Add brands category faces objects

2019-10-20 11:29:07 +11:00 · 2019-10-20 11:29:07 +11:00 · eb4b7d945d
--- a/MLHUB.yaml
+++ b/MLHUB.yaml
@ -3,7 +3,7 @@ meta:
  name         : azcv
  title        : Azure Computer Vision.
  keywords     : azure, computer vision, ocr, thumbnail, image to text, cloud
-  version      : 2.3.4
+  version      : 2.4.0
  languages    : py
  display      : demo
  license      : mit
@ -21,10 +21,18 @@ dependencies:
    - tags.py
    - describe.py
    - thumbnail.py
+    - category.py
+    - objects.py
+    - brands.py
+    - faces.py
 commands:
  demo      : Demonstrate the functionality of computer vision models.
  tag       : Extract tags for an image.
  describe  : Describe the main element(s) of the image.
+  category  : Image category based on a taxonomy of 86 concepts.
+  objects   : Detect objects within the image.
+  brands    : Identify brands in an image.
  ocr       : Extract text from images.
  landmark  : Identify landmark in a photo.
-  thumbnail : Create a good/informative thumbnail from an image.
+  thumbnail : Create a good/informative thumbnail from an image.
+  faces     : Detect faces in an image.
--- a/brands.py
+++ b/brands.py
@ -0,0 +1,76 @@
+# -*- coding: utf-8 -*-
+
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+# Author: Graham.Williams@togaware.com
+#
+# A script to identify brands in an image.
+#
+# ml brands azcv <path>
+
+from msrest.authentication import CognitiveServicesCredentials
+from azure.cognitiveservices.vision.computervision import ComputerVisionClient
+
+import os
+import argparse
+from textwrap import fill
+
+from mlhub.pkg import azkey, is_url
+from mlhub.utils import get_cmd_cwd
+
+# ----------------------------------------------------------------------
+# Parse command line arguments
+# ----------------------------------------------------------------------
+
+option_parser = argparse.ArgumentParser(add_help=False)
+
+option_parser.add_argument(
+    'path',
+    help='path or url to image')
+
+args = option_parser.parse_args()
+
+# ----------------------------------------------------------------------
+
+SERVICE   = "Computer Vision"
+KEY_FILE  = os.path.join(os.getcwd(), "private.txt")
+
+# Request subscription key and endpoint from user.
+
+subscription_key, endpoint = azkey(KEY_FILE, SERVICE, verbose=False)
+
+# Set credentials.
+
+credentials = CognitiveServicesCredentials(subscription_key)
+
+# Create client.
+
+client = ComputerVisionClient(endpoint, credentials)
+
+# ----------------------------------------------------------------------
+# URL or path
+# ----------------------------------------------------------------------
+
+path = args.path
+
+# Check the URL supplied or path exists and is an image.
+
+# ----------------------------------------------------------------------
+# Analyze
+# ----------------------------------------------------------------------
+
+image_features = ["brands"]
+
+# Send provided image (url or path) to azure to analyse.
+
+if is_url(path):
+    analysis = client.analyze_image(path, image_features)
+else:
+    path = os.path.join(get_cmd_cwd(), path)
+    with open(path, 'rb') as fstream:
+        analysis = client.analyze_image_in_stream(fstream, image_features)
+
+for brand in analysis.brands:
+        print(f"{brand.confidence:.2f},{brand.name}," +
+              f"{brand.rectangle.x},{brand.rectangle.x + brand.rectangle.w}," +
+              f"{brand.rectangle.y},{brand.rectangle.y + brand.rectangle.h}")
--- a/category.py
+++ b/category.py
@ -0,0 +1,72 @@
+# -*- coding: utf-8 -*-
+
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+# Author: Graham.Williams@togaware.com
+#
+# A script to categorize an image.
