def gen():
while True:
ret, frame = vid.read()
img = cv2.imencode('.jpg', frame)[1].tobytes()
detected_faces = utils.detect_face_stream(endpoint=ENDPOINT,
key=KEY,
image=img)
print('Image num face detected {}'.format(detected_faces))
color = (255, 0, 0)
thickness = 2
for face in detected_faces:
print(face)
frame = cv2.rectangle(frame, *utils.get_rectangle(face), color,
thickness)
img_send = cv2.imencode('.jpg', frame)[1].tobytes()
time.sleep(1)
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + img_send + b'\r\n')
def identify_from_video(endpoint, key, group_id, frame):
""" Main function to detect faces in 'frame' and detect if its are contained in the 'group_id'
@param endpoint: Endpoint Azure Service
@type endpoint: str
@param key: key connection of Azure Service
@type key: str
@param group_id: unique identify of the group or database
@type group_id: str
@param frame: Frame Image
@type frame: numpy.ndarray
@return: (frame image, string name)
@rtype: (numpy.ndarray, str or None)
"""
start_measure = time.time()
thickness = 2
detected_faces, sleep_time = detect_face(endpoint, key, frame)
print(detected_faces)
if sleep_time == 10:
time.sleep(sleep_time)
if detected_faces is not None:
faces_info = identify_and_process(detected_faces, endpoint, key,
group_id)
print('Detected: {} and Info {}'.format(detected_faces, faces_info))
for face, info in zip(detected_faces, faces_info):
if info['confidence'] > 0.5:
color = (0, 255, 0)
else:
color = (0, 0, 255)
frame = cv2.rectangle(frame, *utils.get_rectangle(face), color,
thickness)
print('Total time required:', time.time() - start_measure)
return frame, faces_info
print('Total time required:', time.time() - start_measure)
return frame, None
def __create_castle(self):
"""
Randomly place a castle in a map.
Private method.
"""
# find a random origin point which doesn't sit on a river
start_x = random.randint(0, self.GRID_X-1)
start_y = random.randint(0, self.GRID_Y-1)
while self.map[start_x][start_y]["river"]:
self.__create_castle()
return
# create endpoint.
end_x = start_x + self.CASTLE_SIZE - 1
end_y = start_y + self.CASTLE_SIZE - 1
# test if castle sits in the field and not on river
if start_x > self.GRID_X-1 or start_y > self.GRID_Y-1:
self.__create_castle()
return
if end_x > self.GRID_X-1 or end_y > self.GRID_Y-1:
self.__create_castle()
return
if self.map[end_x][end_y]["river"]:
self.__create_castle()
return
# get points of the castle and put them on map
castle_points = utils.get_rectangle((start_x, start_y), (end_x, end_y))
for point in castle_points:
# start again if river inside castle
if self.map[point[0]][point[1]]["river"]:
self.__create_castle()
return
for point in castle_points:
self.map[point[0]][point[1]]["castle"] = True
def gen():
global start_time, fps_time, fps
camera = cv2.VideoCapture(0)
camera.set(cv2.CAP_PROP_FRAME_WIDTH, 320)
camera.set(cv2.CAP_PROP_FRAME_HEIGHT, 240)
count = 0
while True:
if (time.perf_counter() - fps_time) >= 1 / fps:
ret, frame = camera.read()
count += 1
fps_time = time.perf_counter()
if ret:
img = cv2.imencode('.jpg', frame)[1].tobytes()
if (time.perf_counter() - start_time) >= 3:
detected_faces = utils.detect_face_stream(
endpoint=ENDPOINT, key=KEY, image=img)
start_time = time.perf_counter()
print('Image num {} face detected {}'.format(
count, detected_faces))
color = (255, 0, 0)
thickness = 2
for face in detected_faces:
print(face)
frame = cv2.rectangle(frame, *utils.get_rectangle(face),
color, thickness)
img = cv2.imencode('.jpg', frame)[1].tobytes()
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + img + b'\r\n')
else:
break
xy_lim[3] + padding
])
# scatter points
xy = controller.points
xs = [i[0] for i in xy]
ys = [i[1] for i in xy]
ax1.scatter(xs, ys, color='red', s=1)
# calculate intersections to plot
intersections = controller.get_intersections()
for points in intersections:
if points is not None:
plt.plot([i[0] for i in points], [i[1] for i in points],
color='pink',
linewidth=3)
# draw rectangle for isolated parts
rects = [get_rectangle(p.points) for p in controller.parts if p.isolated]
for rect in rects:
for edge in rect:
