def start(self):
"""
Starts sending the stream to the Viewer.
Creates a camera, takes a image frame converts the frame to string and sends the string across the network
:return: None
"""
print("Streaming Started...")
camera = Camera()
camera.start_capture()
self.keep_running = True
while self.footage_socket and self.keep_running:
try:
time.sleep(
0.05
) # Sleep to decrease processing time per frame, VERY EFFECTIVE
frame = camera.current_frame.read() # grab the current frame
scale_percent = 100 # percent of original size, SET BY YOUR DESIRE
width = int(frame.shape[1] * scale_percent / 100)
height = int(frame.shape[0] * scale_percent / 100)
dim = (width, height)
frameSmall = cv2.resize(frame,
dim,
interpolation=cv2.INTER_AREA)
frameSmall = cv2.cvtColor(
frameSmall, cv2.COLOR_BGR2GRAY
) #Convert the image to grey scale, decreasing processing time and network trafic, YOU CAN DELETE THIS LINE TO GET A COLOR IMAGE
image_as_string = image_to_string(frameSmall)
self.footage_socket.send(image_as_string)
except KeyboardInterrupt:
cv2.destroyAllWindows()
break
print("Streaming Stopped!")
cv2.destroyAllWindows()
def start(self, framerate):
"""
Starts sending the stream to the Viewer.
Creates a camera, takes a image frame converts the frame to string and sends the string across the network
:return: None
"""
self.framerates = [framerate / 3., framerate]
print("Streaming Started...")
camera = Camera()
camera.start_capture()
self.keep_running = True
id = 0
separator = "______".encode()
time.sleep(2)
start = time.time()
while self.footage_socket and self.footage_socket_tiny and self.keep_running:
try:
try:
normal_framerate = int(
self.footage_socket_tiny.recv(flags=zmq.NOBLOCK))
framerate = self.framerates[normal_framerate]
except zmq.error.Again:
pass
time.sleep(0.6 / framerate)
frame = camera.current_frame.read() # grab the current frame
small_frame = resize_img(frame)[0] # resize
image_as_string = image_to_string(frame) # encode the frame
small_image_as_string = image_to_string(
small_frame) # encode the small frame
self.footage_socket_viewer.send(image_as_string + separator +
str(id).encode())
self.footage_socket.send(
small_image_as_string + separator + str(id).encode() +
separator + str(round(time.time(), 2)).encode()) # send it
id += 1
print('Frame: %d, framerate: %2.2f fps' %
(id, round(id / (time.time() - start), 2)))
except KeyboardInterrupt:
cv2.destroyAllWindows()
break
print("Streaming Stopped!")
cv2.destroyAllWindows()
def frame_capture():
global buffer
camera = Camera()
camera.start_capture()
while True:
frame = camera.current_frame.read() # grab the current frame
frame = rescale_frame(frame)
buffer.append(image_to_string(frame))
print(len(buffer))
def main():
person_group_id = FACE_GROUP_ID
display_image = False
face_api_wrapper = FaceAPIWrapper(FACE_API_KEY, FACE_BASE_URL)
create_dir_if_not_exists('captured_images/' + CAMERA_NAME)
print("Capturing Image every ", CAPTURE_INTERVAL, " seconds...")
i = 0
while 1:
try:
image_filename = 'captured_images/' + CAMERA_NAME + "/" + current_time_to_string() + ".jpg"
image = Camera().capture_image()
cv2.imwrite(image_filename, image)
if display_image:
cv2.imshow("Camera Image", image)
cv2.waitKey(1)
image_link = upload_to_s3(image_filename)
image_url = 'https://mybuckfucket.s3-eu-west-1.amazonaws.com/public_folder/'+image_filename
face_ids = face_api_wrapper.detect_faces(image=image_filename)
i += 1
print(i, "Captured at ", current_time_to_string())
print("S3 url",image_url)
if face_ids:
person_ids = \
face_api_wrapper.identify_faces(face_ids=face_ids,
large_person_group=person_group_id)
req_ids = [{id} for id in person_ids]
print("Detected Faces...", req_ids)
contents=load_dict_from_file('json.txt')
#my_data=(contents[0]['faceAttributes'])
requests.post('http://127.0.0.1:8000/students/', data={
'attributes' : contents,
'name' : "Darragh",
'time_date' : current_time_to_string(),
'face_ids' : person_ids,
'image_link' : image_url,
})
print("#####",data)
#print(contents[0]['faceAttributes']['emotion'])
#requests.post('http://127.0.0.1:8000/students/', data=contents)
time.sleep(CAPTURE_INTERVAL)
except Exception as e:
print(e)
def main():
init_logging()
print("Starting model")
cam = Camera(resolution=(1024, 768))
model = CNN(cam, 'cnn/model/complexCNN.h5')
# Capture image stream and classify the parking spots
while True:
model.detect()
time.sleep(5)
# When everything is done, release the capture
cv2.destroyAllWindows()
def main():
info = {"parkspot_id": "", "token": ""}
# Load Parkspot ID and Token
infoPath = Path(settings.infoFile)
if infoPath.is_file():
info = (pickle.load(open(settings.infoFile, "rb")))
tokenAvailable = True
if (info['token'] == ""):
tokenAvailable = False
networkAvailable = hasNetwork()
if networkAvailable is False or tokenAvailable is False:
setupParkspot(info)
api = ParkspotApi(info)
# Create a new parkspot if we did not create one yet
if info["parkspot_id"] == "":
if api.createParkspot() is False:
print("Could not create Parkspot. Resetting")
reset()
pickle.dump(info, open(settings.infoFile, "w+b"))
# Tell the backend our IP
if api.updateLocalIp() is False:
print("Could update IP. Resetting")
shutdown()
# Initialize the camera and the model
print("Starting model")
cam = Camera(usePiCamera=False, resolution=(1280, 720))
model = CNN(cam, 'cnn/model/complexCNN.h5', api)
# Create the parkspot grpc service
service = ParkspotService(cam, model, api)
# Run model
while True:
model.detect()
time.sleep(5)
# When everything is done, release everything
cv2.destroyAllWindows()
service.stop()
def main():
person_group_id = FACE_GROUP_ID
display_image = False
face_api_wrapper = FaceAPIWrapper(FACE_API_KEY, FACE_BASE_URL)
create_dir_if_not_exists('captured_images/' + CAMERA_NAME)
print("Capturing Image every ", CAPTURE_INTERVAL, " seconds...")