+#
+# ml category azcv <path>
+
+from msrest.authentication import CognitiveServicesCredentials
+from azure.cognitiveservices.vision.computervision import ComputerVisionClient
+
+import os
+import argparse
+from textwrap import fill
+
+from mlhub.pkg import azkey, is_url
+from mlhub.utils import get_cmd_cwd
+
+# ----------------------------------------------------------------------
+# Parse command line arguments
+# ----------------------------------------------------------------------
+
+option_parser = argparse.ArgumentParser(add_help=False)
+
+option_parser.add_argument(
+    'path',
+    help='path or url to image')
+
+args = option_parser.parse_args()
+
+# ----------------------------------------------------------------------
+
+SERVICE   = "Computer Vision"
+KEY_FILE  = os.path.join(os.getcwd(), "private.txt")
+
+# Request subscription key and endpoint from user.
+
+subscription_key, endpoint = azkey(KEY_FILE, SERVICE, verbose=False)
+
+# Set credentials.
+
+credentials = CognitiveServicesCredentials(subscription_key)
+
+# Create client.
+
+client = ComputerVisionClient(endpoint, credentials)
+
+# ----------------------------------------------------------------------
+# URL or path
+# ----------------------------------------------------------------------
+
+path = args.path
+
+# Check the URL supplied or path exists and is an image.
+
+# ----------------------------------------------------------------------
+# Analyze
+# ----------------------------------------------------------------------
+
+image_features = ["categories"]
+
+if is_url(path):
+    analysis = client.analyze_image(path, image_features)
+else:
+    path = os.path.join(get_cmd_cwd(), path)
+    with open(path, 'rb') as fstream:
+        analysis = client.analyze_image_in_stream(fstream, image_features)
+
+for category in analysis.categories:
+        print(f"{category.score:.2f},{category.name}")
--- a/docs/README.md
+++ b/docs/README.md
@ -49,11 +49,25 @@ $ ml configure azcv
 ## Command Line Tools

 In addition to the *demo* presented below, the *azcv* package provides
-a number of useful command line tools.
+a number of useful command line tools. Below we demonstrate a number
+of these. Most commands take an image as a parameter which may be a
+url or a path to a local file.
+
+**Image Category**
+
+The *category* command takes an image and categorises it based on a
+taxonomy-based of 86 concepts.
+
+```console
+ml categroy azcv https://raw.githubusercontent.com/Azure-Samples/cognitive-services-sample-data-files/master/ComputerVision/Images/landmark.jpg
+0.32,building_
+0.00,others_
+0.04,outdoor_
+```

 **Landmarks and Tags**

-The *landmark* command takes an image (url or path to a local file)
+The *landmark* command takes an image
 and identifies the main landmark contained within the image. The
 confidence of the identification is also returned.

@ -86,6 +100,19 @@ $ ml tag azcv https://www.wayoutback.com.au/assets/Uploads/Uluru.jpg
 ```
 See [Landmarks and Tags](TAGS.md) for further details and examples.

+**Object Bounding Boxes**
+
+The *objects* command returns the bounding box of any identified
+objects within the image.
+
+```console
+ml objects azcv https://raw.githubusercontent.com/Azure-Samples/cognitive-services-sample-data-files/master/ComputerVision/Images/objects.jpg
+213,85,365,208
+218,179,402,384
+238,298,417,416
+116,60,419,386
+```
+
 **Optical Character Recognition to Read Street Signs**

 The *ocr* command is useful for extracting text from a variety of
@ -146,6 +173,26 @@ img-thumbnail.jpg
 ```
 See [Thumbnail Examples](THUMBNAIL.md) for further details and examples.

+**Detecting Brands**
+
+```console
+ml brands azcv https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/images/gray-shirt-logo.jpg
+0.62,Microsoft,58,113,106,152
+0.70,Microsoft,58,260,86,149
+```
+
+**Detecting Faces**
+
+```console
+ml faces azcv https://raw.githubusercontent.com/Azure-Samples/cognitive-services-sample-data-files/master/ComputerVision/Images/faces.jpg
+Male,39,118,159,212,253
+Male,54,492,111,582,201
+Female,55,18,153,102,237
+Female,33,386,166,467,247
+Female,18,235,158,311,234
+Female,8,323,163,391,231
+```
+
 ## Demonstration

 ```console
@ -415,31 +462,46 @@ Thank you for exploring the 'azcv' package.

