def test_init_start_stop(self):
"""Test camera's thread init/start/stop methods."""
camera = Camera()
camera.start()
time.sleep(1)
image = bgr8_to_jpeg(camera.value)
camera.stop()
# Check if image changed after stopping
assert image == bgr8_to_jpeg(camera.value)
def execute(change):
image = change['new']
# execute collision model to determine if blocked
collision_output = collision_model(preprocess(image)).detach().cpu()
prob_blocked = float(F.softmax(collision_output.flatten(), dim=0)[0])
blocked_widget.value = prob_blocked
# turn left if blocked
if prob_blocked > 0.5:
robot.left(0.3)
image_widget.value = bgr8_to_jpeg(image)
return
# compute all detected objects
detections = model(image)
# draw all detections on image
for det in detections[0]:
bbox = det['bbox']
cv2.rectangle(image, (int(width * bbox[0]), int(height * bbox[1])),
(int(width * bbox[2]), int(height * bbox[3])),
(255, 0, 0), 2)
# select detections that match selected class label
matching_detections = [
d for d in detections[0] if d['label'] == int(label_widget.value)
]
# get detection closest to center of field of view and draw it
det = closest_detection(matching_detections)
if det is not None:
bbox = det['bbox']
cv2.rectangle(image, (int(width * bbox[0]), int(height * bbox[1])),
(int(width * bbox[2]), int(height * bbox[3])),
(0, 255, 0), 5)
# otherwise go forward if no target detected
if det is None:
robot.forward(float(speed_widget.value))
# otherwsie steer towards target
else:
# move robot forward and steer proportional target's x-distance from center
center = detection_center(det)
robot.set_motors(
float(speed_widget.value + turn_gain_widget.value * center[0]),
float(speed_widget.value - turn_gain_widget.value * center[0]))
# update image widget
image_widget.value = bgr8_to_jpeg(image)
def display_xy(camera_image):
image = np.copy(camera_image)
x = x_slider.value
y = y_slider.value
x = int(x * 224 / 2 + 112)
y = int(y * 224 / 2 + 112)
image = cv2.circle(image, (x, y), 8, (0, 255, 0), 3)
image = cv2.circle(image, (112, 224), 8, (0, 0,255), 3)
image = cv2.line(image, (x,y), (112,224), (255,0,0), 3)
jpeg_image = bgr8_to_jpeg(image)
return jpeg_image
layout=widgets.Layout(align_self='center'))
# In[33]:
display(image_box)
# In[105]:
count = 0
seta = 0
while True:
img = camera.value
try:
img_result, img_op, seta = search_road(img, seta)
print(seta, end='\t')
result1.value = bgr8_to_jpeg(img_result)
result2.value = bgr8_to_jpeg(img_op)
pw = 1.2
w = (seta / 90)
left_power = pw * (0.1 + w * 0.1)
right_power = pw * (0.1 - w * 0.1)
robot.set_motors(left_power, right_power)
# time.sleep(0.5)
# robot.stop()
except:
print('not Found', end='\r')
robot.stop()
input_image.value = bgr8_to_jpeg(img)
if count == 120:
break
else:
def execute(change):
global ping_counter
ping_counter += 1
if ping_counter > 60:
logger.info("still observing")
ping_counter = 0
image = change['new']
resized_image = image
if model_width != camera_width and model_height != camera_height:
resized_image = cv2.resize(image, (model_width, model_height),
interpolation=cv2.INTER_AREA)
# execute collision model to determine if blocked
#collision_output = collision_model(preprocess(image,model_224,224)).detach().cpu()
#prob_blocked = float(F.softmax(collision_output.flatten(), dim=0)[0])
# turn left if blocked
#if prob_blocked > 0.5:
# robot.left(0.4)
# return
# compute all detected objects
detections = model(resized_image)
# draw all detections on image
# for det in detections[0]:
# bbox = det['bbox']
# cv2.rectangle(image, (int(width * bbox[0]), int(height * bbox[1])), (int(width * bbox[2]), int(height * bbox[3])), (255, 0, 0), 2)
# select detections that match selected class label
matching_detections = [
d for d in detections[0]
if d['label'] in v2_coco_labels_to_capture and d['confidence'] > 0.50
]
tf_image_list = []
if len(matching_detections) > 0:
logger.info(matching_detections)
filename = str(uuid1())
for d in matching_detections:
bbox = d['bbox']
xmin = int(xscale * bbox[0])
ymin = int(yscale * bbox[1])
xmax = int(xscale * bbox[2])
ymax = int(yscale * bbox[3])
if xmin < 0:
xmin = 0
if ymin < 0:
ymin = 0
if xmax > camera_width:
xmax = camera_width - 1
if ymax > camera_height:
ymax = camera_height - 1
tf_image_desc = [
filename + '.jpg', camera_width, camera_height,
int(d['label']), xmin, ymin, xmax, ymax
]
tf_image_list.append(tf_image_desc)
save_image(bgr8_to_jpeg(image), filename, tf_image_list)
# get detection closest to center of field of view and center bot
det = closest_detection(matching_detections)
if det is not None:
center = detection_center(det)
logger.debug("center: %s", center)
move_speed = 2.0 * center[0]
if abs(move_speed) > 0.3:
if move_speed > 0.0:
robot.right(move_speed)
else:
robot.left(abs(move_speed))
else:
robot.stop()
if logger.isEnabledFor(logging.DEBUG) and (len(detections[0])) > 0:
logger.debug(detections[0])
import os
import time
from jetbot import Camera, bgr8_to_jpeg
interval_seconds = 5
directory = 'images'
camera = Camera()
# count = 0
# while True:
file_name = os.path.join(directory, 'image_1.jpeg')
with open(file_name, 'wb') as f:
f.write(bgr8_to_jpeg(camera.value))
# count += 1
# time.sleep(interval_seconds)
def button_proc(change):
camera_image.value = bgr8_to_jpeg(camera.value)