# load the Neural Network and the meta
path = "./char/"
cfg = os.path.join(path, "yolov3_plate.cfg").encode()
weights = os.path.join(path, "yolov3_plate_4000_ok.weights").encode()
data = os.path.join(path, "obj_plate.data").encode()
net = dn.load_net(cfg, weights, 0)
meta = dn.load_meta(data)
path_v = "/home/jehl/darknet/char/img_char"
files = os.listdir(path_v)
for f in files:
img_path = os.path.join(path_v, f)
result, im = dn.detect(net, meta, img_path.encode(), thresh=0.1)
if len(result) != 0:
print("Recognize !!!!!!!!!!!!!!!!!!")
for p in result:
x1 = int(p[2][0] - p[2][2] / 2)
y1 = int(p[2][1] - p[2][3] / 2)
x2 = int(p[2][0] + p[2][2] / 2)
y2 = int(p[2][1] + p[2][3] / 2)
#cv2.rectangle(frame,(x1,y1),(x2,y2),(0,255,0),3)
#cv2.putText(frame, p["plate"], (x1,y1-5), cv2.FONT_HERSHEY_SIMPLEX, 1,(0,255,0),2)
h, w = im.h, im.w
center = (p[2][0], p[2][1])
hb, wb = (p[2][3], p[2][2])
#cv2.circle(frame, center, 10, (255,0,0),2)
#cv2.line(frame, (center[0]-wb/2,center[1]), (center[0]+wb/2,center[1]), (255,0,0),3)
#cv2.line(frame, (center[0],center[1]-hb/2), (center[0],center[1]+hb/2), (255,0,0),3)
def detection(image, net, meta):
res = dn.detect(net, meta, str.encode(image))
return res
input_dir = "../video/"
output_path = "../output.mp4"
data = "cfg/aerial.data"
cfg = "cfg/yolov3-aerial.cfg"
weights = "backup/yolov3-aerial.backup"
images = [d for d in os.listdir(input_dir) if d.endswith('.jpg')]
images.sort()
img = cv2.imread(images[0])
height, width, channels = img.shape
net = dn.load_net(cfg.encode(), weights.encode(), 0)
meta = dn.load_meta(data.encode())
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter(output_path, fourcc, 20.0, (width, height))
count = 0
total = len(images)
for i in images:
count += 1
print("Exportation {}%".format(count / total), end="\r")
img_path = os.path.join(input_dir, i)
r = dn.detect(net, meta, im.encode())
out.write(draw_yolo_result(im, r, ['car', 'bus', 'truck']))
out.release()
print("Exported {}".format(output_path))
import python.darknet as dn
dn.set_gpu(0)
net = dn.load_net(str.encode("cfg/tiny-yolo.cfg"),
str.encode("weights/tiny-yolo.weights"), 0)
meta = dn.load_meta(str.encode("cfg/coco.data"))
r = dn.detect(net, meta, str.encode("data/dog.jpg"))
print(r)
# And then down here you could detect a lot more images like:
# r = dn.detect(net, meta, "data/eagle.jpg")
# print(r)
# r = dn.detect(net, meta, "data/giraffe.jpg")
# print(r)
# r = dn.detect(net, meta, "data/horses.jpg")
# print(r)
# r = dn.detect(net, meta, "data/person.jpg")
# print(r)
cv2.putText(img,
detection[0].decode() +
" [" + str(round(detection[1] * 100, 2)) + "]",
(pt1[0], pt1[1] - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
[0, 255, 0], 2)
return img
# In[ ]:
n = 0
while True:
try:
# frame = cap.get_frame()
_,frame = cap.read()
# W,H = frame.shape[:2]
# print(W,H)
detected = detect(net,meta,frame,thresh=0.5)
frame = cvDrawBoxes(detected, frame)
cv2.waitKey(1)
cv2.imshow('frame',frame)
except Exception as e:
print(e)
# In[ ]: