def detect3(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45):
if isinstance(image, bytes):
# image is a filename
# i.e. image = b'/darknet/data/dog.jpg'
im = load_image(image, 0, 0)
else:
# image is an nparray
# i.e. image = cv2.imread('/darknet/data/dog.jpg')
im, image = array_to_image(image)
dn.rgbgr_image(im)
num = dn.c_int(0)
pnum = dn.pointer(num)
dn.predict_image(net, im)
dets = dn.get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0,
pnum)
num = pnum[0]
if nms: dn.do_nms_obj(dets, num, meta.classes, nms)
res = []
for j in range(num):
a = dets[j].prob[0:meta.classes]
if any(a):
ai = np.array(a).nonzero()[0]
for i in ai:
b = dets[j].bbox
res.append(
(meta.names[i], dets[j].prob[i], (b.x, b.y, b.w, b.h)))
res = sorted(res, key=lambda x: -x[1])
if isinstance(image, bytes): free_image(im)
dn.free_detections(dets, num)
return res
def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45):
# im = load_image(image, 0, 0)
t = time.time()
im = array_to_image(image)
print('array_to_image time: {}').format(time.time() - t)
dn.rgbgr_image(im)
num = dn.c_int(0)
pnum = dn.pointer(num)
dn.predict_image(net, im)
dets = dn.get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0,
pnum)
num = pnum[0]
if (nms): dn.do_nms_obj(dets, num, meta.classes, nms)
t = time.time()
res = []
for j in range(num):
for i in range(meta.classes):
if dets[j].prob[i] > 0:
b = dets[j].bbox
res.append(
(meta.names[i], dets[j].prob[i], (b.x, b.y, b.w, b.h)))
res = sorted(res, key=lambda x: -x[1])
# free_image(im)
# free_detections(dets, num)
return res
def detect(self,
image: str,
thresh: float = .5,
hier_thresh: float = .5,
nms: float = .45) -> list:
if isinstance(image, str):
im = dn.load_image(image.encode(), 0, 0)
elif image is None:
return []
else:
arr = image.transpose(2, 0, 1)
c, h, w = arr.shape
arr = (arr/255.0).flatten()
data = dn.c_array(dn.c_float, arr)
im = dn.IMAGE(w, h, c, data)
num = dn.c_int(0)
pnum = dn.pointer(num)
dn.predict_image(self.net, im)
dets = dn.get_network_boxes(
self.net, im.w, im.h, thresh, hier_thresh, None, 0, pnum)
num = pnum[0]
if (nms):
dn.do_nms_obj(dets, num, self.meta.classes, nms)
res = []
for j in range(num):
if dets[j].prob[PERSON_ID] > 0:
bb = dets[j].bbox
res.append((dets[j].prob[PERSON_ID], BBOX(bb)))
res = sorted(res, key=lambda x: -x[0]) # 0 is prob
# dn.free_image(im) # raise double free error
dn.free_detections(dets, num)
return res
def detect():
print('Loading image')
# Use tmp.jpg for demo purposes
im = dn.load_image(bytes('tmp.jpg', encoding='utf-8'), 0, 0)
num = dn.c_int(0)
pnum = dn.pointer(num)
print('Predicting image')
dn.predict_image(net, im)
print('Getting boxes')
dets = dn.get_network_boxes(net, im.w, im.h, 0.5, 0.5, None, 1, pnum)
print('Marking boxes')
res = []
classes = 1
for j in range(num.value):
for i in range(classes):
if dets[j].prob[i] > 0.75:
b = dets[j].bbox
res.append((b.x, b.y, b.w, b.h))
dn.free_image(im)
dn.free_detections(dets, num)
print('Saving image')
source_img = Image.open('tmp.jpg').convert("RGB")
size = source_img.size
w = size[0]
h = size[1]
draw = ImageDraw.Draw(source_img)
for b in res:
x1 = (b[0] - b[2] / 2.) * w
x2 = (b[0] + b[2] / 2.) * w
y1 = (b[1] - b[3] / 2.) * h
y2 = (b[1] + b[3] / 2.) * h
draw.rectangle(((x1, y1), (x2, y2)), outline="red")
print(b)
source_img.save('tmp.jpg', "JPEG")
def detect2(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45):
# num = dn.c_int(0)
# pnum = dn.pointer(num)
# dn.predict_image(net, image)
# dets = dn.get_network_boxes(net, image.w, image.h, thresh, hier_thresh, None, 0, pnum)
# num = pnum[0]
# if (nms): dn.do_nms_obj(dets, num, meta.classes, nms);
# res = []
# for j in range(num):
# for i in range(meta.classes):
# if dets[j].prob[i] > 0:
# b = dets[j].bbox
# res.append((meta.names[i], dets[j].prob[i], (b.x, b.y, b.w, b.h)))
# res = sorted(res, key=lambda x: -x[1])
# dn.free_image(image)
# dn.free_detections(dets, num)
# return res
num = dn.c_int(0)
pnum = dn.pointer(num)
dn.predict_image(net, image)
dets = dn.get_network_boxes(net, image.w, image.h, thresh, hier_thresh,
None, 0, pnum)
num = pnum[0]
if nms: dn.do_nms_obj(dets, num, meta.classes, nms)
res = []
for j in range(num):
a = dets[j].prob[0:meta.classes]
if any(a):
ai = np.array(a).nonzero()[0]
for i in ai:
b = dets[j].bbox
res.append(
(meta.names[i], dets[j].prob[i], (b.x, b.y, b.w, b.h)))
res = sorted(res, key=lambda x: -x[1])
if isinstance(image, bytes):
dn.free_image(image) #这步什么情况下执行？多次无法执行的原因是未对这句做判断
print("free image")
# dn.free_image(image)
dn.free_detections(dets, num)
return res
def detect2(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45):
num = dn.c_int(0)
pnum = dn.pointer(num)
dn.predict_image(net, image)
dets = dn.get_network_boxes(net, image.w, image.h, thresh, hier_thresh,
None, 0, pnum)
num = pnum[0]
if (nms): dn.do_nms_obj(dets, num, meta.classes, nms)
res = []
for j in range(num):
for i in range(meta.classes):
if dets[j].prob[i] > 0:
b = dets[j].bbox
res.append({
"name": meta.names[i].decode("utf-8"),
"conf": dets[j].prob[i],
"box": (b.x, b.y, b.w, b.h)
})
res = sorted(res, key=lambda x: x["name"])
#dn.free_image(im)
dn.free_detections(dets, num)
return res