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main.py
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# *****************************************************
# *
# Copyright 2018 Amazon.com, Inc. or its affiliates. *
# All Rights Reserved. *
# *
# *****************************************************
""" A sample lambda for face detection"""
from threading import Thread, Event
import os
import json
import numpy as np
import awscam
import cv2
import greengrasssdk
stache = cv2.imread('stache.png', -1)
class LocalDisplay(Thread):
""" Class for facilitating the local display of inference results
(as images). The class is designed to run on its own thread. In
particular the class dumps the inference results into a FIFO
located in the tmp directory (which lambda has access to). The
results can be rendered using mplayer by typing:
mplayer -demuxer lavf -lavfdopts format=mjpeg:probesize=32 /tmp/results.mjpeg
"""
def __init__(self, resolution):
""" resolution - Desired resolution of the project stream """
# Initialize the base class, so that the object can run on its own
# thread.
super(LocalDisplay, self).__init__()
# List of valid resolutions
RESOLUTION = {'1080p': (1920, 1080), '720p': (1280, 720), '480p': (858, 480)}
if resolution not in RESOLUTION:
raise Exception("Invalid resolution")
self.resolution = RESOLUTION[resolution]
# Initialize the default image to be a white canvas. Clients
# will update the image when ready.
self.frame = cv2.imencode('.jpg', 255 * np.ones([640, 480, 3]))[1]
self.stop_request = Event()
def run(self):
""" Overridden method that continually dumps images to the desired
FIFO file.
"""
# Path to the FIFO file. The lambda only has permissions to the tmp
# directory. Pointing to a FIFO file in another directory
# will cause the lambda to crash.
result_path = '/tmp/results.mjpeg'
# Create the FIFO file if it doesn't exist.
if not os.path.exists(result_path):
os.mkfifo(result_path)
# This call will block until a consumer is available
with open(result_path, 'w') as fifo_file:
while not self.stop_request.isSet():
try:
# Write the data to the FIFO file. This call will block
# meaning the code will come to a halt here until a consumer
# is available.
fifo_file.write(self.frame.tobytes())
except IOError:
continue
def set_frame_data(self, frame):
""" Method updates the image data. This currently encodes the
numpy array to jpg but can be modified to support other encodings.
frame - Numpy array containing the image data tof the next frame
in the project stream.
"""
ret, jpeg = cv2.imencode('.jpg', cv2.resize(frame, self.resolution))
if not ret:
raise Exception('Failed to set frame data')
self.frame = jpeg
def join(self):
self.stop_request.set()
def put_moustache(client, topic, stache, face):
originalFace = face
try:
client.publish(topic=topic, payload='face width {}; face height {}'.format(face.shape[0], face.shape[1]))
resizedStache = cv2.resize(stache, (face.shape[1], face.shape[0]))
client.publish(topic=topic,
payload='resized mustache image to {}:{}'.format(resizedStache.shape[0], resizedStache.shape[1]))
x_offset = y_offset = 0
y1, y2 = y_offset, y_offset + resizedStache.shape[0]
x1, x2 = x_offset, x_offset + resizedStache.shape[1]
alpha_s = resizedStache[:, :, 3] / 255.0
alpha_l = 1.0 - alpha_s
for c in range(0, 3):
face[y1:y2, x1:x2, c] = (alpha_s * resizedStache[:, :, c] + alpha_l * face[y1:y2, x1:x2, c])
return face
except Exception as e:
client.publish(topic=topic, payload='error putting mustache on face; {}'.format(e))
return originalFace
def greengrass_infinite_infer_run():
""" Entry point of the lambda function"""
iot_topic = '$aws/things/{}/infer'.format(os.environ['AWS_IOT_THING_NAME'])
try:
# This face detection model is implemented as single shot detector (ssd).
model_type = 'ssd'
output_map = {1: 'face'}
# Create an IoT client for sending to messages to the cloud.
client = greengrasssdk.client('iot-data')
# Create a local display instance that will dump the image bytes to a FIFO
# file that the image can be rendered locally.
local_display = LocalDisplay('480p')
local_display.start()
# The sample projects come with optimized artifacts, hence only the artifact
# path is required.
model_path = '/opt/awscam/artifacts/mxnet_deploy_ssd_FP16_FUSED.xml'
# Load the model onto the GPU.
client.publish(topic=iot_topic, payload='Loading face detection model')
model = awscam.Model(model_path, {'GPU': 1})
client.publish(topic=iot_topic, payload='Face detection model loaded')
# Set the threshold for detection
detection_threshold = 0.25
# The height and width of the training set images
input_height = 300
input_width = 300
# Do inference until the lambda is killed.
while True:
# Get a frame from the video stream
ret, frame = awscam.getLastFrame()
if not ret:
raise Exception('Failed to get frame from the stream')
# Resize frame to the same size as the training set.
frame_resize = cv2.resize(frame, (input_height, input_width))
# Run the images through the inference engine and parse the results using
# the parser API, note it is possible to get the output of doInference
# and do the parsing manually, but since it is a ssd model,
# a simple API is provided.
parsed_inference_results = model.parseResult(model_type,
model.doInference(frame_resize))
# Compute the scale in order to draw bounding boxes on the full resolution
# image.
yscale = float(frame.shape[0] / input_height)
xscale = float(frame.shape[1] / input_width)
# Dictionary to be filled with labels and probabilities for MQTT
cloud_output = {}
# Get the detected faces and probabilities
for obj in parsed_inference_results[model_type]:
if obj['prob'] > detection_threshold:
# Add bounding boxes to full resolution frame
xmin = int(xscale * obj['xmin']) \
+ int((obj['xmin'] - input_width / 2) + input_width / 2)
ymin = int(yscale * obj['ymin'])
xmax = int(xscale * obj['xmax']) \
+ int((obj['xmax'] - input_width / 2) + input_width / 2)
ymax = int(yscale * obj['ymax'])
# put the mustache on the face using the ROI of the face in the frame
frame[ymin:ymax, xmin:xmax] = put_moustache(client, iot_topic, stache, frame[ymin:ymax, xmin:xmax])
#cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), (255, 165, 20), 10)
# Amount to offset the label/probability text above the bounding box.
#text_offset = 15
# See https://docs.opencv.org/3.4.1/d6/d6e/group__imgproc__draw.html
# for more information about the cv2.putText method.
# Method signature: image, text, origin, font face, font scale, color,
# and tickness
#cv2.putText(frame, '{:.2f}%'.format(obj['prob'] * 100), (xmin, ymin - text_offset), cv2.FONT_HERSHEY_SIMPLEX, 2.5, (255, 165, 20), 6)
# Store label and probability to send to cloud
cloud_output[output_map[obj['label']]] = obj['prob']
# Set the next frame in the local display stream.
local_display.set_frame_data(frame)
# Send results to the cloud
client.publish(topic=iot_topic, payload=json.dumps(cloud_output))
except Exception as ex:
client.publish(topic=iot_topic, payload='Error in face detection lambda: {}'.format(ex))
greengrass_infinite_infer_run()