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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,4 +1,5 @@ | ||
| # Distribution / packaging | ||
| *.pyc | ||
| .Python | ||
| .env | ||
| node_modules | ||
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,123 @@ | ||
| import os | ||
| from threading import Timer | ||
| import time | ||
| import datetime | ||
| import awscam | ||
| import cv2 | ||
| from botocore.session import Session | ||
| from threading import Thread | ||
|
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||
| # Setup the S3 client | ||
| session = Session() | ||
| s3 = session.create_client('s3') | ||
| s3_bucket = 'doorman-faces' | ||
|
|
||
| # setup the camera and frame | ||
| ret, frame = awscam.getLastFrame() | ||
| ret,jpeg = cv2.imencode('.jpg', frame) | ||
| Write_To_FIFO = True | ||
| class FIFO_Thread(Thread): | ||
| def __init__(self): | ||
| ''' Constructor. ''' | ||
| Thread.__init__(self) | ||
|
|
||
| def run(self): | ||
| # write to tmp file for local debugging purpose | ||
| fifo_path = "/tmp/results.mjpeg" | ||
| if not os.path.exists(fifo_path): | ||
| os.mkfifo(fifo_path) | ||
| f = open(fifo_path,'w') | ||
|
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||
| # yay, succesful, let's start streaming to the file | ||
| while Write_To_FIFO: | ||
| try: | ||
| f.write(jpeg.tobytes()) | ||
| except IOError as e: | ||
| continue | ||
|
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||
| def greengrass_infinite_infer_run(): | ||
| try: | ||
| modelPath = "/opt/awscam/artifacts/mxnet_deploy_ssd_resnet50_300_FP16_FUSED.xml" | ||
| modelType = "ssd" | ||
| input_width = 300 | ||
| input_height = 300 | ||
| max_threshold = 0.60 # raise/lower this value based on your conditions | ||
| outMap = { 1: 'aeroplane', 2: 'bicycle', 3: 'bird', 4: 'boat', 5: 'bottle', 6: 'bus', 7 : 'car', 8 : 'cat', 9 : 'chair', 10 : 'cow', 11 : 'dinning table', 12 : 'dog', 13 : 'horse', 14 : 'motorbike', 15 : 'person', 16 : 'pottedplant', 17 : 'sheep', 18 : 'sofa', 19 : 'train', 20 : 'tvmonitor' } | ||
| results_thread = FIFO_Thread() | ||
| results_thread.start() | ||
|
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||
| # Load model to GPU | ||
| mcfg = {"GPU": 1} | ||
| model = awscam.Model(modelPath, mcfg) | ||
|
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||
| # try to get a frame from the camera | ||
| ret, frame = awscam.getLastFrame() | ||
| if ret == False: | ||
| raise Exception("Failed to get frame from the stream") | ||
|
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||
| yscale = float(frame.shape[0]/input_height) | ||
| xscale = float(frame.shape[1]/input_width) | ||
|
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| doInfer = True | ||
| while doInfer: | ||
| # Get a frame from the video stream | ||
| ret, frame = awscam.getLastFrame() | ||
|
|
||
| # Raise an exception if failing to get a frame | ||
| if ret == False: | ||
| raise Exception("Failed to get frame from the stream") | ||
|
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| # Resize frame to fit model input requirement | ||
| frameResize = cv2.resize(frame, (input_width, input_height)) | ||
|
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| # Run model inference on the resized frame | ||
| inferOutput = model.doInference(frameResize) | ||
|
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| # Output inference result to the fifo file so it can be viewed with mplayer | ||
| parsed_results = model.parseResult(modelType, inferOutput)['ssd'] | ||
| label = '{' | ||
| for obj in parsed_results: | ||
| if obj['prob'] > max_threshold: | ||
| 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'] ) | ||
|
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| # if a person was found, upload the target area to S3 for further inspection | ||
| if outMap[obj['label']] == 'person': | ||
|
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||
| # get the person image | ||
| person = frame[ymin:ymax, xmin:xmax] | ||
|
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||
| # create a nice s3 file key | ||
| s3_key = datetime.datetime.utcnow().strftime('%Y-%m-%d_%H_%M_%S.%f') + '.jpg' | ||
| encode_param=[int(cv2.IMWRITE_JPEG_QUALITY), 90] # 90% should be more than enough | ||
| _, jpg_data = cv2.imencode('.jpg', person, encode_param) | ||
| filename = "incoming/%s" % s3_key # the guess lambda function is listening here | ||
| response = s3.put_object(ACL='public-read', Body=jpg_data.tostring(),Bucket=s3_bucket,Key=filename) | ||
|
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||
| # draw a rectangle around the designated area, and tell what label was found | ||
| cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), (255, 165, 20), 4) | ||
| label += '"{}": {:.2f},'.format(outMap[obj['label']], obj['prob'] ) | ||
| label_show = "{}: {:.2f}%".format(outMap[obj['label']], obj['prob']*100 ) | ||
| cv2.putText(frame, label_show, (xmin, ymin-15),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 165, 20), 4) | ||
| label += '"null": 0.0' | ||
| label += '}' | ||
|
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| global jpeg | ||
| ret,jpeg = cv2.imencode('.jpg', frame) | ||
|
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||
| except Exception as e: | ||
| print "Crap, something failed: %s" % str(e) | ||
|
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||
| # Asynchronously schedule this function to be run again in 15 seconds | ||
| Timer(15, greengrass_infinite_infer_run).start() | ||
|
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||
| # Execute the function above | ||
| greengrass_infinite_infer_run() | ||
|
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||
|
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||
| # This is a dummy handler and will not be invoked | ||
| # Instead the code above will be executed in an infinite loop for our example | ||
| def function_handler(event, context): | ||
| return |
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