plt.plot([i[0] for i in edge], [i[1] for i in edge],
color='pink',
linewidth=3)
# formatting the plot
# ax1.text(-0.9, 3.85, 'Variance: {:8.4f}'.format(variance(regressor.points, regressor.parameters, regressor.segments)), fontsize=10)
# ax1.set_title("Segmented Linear Regression(segments = {})".format(segments))
# plt.savefig("Segemented_Linear_Regression_segments_{}.png".format(str(segments)), dpi=300)
plt.show()
# create a video object and configure size of the output image
vid = cv2.VideoCapture(0)
vid.set(cv2.CAP_PROP_FRAME_WIDTH, 320)
vid.set(cv2.CAP_PROP_FRAME_HEIGHT, 240)
count = 0
while True:
ret, frame = vid.read()
img = cv2.imencode('.jpg', frame)[1].tobytes()
detected_faces = utils.detect_face_stream(endpoint=ENDPOINT, key=KEY, image=img)
print('Image num {} face detected {}'.format(count, detected_faces))
count += 1
color = (255, 0, 0)
thickness = 2
for face in detected_faces:
print(face)
frame = cv2.rectangle(frame, *utils.get_rectangle(face), color, thickness)
# Display the resulting frame
cv2.imshow('frame', frame)
# the 'q' button is set as the quitting button
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# wait 3 second to accomplish azure free tier service
time.sleep(3)
# After the loop release the cap object
vid.release()
# Destroy all the windows
cv2.destroyAllWindows()
# configure the face client
KEY = args['KEY']
ENDPOINT = 'https://{0}.cognitiveservices.azure.com/'.format(
args['SERVICE_NAME'])
face_client = FaceClient(ENDPOINT, CognitiveServicesCredentials(KEY))
# make prediction from URL
multi_face_image_url = "http://www.historyplace.com/kennedy/president-family-portrait-closeup.jpg"
multi_image_name = os.path.basename(multi_face_image_url)
detected_faces = face_client.face.detect_with_url(url=multi_face_image_url)
# make prediction from URL
local_image_path = os.path.join(os.getcwd(), 'data',
'president-family.jpg')
image = open(local_image_path, 'r+b')
detected_faces = face_client.face.detect_with_stream(image)
# download the image from web source
response = requests.get(multi_face_image_url)
img = Image.open(BytesIO(response.content))
# For each face returned use the face rectangle and draw a red box.
draw = ImageDraw.Draw(img)
for face in detected_faces:
print(face)
draw.rectangle(utils.get_rectangle(face), outline='red')
# Display the image in the users default image browser.
img.show()
local_image = cv2.imread(image_path)
img = cv2.imencode('.jpg', local_image)[1].tobytes()
# Function to call the API REST with local image
attributes = ''
detected_faces = utils.detect_face_stream(endpoint=ENDPOINT,
key=KEY,
image=img,
face_attributes=attributes)
# Function to call the API REST with web image
image_url = "http://www.historyplace.com/kennedy/president-family-portrait-closeup.jpg"
detected_faces = utils.detect_face_url(endpoint=ENDPOINT,
key=KEY,
image_url=image_url,
face_attributes=attributes)
# Display the resulting frame
color = (255, 0, 0)
thickness = 2
for face in detected_faces:
print(face)
local_image = cv2.rectangle(local_image, *utils.get_rectangle(face),
color, thickness)
cv2.imshow('frame', local_image)
cv2.waitKey()
# Destroy all the windows
cv2.destroyAllWindows()
print('Result of face: {0}.'.format(person['faceId']))
face = [
face for face in detected_faces
if face['faceId'] == person['faceId']
][0]
if not len(person['candidates']) == 0:
candidate = person['candidates'][0]
print('Identified in {} with a confidence: {}.'.format(
person['faceId'], candidate['confidence']))
person_info = utils.get_person_info(ENDPOINT, KEY, GROUP_ID,
candidate['personId'])
print('Name Group person identified: {0}'.format(
person_info['name']))
color = (0, 255, 0)
local_image = cv2.rectangle(local_image,
*utils.get_rectangle(face), color,
thickness)
x, y = face['faceRectangle'][
'left'], face['faceRectangle']['top'] - 5
cv2.putText(local_image, person_info['name'], (x, y),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 1)
else:
print('no match found')
color = (255, 0, 0)
local_image = cv2.rectangle(local_image,
*utils.get_rectangle(face), color,
thickness)
cv2.imshow('frame', local_image)
cv2.waitKey()
