def car_detect(Iorig):
Lcars = []
R = []
R = detect(vehicle_net, vehicle_meta, Iorig, thresh=0.5)
R = [r for r in R if r[0] in [b'car', 'bus']]
if len(R):
WH = np.array(Iorig.shape[1::-1], dtype=float)
Lcars = []
cars_crop = []
for i, r in enumerate(R):
cx, cy, w, h = (old_div(np.array(r[2]), np.concatenate(
(WH, WH)))).tolist()
tl = np.array([cx - w / 2., cy - h / 2.])
br = np.array([cx + w / 2., cy + h / 2.])
label = Label(0, tl, br)
Icar = crop_region(Iorig, label)
cv2.imwrite("crop1.png", Icar)
Icar1 = cv2.imread("crop1.png")
Lcars.append(label)
cars_crop.append(Icar1)
return Lcars, cars_crop
print('\t\t%d cars found' % len(R))
if len(R):
Iorig = cv2.imread(img_path)
WH = np.array(Iorig.shape[1::-1], dtype=float)
Lcars = []
for i, r in enumerate(R):
cx, cy, w, h = (np.array(r[2]) / np.concatenate(
(WH, WH))).tolist()
tl = np.array([cx - w / 2., cy - h / 2.])
br = np.array([cx + w / 2., cy + h / 2.])
label = Label(0, tl, br)
Icar = crop_region(Iorig, label)
Lcars.append(label)
cv2.imwrite('%s/%s_%dcar.png' % (output_dir, bname, i),
Icar)
lwrite('%s/%s_cars.txt' % (output_dir, bname), Lcars)
except:
traceback.print_exc()
sys.exit(1)
sys.exit(0)
def on_any_event(event):
if event.is_directory:
return None
elif event.event_type == 'created':
# Take any action here when a file is first created.
print("Received created event - %s." % event.src_path)
try:
print('\tScanning %s' % event.src_path)
try:
img_path = event.src_path
print('\tScanning %s' % img_path)
bname = basename(splitext(img_path)[0])
R, _ = detect(vehicle_net, vehicle_meta, img_path.encode('utf-8'), thresh=vehicle_threshold)
P, _ = detect(vehicle_net, vehicle_meta, img_path.encode('utf-8'), thresh=vehicle_threshold)
R = [r for r in R if r[0] in ['car', 'bus']]
P = [p for p in P if p[0] in ['person']]
print('\t\t%d cars found' % len(R))
print('\t\t%d persons found' % len(P))
if len(R):
Iorig = cv2.imread(img_path)
WH = np.array(Iorig.shape[1::-1], dtype=float)
Lcars = []
for i, r in enumerate(R):
cx, cy, w, h = (np.array(r[2]) / np.concatenate((WH, WH))).tolist()
tl = np.array([cx - w / 2., cy - h / 2.])
br = np.array([cx + w / 2., cy + h / 2.])
label = Label(0, tl, br)
Icar = crop_region(Iorig, label)
Lcars.append(label)
cv2.imwrite('%s/%s_%dcar.png' % (output_dir, bname, i), Icar)
# lwrite('%s/%s_cars.txt' % (output_dir, bname), Lcars)
if len(P):
Iorig = cv2.imread(img_path)
WH = np.array(Iorig.shape[1::-1], dtype=float)
Lpeople = []
for i, r in enumerate(P):
cx, cy, w, h = (np.array(r[2]) / np.concatenate((WH, WH))).tolist()
print(cx,cy,w,h)
tl = np.array([cx - w / 2., cy - h / 2.])
tl = [tl[0]-.03,tl[1]-.03]
if tl[0]<0:
tl[0]=0.00
if tl[1]<0:
tl[1]=0.00
br = np.array([cx + w / 2., cy + h / 2.])
br = [br[0]+.03,br[1]+.03]
if br[0]>1:
br[0]=1.00
if br[1]>1:
br[1]=1.00
label = Label(0, tl, br)
Iperson = crop_region(Iorig, label)
print(tl,br)
Lpeople.append(label)
height,width,_ = Iperson.shape
print(height,width)
if width>320:
cv2.imwrite('%s/%s_%dperson.png' % (output_dir_people, bname, i), Iperson)
else:
print('face likely too small to recognize')
print('%s/%s_%dperson.png' % (output_dir_people, bname, i))
# lwrite('%s/%s_cars.txt' % (output_dir, bname), Lcars)
except:
traceback.print_exc()
sys.exit(1)
except:
traceback.print_exc()
sys.exit(1)
elif event.event_type == 'modified':
# Taken any action here when a file is modified.
print("Received modified event - %s." % event.src_path)
frame_rgb = cv2.cvtColor(arr, cv2.COLOR_BGR2RGB)
dn.copy_image_from_bytes(darknet_image, frame_rgb.tobytes())
# im = nparray_to_image(arr)
R = detect_image(vehicle_net, vehicle_meta, darknet_image, thresh=vehicle_threshold)
R = [r for r in R if r[0].decode('utf-8') in ['car', 'bus', 'truck']]
if len(R):
WH = np.array(arr.shape[1::-1], dtype=float)
Lcars = []
for i, r in enumerate(R):
cx, cy, w, h = (np.array(r[2]) / np.concatenate((WH, WH))).tolist()
tl = np.array([cx - w / 2., cy - h / 2.])
br = np.array([cx + w / 2., cy + h / 2.])
label = Label(0, tl, br)
Lcars.append(label)
Icar = crop_region(arr, label)
# print('Searching for license plates using WPOD-NET')
ratio = float(max(Icar.shape[:2])) / min(Icar.shape[:2])
side = int(ratio * 288.)