 # Contributing

-This project welcomes contributions and suggestions.  Most contributions require you to agree to a
-Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
-the rights to use your contribution. For details, visit https://cla.microsoft.com.
+This project welcomes contributions and suggestions.  Most
+contributions require you to agree to a Contributor License Agreement
+(CLA) declaring that you have the right to, and actually do, grant us
+the rights to use your contribution. For details, visit
+https://cla.microsoft.com.

-When you submit a pull request, a CLA-bot will automatically determine whether you need to provide
-a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions
-provided by the bot. You will only need to do this once across all repos using our CLA.
+When you submit a pull request, a CLA-bot will automatically determine
+whether you need to provide a CLA and decorate the PR appropriately
+(e.g., label, comment). Simply follow the instructions provided by the
+bot. You will only need to do this once across all repos using our
+CLA.

-This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
-For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
-contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
+This project has adopted the [Microsoft Open Source Code of
+Conduct](https://opensource.microsoft.com/codeofconduct/).  For more
+information see the [Code of Conduct
+FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact
+[opencode@microsoft.com](mailto:opencode@microsoft.com) with any
+additional questions or comments.

 # Legal Notices

-Microsoft and any contributors grant you a license to the Microsoft documentation and other content
-in this repository under the [Creative Commons Attribution 4.0 International Public License](https://creativecommons.org/licenses/by/4.0/legalcode),
-see the [LICENSE](LICENSE) file, and grant you a license to any code in the repository under the [MIT License](https://opensource.org/licenses/MIT), see the
+Microsoft and any contributors grant you a license to the Microsoft
+documentation and other content in this repository under the [Creative
+Commons Attribution 4.0 International Public
+License](https://creativecommons.org/licenses/by/4.0/legalcode), see
+the [LICENSE](LICENSE) file, and grant you a license to any code in
+the repository under the [MIT
+License](https://opensource.org/licenses/MIT), see the
 [LICENSE-CODE](LICENSE-CODE) file.

-Microsoft, Windows, Microsoft Azure and/or other Microsoft products and services referenced in the documentation
-may be either trademarks or registered trademarks of Microsoft in the United States and/or other countries.
-The licenses for this project do not grant you rights to use any Microsoft names, logos, or trademarks.
-Microsoft's general trademark guidelines can be found at http://go.microsoft.com/fwlink/?LinkID=254653.
+Microsoft, Windows, Microsoft Azure and/or other Microsoft products
+and services referenced in the documentation may be either trademarks
+or registered trademarks of Microsoft in the United States and/or
+other countries.  The licenses for this project do not grant you
+rights to use any Microsoft names, logos, or trademarks.  Microsoft's
+general trademark guidelines can be found at
+http://go.microsoft.com/fwlink/?LinkID=254653.

 Privacy information can be found at https://privacy.microsoft.com/en-us/

-Microsoft and any contributors reserve all other rights, whether under their respective copyrights, patents,
-or trademarks, whether by implication, estoppel or otherwise.
+Microsoft and any contributors reserve all other rights, whether under
+their respective copyrights, patents, or trademarks, whether by
+implication, estoppel or otherwise.
--- a/faces.py
+++ b/faces.py
@ -0,0 +1,77 @@
+# -*- coding: utf-8 -*-
+
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+# Author: Graham.Williams@togaware.com
+#
+# A script to detect faces in an image.
