EasyOCR example added for text detection in images

samples/PT37_opengpu_easyocr/README.md

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+# Run EasyOCR library on AWS Panorama
+
+## Brief
+In this guide, we show how to use easyocr library on the Panorama device. 
+
+## Setup the application
+
+The dependencies folder included with this application has 
+
+* The Dockerfile
+* TensorRT 7.1
+* Cuda Enabled PyTorch for Jetson Xavier
+
+## Steps for setting this up
+
+* Step 1: Navigate to ./dependencies
+* Step 2 : ``` sudo docker build -t pt:37 . ```
+* Step 3 : Open pytorch_example.ipynb and make sure you configure the following
+    * The Device ID
+    * The Camera node information
+* Step 4 : Follow the steps outlined in the notebook
+
+## Special flags in package.json
+
+* Step 1 : Before you deploy the application, open PT37_opengpu_easyocr/easyocr_37_app/packages/(account-id)-easyocr_37_app-1.0/package.json
+* Step 2 : Add the following flags to the package.json
+
+```
+"requirements": 
+            [{
+                    "type" : "hardware_access",
+                    "inferenceAccelerators": [ 
+                        {
+                            "deviceType": "nvhost_gpu",
+                            "sharedResourcePolicy": {
+                                "policy" : "allow_all"
+                            }
+                        }
+                    ]
+            }]
+```
+
+The assets should look something like this
+
+```
+"assets": [
+    {
+        "name": "easyocr_37_2_app",
+        "implementations": [
+            {
+                "type": "container",
+                "assetUri": "9a49a98784f4571adacc417f00942dac7ef2e34686eef21dca9fcb7f4b7ffd70.tar.gz",
+                "descriptorUri": "4bab130ec48eea84e072d9fe813b947e9d9610b2924099036b0165026a91d306.json",
+                "requirements": 
+                [{
+                    "type" : "hardware_access",
+                    "inferenceAccelerators": [ 
+                        {
+                            "deviceType": "nvhost_gpu",
+                            "sharedResourcePolicy": {
+                                "policy" : "allow_all"
+                            }
+                        }
+                    ]
+                }]
+            }
+        ]
+    }
+],
+```
+
+# Debugging
+
+If you encounter issues with deploying from this, once the application is uploaded to the cloud, you can use the graph.json and deploy using the Panorama console as well

samples/PT37_opengpu_easyocr/easyocr_37_2_app/deployment_overrides/override_camera.json

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+{"nodeGraphOverrides": {"envelopeVersion": "2021-01-01", "packages": [{"name": "028663699634::test_rtsp_camera_lab3", "version": "2.0"}], "nodes": [{"name": "test_rtsp_camera_lab3", "interface": "028663699634::test_rtsp_camera_lab3.test_rtsp_camera_lab3", "overridable": true, "overrideMandatory": false, "launch": "onAppStart"}], "nodeOverrides": [{"replace": "front_door_camera", "with": [{"name": "test_rtsp_camera_lab3"}]}]}}

samples/PT37_opengpu_easyocr/easyocr_37_2_app/graphs/easyocr_37_2_app/graph.json

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+{
+    "nodeGraph": {
+        "envelopeVersion": "2021-01-01",
+        "packages": [
+            {
+                "name": "028663699634::easyocr_37_2_app",
+                "version": "1.0"
+            },
+            {
+                "name": "panorama::hdmi_data_sink",
+                "version": "1.0"
+            },
+            {
+                "name": "panorama::abstract_rtsp_media_source",
+                "version": "1.0"
+            }
+        ],
+        "nodes": [
+            {
+                "name": "front_door_camera",
+                "interface": "panorama::abstract_rtsp_media_source.rtsp_v1_interface",
+                "overridable": true,
+                "launch": "onAppStart",
+                "decorator": {
+                    "title": "Camera front_door_camera",
+                    "description": "Default description for camera front_door_camera"
+                }
+            },
+            {
+                "name": "easyocr_37_2_app_node",
+                "interface": "028663699634::easyocr_37_2_app.easyocr_37_2_app_interface",
+                "overridable": false,
+                "launch": "onAppStart"
+            },
+            {
+                "name": "output_node",
+                "interface": "panorama::hdmi_data_sink.hdmi0",
+                "overridable": true,
+                "launch": "onAppStart"
+            }
+        ],
+        "edges": [
+            {
+                "producer": "front_door_camera.video_out",
+                "consumer": "easyocr_37_2_app_node.video_in"
+            },
+            {
+                "producer": "easyocr_37_2_app_node.video_out",
+                "consumer": "output_node.video_in"
+            }
+        ]
+    }
+}

samples/PT37_opengpu_easyocr/easyocr_37_2_app/packages/028663699634-easyocr_37_2_app-1.0/Dockerfile

