,:,:3])#because data.shape is (376,672,4) and it only supports 3 channels.
cv2.imshow("image", data)
cv2.waitKey(35)
end = time.time()
print("Total cycle time: ", end - start)
# Close the camera
zed.close()
import sys
sys.path.append('/usr/local/lib/python2.7/dist-packages/')
import pyyolo
import numpy as np
import cv2
darknet_path = './darknet'
data_cfg = 'deployment/easy.data'
cfg_file = 'deployment/easy-tiny.cfg'
weight_file = 'deployment/easy-tiny.backup'
thresh = 0.24
hier_thresh = 0.5
pyyolo.init(darknet_path, data_cfg, cfg_file, weight_file)
def predict(img):
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)
return outputs
def clean_up():
pyyolo.cleanup()
def init_yolo(yoloPath):
darknet_path = yoloPath+"/darknet"
datacfg = 'cfg/coco.data'
cfgfile = 'cfg/tiny-yolo.cfg'
weightfile = '../tiny-yolo.weights'
pyyolo.init(darknet_path, datacfg, cfgfile, weightfile)
import imutils
darknet_path = './darknet'
datacfg = 'cfg/coco.data'
cfgfile = 'cfg/tiny-yolo.cfg'
weightfile = '../tiny-yolo.weights'
#filename = darknet_path + '/data/person.jpg'
filename = "/home/roboy/outputRoboy.mp4"
thresh = 0.24
hier_thresh = 0.5
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)
# Create output folder
if os.path.isdir(output_dir):
for file in glob.iglob(os.path.join(output_dir, image_extension)):
os.remove(file)
else:
os.makedirs(output_dir, mode=0o777, exist_ok=True)
# Open video stream
cap = cv2.VideoCapture(video_file) #opening the cam
ret_val, img = cap.read()
h, w, c = img.shape
# ratio=np.min([540/float(h), 960/float(w)])
# Load YOLO weight
pyyolo.init(darknet_path, data_file, cfg_file,
weight_file) #loading darknet in the memory
time_start = datetime.now()
while (cap.isOpened()):
if frame_id % 100 == 0:
print("Processing frame: ", frame_id)
ret_val, img = cap.read()
frame_id += 1
if not ret_val:
break
#if frame_id % 5 != 0:
# continue
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
