Hi guys does anyone work with Jetson Nano?
I added here Сayenne.
(general introduction, overall experience with Cayenne, why did you create this project??)
import face_recognition
import cv2
from datetime import datetime, timedelta
import numpy as np
import platform
import pickle
import RPi.GPIO as GPIO
import cayenne.client
import time
# Cayenne authentication info. This should be obtained from the Cayenne Dashboard.
MQTT_USERNAME = "39a5ef50-a6bb-11e6-a85d-c165103f15c2"
MQTT_PASSWORD = "7a77d955ff9c0d1e6c2b394062801df05b2c645b"
MQTT_CLIENT_ID = "742cff30-21b3-11ea-b73d-1be39589c6b2"
# The callback for when a message is received from Cayenne.
def on_message(message):
print("message received: " + str(message))
# If there is an error processing the message return an error string, otherwise return nothing.
client = cayenne.client.CayenneMQTTClient()
client.on_message = on_message
client.begin(MQTT_USERNAME, MQTT_PASSWORD, MQTT_CLIENT_ID)
# For a secure connection use port 8883 when calling client.begin:
# client.begin(MQTT_USERNAME, MQTT_PASSWORD, MQTT_CLIENT_ID, port=8883)
i = 0
timestamp = 0
# Our list of known face encodings and a matching list of metadata about each face.
known_face_encodings = []
known_face_metadata = []
def save_known_faces():
with open("known_faces.dat", "wb") as face_data_file:
face_data = [known_face_encodings, known_face_metadata]
pickle.dump(face_data, face_data_file)
print("Known faces backed up to disk.")
def load_known_faces():
global known_face_encodings, known_face_metadata
try:
with open("known_faces.dat", "rb") as face_data_file:
known_face_encodings, known_face_metadata = pickle.load(face_data_file)
print("Known faces loaded from disk.")
except FileNotFoundError as e:
print("No previous face data found - starting with a blank known face list.")
pass
def running_on_jetson_nano():
# To make the same code work on a laptop or on a Jetson Nano, we'll detect when we are running on the Nano
# so that we can access the camera correctly in that case.
# On a normal Intel laptop, platform.machine() will be "x86_64" instead of "aarch64"
return platform.machine() == "aarch64"
def get_jetson_gstreamer_source(capture_width=1280, capture_height=720, display_width=1280, display_height=720, framerate=60, flip_method=0):
"""
Return an OpenCV-compatible video source description that uses gstreamer to capture video from the camera on a Jetson Nano
"""
return (
f'nvarguscamerasrc ! video/x-raw(memory:NVMM), ' +
f'width=(int){capture_width}, height=(int){capture_height}, ' +
f'format=(string)NV12, framerate=(fraction){framerate}/1 ! ' +
f'nvvidconv flip-method={flip_method} ! ' +
f'video/x-raw, width=(int){display_width}, height=(int){display_height}, format=(string)BGRx ! ' +
'videoconvert ! video/x-raw, format=(string)BGR ! appsink'
)
def register_new_face(face_encoding, face_image):
"""
Add a new person to our list of known faces
"""
# Add the face encoding to the list of known faces
known_face_encodings.append(face_encoding)
# Add a matching dictionary entry to our metadata list.
# We can use this to keep track of how many times a person has visited, when we last saw them, etc.
known_face_metadata.append({
"first_seen": datetime.now(),
"first_seen_this_interaction": datetime.now(),
"last_seen": datetime.now(),
"seen_count": 1,
"seen_frames": 1,
"face_image": face_image,
})
def lookup_known_face(face_encoding):
"""
See if this is a face we already have in our face list
"""
metadata = None
# If our known face list is empty, just return nothing since we can't possibly have seen this face.
if len(known_face_encodings) == 0:
return metadata
# Calculate the face distance between the unknown face and every face on in our known face list
# This will return a floating point number between 0.0 and 1.0 for each known face. The smaller the number,
# the more similar that face was to the unknown face.
face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
# Get the known face that had the lowest distance (i.e. most similar) from the unknown face.
best_match_index = np.argmin(face_distances)
# If the face with the lowest distance had a distance under 0.6, we consider it a face match.
# 0.6 comes from how the face recognition model was trained. It was trained to make sure pictures
# of the same person always were less than 0.6 away from each other.
# Here, we are loosening the threshold a little bit to 0.65 because it is unlikely that two very similar
# people will come up to the door at the same time.
if face_distances[best_match_index] < 0.65:
# If we have a match, look up the metadata we've saved for it (like the first time we saw it, etc)
metadata = known_face_metadata[best_match_index]
# Update the metadata for the face so we can keep track of how recently we have seen this face.
metadata["last_seen"] = datetime.now()