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+# Use the pre-built docker image attached in this example by doing ```docker load --input panoramasdk_gpu_access_base_image.tar.gz``` or build the base image yourself using the dockerfile provided under /docker/Dockerfile
+FROM pt:37
+RUN apt-get update && apt-get install -y libglib2.0-0
+RUN python3.7 -m pip install boto3
+COPY src /panorama
+# COPY saved_model_trt_fp16 /panorama/saved_model_trt_fp16
+
+# For easyOCR
+RUN pip install opencv-python-headless==4.5.4.60
+RUN pip install easyocr
+RUN pip install -U scikit-image==0.17.2
+RUN python3 -c 'import easyocr; easyocr.Reader(["en"], gpu=False);'
+RUN chmod -R 777 /root/
+RUN ls /root/.EasyOCR/model/ -lrt

samples/PT37_opengpu_easyocr/easyocr_37_2_app/packages/028663699634-easyocr_37_2_app-1.0/descriptor.json

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+{
+    "runtimeDescriptor":
+    {
+        "envelopeVersion": "2021-01-01",
+        "entry":
+        {
+            "path": "python3.7",
+            "name": "/panorama/src/app.py"
+        }
+    }
+}

samples/PT37_opengpu_easyocr/easyocr_37_2_app/packages/028663699634-easyocr_37_2_app-1.0/package.json

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+{
+    "nodePackage": {
+        "envelopeVersion": "2021-01-01",
+        "name": "easyocr_37_2_app",
+        "version": "1.0",
+        "description": "Default description for package easyocr_37_app",
+        "assets": [
+
+        ],
+        "interfaces": [
+            {
+                "name": "easyocr_37_2_app_interface",
+                "category": "business_logic",
+                "asset": "easyocr_37_2_app",
+                "inputs": [
+                    {
+                        "name": "video_in",
+                        "type": "media"
+                    }
+                ],
+                "outputs": [
+                    {
+                        "name": "video_out",
+                        "type": "media"
+                    }
+                ]
+            }
+        ]
+    }
+}

samples/PT37_opengpu_easyocr/easyocr_37_2_app/packages/028663699634-easyocr_37_2_app-1.0/src/app.py

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+import logging
+from logging.handlers import RotatingFileHandler
+
+import easyocr
+import numpy as np
+import panoramasdk
+
+
+class Application(panoramasdk.node):
+    def __init__(self):
+        """Initializes the application's attributes with parameters from the interface, and default values."""
+        self.ocr_detector = easyocr.Reader(["en"], gpu=True)
+
+        logger.info('Initialiation complete.')
+
+
+    def process_streams(self):
+        """Processes one frame of video from one or more video streams."""
+        self.frame_num += 1
+        logger.debug(self.frame_num)
+
+        # Loop through attached video streams
+        streams = self.inputs.video_in.get()
+        for stream in streams:
+            self.process_media(stream)
+
+        self.outputs.video_out.put(streams)
+
+    def process_media(self, stream):
+        """Runs inference on a frame of video."""
+        image_data = stream.image
+        logger.debug(image_data.shape)
+
+        # Cropping image to focus on region to read OCR (top left region with 100*100 pixels)
+        cropped_image = image_data[:100, :100, :]
+
+        # Process results (object deteciton)
+        self.process_results(cropped_image, stream)
+
+    def process_results(self, cropped_image, stream):
+        """Processes output tensors from a computer vision model and annotates a video frame."""
+        if any(dim_size==0 for dim_size in cropped_image.shape):
+            logger.warning("Image size too small")
+            return
+
+        list_of_words_detected = self.ocr_detector.readtext(cropped_image, detail=0)
+
+        drift = 0.05
+
+        # Logging and printing first 5 words
+        for idx, word in enumerate(list_of_words_detected[:5]):
+            logger.info('word #{} = {}'.format(str(idx), word))
+            stream.add_label('word #{} = {}'.format(str(idx), word), 0.1, 0.1*(drift*idx))
+
+def get_logger(name=__name__,level=logging.INFO):
+    logger = logging.getLogger(name)
+    logger.setLevel(level)
+    handler = RotatingFileHandler("/opt/aws/panorama/logs/app.log", maxBytes=100000000, backupCount=2)
+    formatter = logging.Formatter(fmt='%(asctime)s %(levelname)-8s %(message)s',
+                                    datefmt='%Y-%m-%d %H:%M:%S')
+    handler.setFormatter(formatter)
+    logger.addHandler(handler)
+    return logger
+
+def main():
+    try:
+        logger.info("INITIALIZING APPLICATION")
+        app = Application()
+        logger.info("PROCESSING STREAMS")
+        while True:
+            app.process_streams()
+    except:
+        logger.exception('Exception during processing loop.')
+
+logger = get_logger(level=logging.INFO)
+main()