+#
+# ml faces azcv <path>
+
+from msrest.authentication import CognitiveServicesCredentials
+from azure.cognitiveservices.vision.computervision import ComputerVisionClient
+
+import os
+import argparse
+from textwrap import fill
+
+from mlhub.pkg import azkey, is_url
+from mlhub.utils import get_cmd_cwd
+
+# ----------------------------------------------------------------------
+# Parse command line arguments
+# ----------------------------------------------------------------------
+
+option_parser = argparse.ArgumentParser(add_help=False)
+
+option_parser.add_argument(
+    'path',
+    help='path or url to image')
+
+args = option_parser.parse_args()
+
+# ----------------------------------------------------------------------
+
+SERVICE   = "Computer Vision"
+KEY_FILE  = os.path.join(os.getcwd(), "private.txt")
+
+# Request subscription key and endpoint from user.
+
+subscription_key, endpoint = azkey(KEY_FILE, SERVICE, verbose=False)
+
+# Set credentials.
+
+credentials = CognitiveServicesCredentials(subscription_key)
+
+# Create client.
+
+client = ComputerVisionClient(endpoint, credentials)
+
+# ----------------------------------------------------------------------
+# URL or path
+# ----------------------------------------------------------------------
+
+path = args.path
+
+# Check the URL supplied or path exists and is an image.
+
+# ----------------------------------------------------------------------
+# Analyze
+# ----------------------------------------------------------------------
+
+image_features = ["faces"]
+
+# Send provided image (url or path) to azure to analyse.
+
+if is_url(path):
+    analysis = client.analyze_image(path, image_features)
+else:
+    path = os.path.join(get_cmd_cwd(), path)
+    with open(path, 'rb') as fstream:
+        analysis = client.analyze_image_in_stream(fstream, image_features)
+
+for face in analysis.faces:
+        print(f"{face.gender},{face.age}," +
+              f"{face.face_rectangle.left},{face.face_rectangle.top}," +
+              f"{face.face_rectangle.left + face.face_rectangle.width}," +
+              f"{face.face_rectangle.top + face.face_rectangle.height}")
--- a/objects.py
+++ b/objects.py
@ -0,0 +1,74 @@
+# -*- coding: utf-8 -*-
+
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+# Author: Graham.Williams@togaware.com
+#
+# A script to identify objects in an image.
+#
+# ml objects azcv <path>
+
+from msrest.authentication import CognitiveServicesCredentials
+from azure.cognitiveservices.vision.computervision import ComputerVisionClient
+
+import os
+import argparse
+from textwrap import fill
+
+from mlhub.pkg import azkey, is_url
+from mlhub.utils import get_cmd_cwd
+
+# ----------------------------------------------------------------------
+# Parse command line arguments
+# ----------------------------------------------------------------------
+
+option_parser = argparse.ArgumentParser(add_help=False)
+
+option_parser.add_argument(
+    'path',
+    help='path or url to image')
+
+args = option_parser.parse_args()
+
+# ----------------------------------------------------------------------
+
+SERVICE   = "Computer Vision"
+KEY_FILE  = os.path.join(os.getcwd(), "private.txt")
+
+# Request subscription key and endpoint from user.
+
+subscription_key, endpoint = azkey(KEY_FILE, SERVICE, verbose=False)
+
+# Set credentials.
+
+credentials = CognitiveServicesCredentials(subscription_key)
+
+# Create client.
+
+client = ComputerVisionClient(endpoint, credentials)
+
+# Check the URL supplied or path exists and is an image.
+
+# Send provided image (url or path) to azure to extract text.
+
+# ----------------------------------------------------------------------
+# URL or path
+# ----------------------------------------------------------------------
+
+path = args.path
+
+# ----------------------------------------------------------------------
+# Objects
+# ----------------------------------------------------------------------
+
+if is_url(path):
+    analysis = client.detect_objects(path)
+else:
+    path = os.path.join(get_cmd_cwd(), path)
+    with open(path, 'rb') as fstream:
+        analysis = client.detect_objects_in_stream(fstream)
+
+for object in analysis.objects:
+    print(f"{object.rectangle.x},{object.rectangle.y}," +
+          f"{object.rectangle.x + object.rectangle.w}," +
+          f"{object.rectangle.y + object.rectangle.h}")