def stream(cfg,
classes_file,
weights,
socket_ip,
socket_port,
image_size=128,
confidence_threshold=0.6,
nms_thres=0.5):
print('+ Initializing model')
model = Darknet(cfg, image_size)
print('+ Loading model')
load_darknet_weights(model, weights)
print('+ Fusing model')
model.fuse()
print('+ Loading model to CPU')
model.to('cpu').eval()
print('+ Loading webcam')
cap = LoadKinect(img_size=image_size)
print('+ Loading classes')
classes = load_classes(classes_file)
colors = [[random.randint(0, 255) for _ in range(3)]
for _ in range(len(classes))]
print('+ Connecting to remote socket')
global sock
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.connect((socket_ip, socket_port))
print('+ Enumerating cam')
for counter, (path, img, im0, vid_cap) in enumerate(cap):
t = time.time()
print('+ Loading image to CPU')
img = torch.from_numpy(img).unsqueeze(0).to('cpu')
pred, _ = model(img)
print('+ Detecting objects')
det = non_max_suppression(pred, confidence_threshold, nms_thres)[0]
if det is not None and len(det) > 0:
detected_classes = []
print('+ Rescaling model')
det[:, :4] = scale_coords(img.shape[2:], det[:, :4],
im0.shape).round()
print('+ Reading depth')
depth = get_depth()
depth_swap = np.swapaxes(depth, 0, 1)
depth_strip1d = np.array([
np.sort(stripe)[100] for stripe in depth_swap
]).astype(np.uint8)
depth_strip2d_swap = np.array([
np.ones(depth_swap.shape[1]) * depth for depth in depth_strip1d
]).astype(np.uint8)
depth_strip2d = np.swapaxes(depth_strip2d_swap, 0, 1)
depth_edge1d = np.zeros(depth_strip1d.shape)
state = False
for counter, _ in np.ndenumerate(depth_edge1d[:-1]):
state = True if not state and depth_strip1d[
counter] < 230 else False
depth_edge1d[counter[0]] = not state
state = False
state_cnt = 0
for counter, _ in np.ndenumerate(depth_edge1d[:-1]):
counter = counter[0]
if depth_edge1d[counter] == state:
state_cnt += 1
else:
if state_cnt < 10:
for r in range(max(0, counter - 10), counter):
depth_edge1d[counter] = state
state_cnt = 0
state = depth_edge1d[counter]
depth_edge1d = depth_edge1d * 255
depth_edge2d_swap = np.array([
np.ones(100) * awddawd for awddawd in depth_edge1d
]).astype(np.uint8)
depth_edge2d = np.swapaxes(depth_edge2d_swap, 0, 1)
for *coordinates, conf, cls_conf, cls in det:
if classes[int(cls)] in RISKY_CLASSES:
label = '%s %.2f' % (classes[int(cls)], conf)
plot_one_box(coordinates,
im0,
label=label,
color=colors[int(cls)])
print(f"+ Detected {classes[int(cls)]}")
x_avg_depth = np.mean(depth[coordinates[0] -
5:coordinates[0] + 5])
y_avg_depth = np.mean(depth[coordinates[1] -
5:coordinates[1] + 5])
detected_classes.append({
classes[int(cls)]: {
'x': coordinates[0],
'y': coordinates[1],
'z':
np.average(np.array([x_avg_depth, y_avg_depth]))
}
})
n = []
for counter in detected_classes:
width = im0.shape[1]
x, y, z = counter[list(counter.keys())[0]].values()
phi = (x / width * 2 - 1) * (CAMERA_FOV / 2)
n.append(f"{list(counter.keys())[0]};{phi};{z}|")
sock.send(''.join(str(x) for x in n)[:-1].encode('utf-8'))
print('+ Cycle took %.3fs' % (time.time() - t))
plt.imshow(bgr_to_rgb(im0))
plt.show(block=False)
plt.pause(.001)
def app():
cfg = 'ml-data/yolov3.cfg'
global image_size
image_size = 320
weights = 'ml-data/weights/yolov3.weights'
classes_file = 'ml-data/classes.txt'
socket_ip = '10.10.10.1'
# socket_ip = '127.0.0.1'
socket_port = 1337
print('+ Initializing model')
global model
model = Darknet(cfg, image_size)
print('+ Loading model')
load_darknet_weights(model, weights)
print('+ Fusing model')
model.fuse()
print('+ Loading model to CPU')
model.to('cpu').eval()
print('+ Loading classes')
global classes
classes = load_classes(classes_file)
global colors
colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(classes))]
print('+ Connecting to remote socket')
global sock
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.connect((socket_ip, socket_port))
while 1:
#
# Depth
#
depth_result = analyse_depth()
depth_raw = depth_result["raw"]
depth_done = depth_result["done"]
depth_objects = depth_result["objects"]
#
# RGB
#
rgb_result = analyse_rgb()
rgb_raw = rgb_result["raw"]
rgb_done = rgb_result["done"]
rgb_objects = rgb_result["objects"]
print("FRAME [D]: " + depth_objects)
print("FRAME [C]: " + rgb_objects)
sock.send(bytes(f"{rgb_objects}|{depth_objects}".encode('utf-8')))
time.sleep(0.01)
# Plot
vbar = np.zeros((depth_raw.shape[0], 5, 3)).astype(np.uint8)
depthbar = np.concatenate((depth_raw, vbar, depth_done), axis=1)
rgbbar = np.concatenate((rgb_raw, vbar, rgb_done), axis=1)
hbar = np.zeros((5, depthbar.shape[1], 3)).astype(np.uint8)
cv2.imshow('LineWarn', np.concatenate((depthbar, hbar, rgbbar), axis=0))
if cv2.waitKey(10) == 27:
break