bound_dim = min(side + (side % (2 ** 4)), 608)
# print("\t\tBound dim: %d, ratio: %f" % (bound_dim, ratio))
Llp, LlpImgs, _ = detect_lp(wpod_net, Icar / 255, bound_dim, 2 ** 4, (240, 80),
0.5)
if len(LlpImgs):
Ilp = LlpImgs[0]
res, confidence = ocrmodel.recognizeOneframe(Ilp * 255.)
pts = Llp[0].pts * label.wh().reshape(2, 1) + label.tl().reshape(2, 1)
ptspx = pts * np.array(arr.shape[1::-1], dtype=float).reshape(2, 1)
draw_losangle(arr, ptspx, RED, 3)
if confidence > 0.5:
if len(R):
Iorig = frame # 原始帧图片
WH = np.array(Iorig.shape[1::-1], dtype=float)
Lcars = []
for i, r in enumerate(R):
cx, cy, w, h = (np.array(r[2]) / np.concatenate(
(WH, WH))).tolist()
tl = np.array([cx - w / 2., cy - h / 2.])
br = np.array([cx + w / 2., cy + h / 2.])
label = Label(0, tl, br)
print(label.wh())
print(label.tl())
Icar = crop_region(Iorig,
label) # 截取车辆区域 Icar 车辆 label 车辆坐标信息
Icar = Icar.astype('uint8')
Lcars.append(label)
draw_label(Iorig, label, color=YELLOW, thickness=3)
# lp detector
print('Searching for license plates using WPOD-NET')
ratio = float(max(Icar.shape[:2])) / min(Icar.shape[:2])
side = int(ratio * 288.)
bound_dim = min(side + (side % (2**4)), 608)
print("\t\tBound dim: %d, ratio: %f" % (bound_dim, ratio))
Llp, LlpImgs, elapse = detect_lp(wpod_net, im2single(Icar),
bound_dim, 2**4,
R, _ = detect(lp_net,
lp_meta,
img_path.encode('utf-8'),
thresh=lp_threshold)
#R = [r for r in R if r[0] in ['lp']]
if len(R):
Iorig = cv2.imread(img_path)
WH = np.array(Iorig.shape[1::-1], dtype=float)
Llp = []
for i, r in enumerate(R):
cx, cy, w, h = (np.array(r[2]) / np.concatenate(
(WH, WH))).tolist()
tl = np.array([cx - w / 2., cy - h / 2.])
br = np.array([cx + w / 2., cy + h / 2.])
label = Label(0, tl, br)
Ilp = crop_region(Iorig, label)
Llp.append(label)
cv2.imwrite('%s/%s_lp.png' % (output_dir, bname), Ilp)
lwrite('%s/%s_lp.txt' % (output_dir, bname), Llp)
except:
traceback.print_exc()
sys.exit(1)
sys.exit(0)
def detect(self, img, bname, pixel_threshold=100000):
output_dir = "output/tmp"
boxes = []
classes_id = []
confidences = []
scale = 0.00392
blob = cv2.dnn.blobFromImage(img,
scalefactor=scale,
size=(416, 416),
mean=(0, 0, 0),
swapRB=True,
crop=False)
height, width = img.shape[:2]
# take image to model
self.model.setInput(blob)
# run forward
outputs = self.model.forward(utils.get_output_layers(self.model))
for out in outputs:
for detection in out:
scores = detection[5:]
class_id = np.argmax(scores)
confidence = float(scores[class_id])
if confidence > self.threshold:
# coordinate of bounding boxes
center_x = int(detection[0] * width)
center_y = int(detection[1] * height)
w = int(detection[2] * width)
h = int(detection[3] * height)
#cv2.circle(img,(center_x,center_y),10,(0,255,0),2)
#rectangle co-ordinaters
x = int(center_x - w / 2)
y = int(center_y - h / 2)
#cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
boxes.append([x, y, w, h])
classes_id.append(class_id)
confidences.append(confidence)
indices = cv2.dnn.NMSBoxes(boxes,
confidences,
score_threshold=self.threshold,
nms_threshold=0.3)
R = []
for i in range(len(boxes)):
if i in indices:
x, y, w, h = boxes[i]
label = str(self.labels[classes_id[i]])
R.append((label, confidences[i], (x, y, w, h)))
R = sorted(R, key=lambda x: -x[1])
R = [r for r in R if r[0] in ['car', 'bus']]
cars_img = []
Lcars = []
if len(R):
# Iorig = cv2.imread(img_path)
Iorig = img
WH = np.array(Iorig.shape[1::-1], dtype=float)
for i, r in enumerate(R):
# print("car size: w={}, h={}, area={}", r[2][2], r[2][3], r[2][2] * r[2][3])
_, _, ori_w, ori_h = r[2]
if ori_w * ori_h >= pixel_threshold:
cx, cy, w, h = (np.array(r[2]) / np.concatenate(
(WH, WH))).tolist()
tl = np.array([cx, cy])
br = np.array([cx + w, cy + h])
label = Label(0, tl, br)
Icar = crop_region(Iorig, label)
Lcars.append(label)
# cv2.imwrite('{}/{}_{}car.png'.format(output_dir, bname, i), Icar)
cars_img.append(Icar)
# lwrite('{}/{}_cars.txt'.format(output_dir, bname), Lcars)
print('\t\t{} cars found by using detect_vehicle'.format(
len(cars_img)))
return cars_img, Lcars
