fixed vision to recognize jpeg format and print error Images

.gitignore

@@ -11,7 +11,10 @@ templates/
 .ipynb_checkpoints/
 flower_photos/
 flower_photos_spl/
+flower_photos_spt/
 flower.h5
+flower2.h5
+flower.jpg
 move_files.ipynb
 report.ipynb
 

LICENSE

@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [yyyy] [name of copyright owner]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

README.md

@@ -1,4 +1,4 @@
-# Gap : NLP/CV Data Engineering Framework, v0.9.3 (Pre-launch: alpha)
+# Gap : NLP/CV Data Engineering Framework
 
 ## Natural Language Processing for PDF, TIFF, and Camera Captured Documents, and
 ## Computer Vision Processing for Images
@@ -7,7 +7,7 @@
 
 The Gap NLP/CV data engineering framework provides an easy to get started into the world of machine learning for your unstructured data in PDF documents, scanned documents, TIFF facsimiles and camera captured documents, and your image data in image files and image repositories.
 
-*NLP*
+*NLP , v0.9.3 (Pre-launch: alpha)*
 
   - Automatic OCR of scanned PDF and camera captured images.
   - Automatic Text Extraction from documents.
@@ -27,7 +27,7 @@ The Gap NLP/CV data engineering framework provides an easy to get started into t
   - Asynchronous processing of documents.
   - Automatic generation of NLP machine learning ready data.
 
-*CV*
+*CV , v0.9.4 (Pre-launch: beta)*
 
   - Automatic storage and retrieval with high performance HDF5 files.
   - Automatic handling of mixed channels (grayscale, RGB and RGBA) and pixel size.

docs/specs/vision_spec.md

@@ -2,7 +2,7 @@
 
 ## VISION MODULE
 High Precision Image Processing  
-Technical Specification, Gap v0.9.3
+Technical Specification, Gap v0.9.4
 
 ## 1 Images
 ### 1.1 Images Overview
@@ -19,6 +19,7 @@ images = Images([<list of images>], [<list_of_labels>], flags …)
     Alternately, the list of images can be a multi-dimensional numpy array (where the first dimension is the number of images).
     Alternately, the list of images can be a list of multi-dimensional numpy arrays.
     Alternately, the list of labels maybe a single value; in which case, the label applies to all the images.
+    Alternately, the list of labels maybe a numpy 1D (not one-hot encoded) vector or 2D (one-hot encoded) matrix.
 
 + **Image** – This is the base class for the representation of a single Computer Vision (CV). The constructor optionally takes as parameters an image (path), corresponding label, and flags for CV preprocessing the image.
 
@@ -50,6 +51,8 @@ For a single multi-dimensional numpy array, the first dimension are the individu
 **labels:** If not `None`, either:  
 1.	A single integer value (i.e., label) which corresponds to all the images.  
 2.	A list of the same size as `images` parameter list of integer values; where the index of each value is the label for the corresponding index in the `images` parameter.
+3.	A numpy 1D vector of the same size as `images` parameter list of integer values; where the index of each value is the label for the corresponding index in the `images` parameter.
+4.	A numpy 2D vector where the first dimension is of the same size as `images` parameter list, and the second dimension is a one-hot encoded 1D vector.
 
 **dir:** If not `./`, the directory where to store the machine learning ready data.
 
@@ -73,6 +76,7 @@ def myHandler(images):
         float16                 | float32 | float64
         nostore
         raw
+	nlabels=(n)
 			
 **Usage**
 
@@ -127,6 +131,8 @@ def done(image):
 
 If the path to an image file is remote (i.e., starts with http), an HTTP request will be made to fetch the contents of the file from the remote location.
 
+By default, when one-hot encoding of the labels, the `Images` object uses np.max() to calculate the total number of labels in the collection. The `nlabels=n`, where n is the number of labels, configuration setting will override the internal calculation.
+
 **Preprocessing Errors**
 
 During preprocessing of each individual image, if the preprocessing of the image fails, its corresponding `Image` object in the `Images` collection will be `None`, and are not written to HDF5 storage. For example, if ten images are to be preprocessed and two failed, then only eight `Image` objects are written to the HDF5 storage. The number of images that failed to be preprocessed is obtainable from the property `fail`. 
@@ -245,7 +251,7 @@ When used as a setter, a training and test dataset is generated. The `percent` p
 
 When repeated, the property will re-split the data and re-randomize it.  
 
-When used as a getter, the split training, test, and corresponding labels are returned as lists converted to numpy arrays, and the labels are one-hot encoded. This is typically used in conjunction with `next()` operator or `minibatch` property.  
+When used as a getter, the split training, test, and corresponding labels are returned as lists converted to numpy arrays, and the labels are one-hot encoded (if not already). This is typically used in conjunction with `next()` operator or `minibatch` property.  
 
 When the percent is `0`, the data is not split. All the data will be returned in `x_train` and `y_train`, but will still be randomized; `x_test` and `y_test` will be `None`.
 
@@ -487,7 +493,7 @@ Image(image=None, label=0, dir=’./’, ehandler=None, config=None)
 2.	remote location of an image file (i.e., http[s]://….).
 3.	or raw pixel data as a numpy array.
 
-**label:** An integer value which is the label corresponding to the image.
+**label:** An integer value which is the label corresponding to the image, or a numpy 1D vector which is one-hot encoded.
 
 **dir:** If not `'./'`, the directory where to store the machine learning ready data.
 

docs/tutorials/computer_vision.md

@@ -439,6 +439,26 @@ In the above example, we used the variable dataset for the combined collection.
 
 Because the processing and invoking the event handler happen concurrently, there are possible problems including a race condition (i.e., two threads access dataset at the same time), and trashing the internal data (i.e., two threads are combining data at the same time). We solve this by making this operation atomic using Python's thread lock mechanism.
 
+### Example: Image Data is Already Numpy Preprocessed
+
+**Gap** can handle datasets that have been prior preprocessed into numpy arrays, where the image data has been normalized and the label data has been one-hot encoded. For example, the Tensorflow MNIST example dataset, all the images have been flatten and normalized into a numpy array, and all the labels have been one-hot encoded into a 2D numpy matrix. Below is an example demonstrating importing the datasets into an `Images` collection.
+
+```python
+# Import the MNIST input_data function from the tutorials.mnist package
+from tensorflow.examples.tutorials.mnist import input_data
+
+# Read in the data
+# The paramter one_hot=True refers to the label which is categorical (1-10). 
+# The paramter causes the label to be re-encoded as a 10 column vector.
+mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
+
+# Create the images collection for the Training Set
+train = Images(mnist.train.images, mnist.train.labels)
+
+# Create the images collection for the Test Set
+test = Images(mnist.test.images, mnist.test.labels)
+```
+
 ### Size of Preprocessed Machine Learning Ready Data
 
 When preprocessing image data into machine learning ready data, there can be a significant expansion in size. For example, the average size of an (compressed) JPEG flowers sample set (not shown) image is 30K bytes. The compression ratio on these image is as much as 90%. When read in by openCV and decompressed into a raw pixel image, the size typically is 250K bytes. In the raw pixel data, the byte size per pixel is 1 (i.e., 8bits per pixel). When the data is normalized (e.g., divided by 255.0), each pixel becomes represented by a floating point value. By default, the data type is np.float32, which is a 4 byte per pixel representation. Thus, a 250K byte raw pixel image will expand to 1Mb in memory.