i = 0
while 1:
try:
image_filename = 'captured_images/' + CAMERA_NAME + "/" + current_time_to_string(
) + ".jpg"
image = Camera().capture_image()
cv2.imwrite(image_filename, image)
if display_image:
cv2.imshow("Camera Image", image)
cv2.waitKey(1)
image_link = upload_to_s3(image_filename)
face_ids = face_api_wrapper.detect_faces(image=image_filename)
i += 1
print(i, "Captured at ", time.time())
if face_ids:
person_ids = \
face_api_wrapper.identify_faces(face_ids=face_ids,
large_person_group=person_group_id)
req_ids = [{id} for id in person_ids]
print("Detected Faces...", req_ids)
requests.post(REST_SERVER_URL + 'time-face-id',
data={
'lecture_number': get_lecture_number(),
'face_ids': req_ids,
'image-link': image_link,
'camera-name': CAMERA_NAME,
})
print("Present IDs:", req_ids)
time.sleep(CAPTURE_INTERVAL)
except Exception as e:
print(e)
def start(self):
"""
Starts sending the stream to the Viewer.
Creates a camera, takes a image frame converts the frame to string and sends the string across the network
:return: None
"""
print("Streaming Started...")
camera = Camera()
camera.start_capture()
self.keep_running = True
while self.footage_socket and self.keep_running:
try:
frame = camera.current_frame.read() # grab the current frame
image_as_string = image_to_string(frame)
self.footage_socket.send(image_as_string)
except KeyboardInterrupt:
cv2.destroyAllWindows()
break
print("Streaming Stopped!")
cv2.destroyAllWindows()
import os
import time
from api.API import APIClient
from camera.Camera import Camera
from flask import Flask
app = Flask(__name__)
SAVE_PICTURE_PATH = "/data"
cam = Camera()
@app.route('/picture')
def take_picture():
picture_path = cam.shoot(SAVE_PICTURE_PATH, "python-test")
print("Picture taked")
api = APIClient("https://api.pizzia.k8s.jeremychauvin.fr")
api.predict(picture_path)
def test_camera_image(self):
self.assertIsInstance(Camera().capture_image(), numpy.ndarray)
def setUpClass(cls):
super(TestCameraStream, cls).setUpClass()
cls.camera = Camera()
cls.camera.start_capture()
def test_camera_image_not_null(self):
self.assertIsNotNone(Camera().capture_image())
from camera.Camera import Camera c = Camera() c.rotate(300)
def main():
person_group_id = 'students'
display_image = False
face_api_wrapper = FaceAPIWrapper("##MY_KEY###", 'https://projectface.cognitiveservices.azure.com/face/v1.0')
create_dir_if_not_exists('capturedImages/' + "Camera 1")
print("Capture Image every ", 60, " seconds...")
i = 0
while 1:
try:
image_filename = 'capturedImages/' + "Camera 1" + "/" + current_time_to_string() + ".jpg"
image = Camera().capture_image()
cv2.imwrite(image_filename, image)
if display_image:
cv2.imshow("Camera Image", image)
cv2.waitKey(1)
image_link = upload_to_s3(image_filename)
face_ids = face_api_wrapper.detect_faces(image=image_filename)
i += 1
print(i, "Captured at ", time.time())
if face_ids:
person_ids = \
face_api_wrapper.identify_faces(face_ids=face_ids,
large_person_group=person_group_id)
req_ids = [{id} for id in person_ids]
print("Detected Faces...", req_ids
time.sleep(60)
except Exception as e:
print(e)
if __name__ == '__main__':
# initial_setup()
main()
def current_time_to_string():
from datetime import datetime
return datetime.now().strftime("%Y%m%d_%H%M%S%f")
def upload_to_s3(key):
print("Uploading file to S3...")
bucket_name = 'mybuckfucket'
folder_name = "public_folder"
output_name = folder_name + "/" + key
location = 'us-west-1'
s3 = boto3.client('s3')
s3.upload_file(key, bucket_name, output_name, ExtraArgs={'ACL': 'public-read'})
url = "https://s3.amazonaws.com/%s/%s/%s" % (bucket_name, folder_name, key)
return url
def create_dir_if_not_exists(output_dir):
try:
os.makedirs(output_dir)
except OSError:
if not os.path.isdir(output_dir):
raise