add lambda code

+#*****************************************************
+#                                                    *
+# Copyright 2018 Amazon.com, Inc. or its affiliates. *
+# All Rights Reserved.                               *
+#                                                    *
+#*****************************************************
+""" A sample lambda for bird detection"""
+from threading import Thread, Event
+import os
+import json
+import numpy as np
+import awscam
+import cv2
+import mo
+import greengrasssdk
+
+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"""
+        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 of 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 infinite_infer_run():
+    """ Entry point of the lambda function"""
+    try:
+        # is quite large so we upload them to a list to map the machine labels to human readable
+        # labels.
+        model_type = 'classification'
+        with open('caltech256_labels.txt', 'r') as labels_file:
+            output_map = [class_label.rstrip() for class_label in labels_file]
+        # Create an IoT client for sending to messages to the cloud.
+        client = greengrasssdk.client('iot-data')
+        iot_topic = '$aws/things/{}/infer'.format(os.environ['AWS_IOT_THING_NAME'])
+        # 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 height and width of the training set images
+        input_height = 224
+        input_width = 224
+        # The sample projects come with optimized artifacts, hence only the artifact
+        # path is required.
+        model_name = "image-classification"
+        ret, model_path = mo.optimize(model_name, input_width, input_height, aux_inputs={'--epoch': 5,'--precision':'FP16'})
+        # Load the model onto the GPU.
+        client.publish(topic=iot_topic, payload='Loading bird detection model')
+        model = awscam.Model(model_path, {'GPU': 1})
+        client.publish(topic=iot_topic, payload='Bird detection loaded')
+        # The number of top results to stream to IoT.
+        num_top_k = 3
+        # Define the detection region size.
+        region_size = 1500
+        # Define the inference display region size. This size was decided based on the longest label.
+        label_region_width = 590
+        label_region_height = 400
+        # Heading for the inference display.
+        
+        # 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')
+            # Crop the detection region for inference.
+            frame_crop = frame[int(frame.shape[0]/2-region_size/2):int(frame.shape[0]/2+region_size/2), \
+                         int(frame.shape[1]/2-region_size/2):int(frame.shape[1]/2+region_size/2), :]
+            # Resize frame to the same size as the training set.
+            frame_resize = cv2.resize(frame_crop, (input_height, input_width))
+            # Model was trained in RGB format but getLastFrame returns image
+            # in BGR format so need to switch.
+            frame_resize = cv2.cvtColor(frame_resize, cv2.COLOR_BGR2RGB)
+            # 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 classification model,
+            # a simple API is provided.
+            parsed_inference_results = model.parseResult(model_type,
+                                                         model.doInference(frame_resize))
+            # Get top k results with highest probabilities
+            top_k = parsed_inference_results[model_type][0:num_top_k]
+            # Create a copy of the original frame.
+            overlay = frame.copy()
+            # Create the rectangle that shows the inference results.
+            cv2.rectangle(overlay, (0, 0), \
+                          (int(label_region_width), int(label_region_height)), (211,211,211), -1)
+            # Blend with the original frame.
+            opacity = 0.7
+            cv2.addWeighted(overlay, opacity, frame, 1 - opacity, 0, frame)
+            # Add the header for the inference results.
+            
+            # Add the label along with the probability of the top result to the frame used by local display.
+            # 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
+            if top_k[0]['prob']*100 < 60 :
+                cv2.putText(frame,'Take your pose',(0,50), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (255, 0, 0), 3)
+            else :
+                cv2.putText(frame, output_map[top_k[0]['label']] + ' ' + str(round(top_k[0]['prob'], 3) * 100) + '%', \
+                            (0, 150), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (255, 0, 0), 3)
+           
+            # Display the detection region.
+            cv2.rectangle(frame, (int(frame.shape[1]/2-region_size/2), int(frame.shape[0]/2-region_size/2)), \
+                          (int(frame.shape[1]/2+region_size/2), int(frame.shape[0]/2+region_size/2)), (255,0,0), 5)
+            # Set the next frame in the local display stream.
+            local_display.set_frame_data(frame)
+            # Send the top k results to the IoT console via MQTT
+            cloud_output = {}
+            for obj in top_k:
+                cloud_output[output_map[obj['label']]] = obj['prob']
+            client.publish(topic=iot_topic, payload=json.dumps(cloud_output))
+    except Exception as ex:
+        client.publish(topic=iot_topic, payload='Error in bird detection lambda: {}'.format(ex))
+
+infinite_infer_run()