metadata["seen_frames"] += 1
# We'll also keep a total "seen count" that tracks how many times this person has come to the door.
# But we can say that if we have seen this person within the last 5 minutes, it is still the same
# visit, not a new visit. But if they go away for awhile and come back, that is a new visit.
if datetime.now() - metadata["first_seen_this_interaction"] > timedelta(minutes=5):
metadata["first_seen_this_interaction"] = datetime.now()
metadata["seen_count"] += 1
return metadata
def main_loop():
# Get access to the webcam. The method is different depending on if this is running on a laptop or a Jetson Nano.
if running_on_jetson_nano():
# Accessing the camera with OpenCV on a Jetson Nano requires gstreamer with a custom gstreamer source string
video_capture = cv2.VideoCapture(get_jetson_gstreamer_source(), cv2.CAP_GSTREAMER)
else:
# Accessing the camera with OpenCV on a laptop just requires passing in the number of the webcam (usually 0)
# Note: You can pass in a filename instead if you want to process a video file instead of a live camera stream
video_capture = cv2.VideoCapture(0)
# Track how long since we last saved a copy of our known faces to disk as a backup.
number_of_faces_since_save = 0
while True:
# Grab a single frame of video
ret, frame = video_capture.read()
# Resize frame of video to 1/4 size for faster face recognition processing
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_small_frame = small_frame[:, :, ::-1]
# Find all the face locations and face encodings in the current frame of video
face_locations = face_recognition.face_locations(rgb_small_frame)
face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
client.loop()
# Loop through each detected face and see if it is one we have seen before
# If so, we'll give it a label that we'll draw on top of the video.
face_labels = []
for face_location, face_encoding in zip(face_locations, face_encodings):
# See if this face is in our list of known faces.
metadata = lookup_known_face(face_encoding)
# If we found the face, label the face with some useful information.
if metadata is not None:
time_at_door = datetime.now() - metadata['first_seen_this_interaction']
face_label = f"At door {int(time_at_door.total_seconds())}s"
client.virtualWrite(2,1,"digital_sensor","d")
# If this is a brand new face, add it to our list of known faces
else:
face_label = "New visitor!"
# Grab the image of the the face from the current frame of video
top, right, bottom, left = face_location
face_image = small_frame[top:bottom, left:right]
face_image = cv2.resize(face_image, (150, 150))
# Add the new face to our known face data
register_new_face(face_encoding, face_image)
face_labels.append(face_label)
# Draw a box around each face and label each face
for (top, right, bottom, left), face_label in zip(face_locations, face_labels):
# Scale back up face locations since the frame we detected in was scaled to 1/4 size
top *= 4
right *= 4
bottom *= 4
left *= 4
# Draw a box around the face
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
# Draw a label with a name below the face
cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
cv2.putText(frame, face_label, (left + 6, bottom - 6), cv2.FONT_HERSHEY_DUPLEX, 0.8, (255, 255, 255), 1)
# Display recent visitor images
number_of_recent_visitors = 0
for metadata in known_face_metadata:
# If we have seen this person in the last minute, draw their image
if datetime.now() - metadata["last_seen"] < timedelta(seconds=10) and metadata["seen_frames"] > 5:
# Draw the known face image
x_position = number_of_recent_visitors * 150
frame[30:180, x_position:x_position + 150] = metadata["face_image"]
number_of_recent_visitors += 1
# Label the image with how many times they have visited
visits = metadata['seen_count']
visit_label = f"{visits} visits"
if visits == 1:
visit_label = "First visit"
cv2.putText(frame, visit_label, (x_position + 10, 170), cv2.FONT_HERSHEY_DUPLEX, 0.6, (255, 255, 255), 1)
if number_of_recent_visitors > 0:
cv2.putText(frame, "Visitors at Door", (5, 18), cv2.FONT_HERSHEY_DUPLEX, 0.8, (255, 255, 255), 1)
# Display the final frame of video with boxes drawn around each detected fames
cv2.imshow('Video', frame)
# Hit 'q' on the keyboard to quit!
if cv2.waitKey(1) & 0xFF == ord('q'):
save_known_faces()
break
# We need to save our known faces back to disk every so often in case something crashes.
if len(face_locations) > 0 and number_of_faces_since_save > 100:
save_known_faces()
number_of_faces_since_save = 0
else:
number_of_faces_since_save += 1
# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()
if __name__ == "__main__":
load_known_faces()
main_loop()
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