using latest custom vision image

modules/CameraCapture/module.json

@@ -4,7 +4,7 @@
     "image": {
         "repository": "$CONTAINER_REGISTRY_ADDRESS/cameracapture",
         "tag": {
-            "version": "0.2.8",
+            "version": "0.2.11",
             "platforms": {
                 "amd64": "./amd64.Dockerfile",
                 "arm32v7": "./arm32v7.Dockerfile",

modules/ImageClassifierService/amd64.Dockerfile

@@ -1,13 +1,9 @@
-FROM tensorflow/tensorflow:latest-py3
+FROM python:3.7-slim
 
-RUN echo "BUILD MODULE: ImageClassifierService"
+RUN pip install -U pip
+RUN pip install numpy==1.17.3 tensorflow==2.0.0 flask pillow
 
-COPY /build/amd64-requirements.txt amd64-requirements.txt
-
-# Install Python packages
-RUN pip install -r amd64-requirements.txt
-
-ADD app /app
+COPY app /app
 
 # Expose the port
 EXPOSE 80
@@ -16,4 +12,4 @@ EXPOSE 80
 WORKDIR /app
 
 # Run the flask server for the endpoints
-CMD python app.py
+CMD python -u app.py

modules/ImageClassifierService/app/app.py

@@ -4,64 +4,75 @@
 import io
 
 # Imports for the REST API
-from flask import Flask, request
+from flask import Flask, request, jsonify
 
 # Imports for image procesing
 from PIL import Image
-#import scipy
-#from scipy import misc
 
 # Imports for prediction
 from predict import initialize, predict_image, predict_url
 
 app = Flask(__name__)
 
 # 4MB Max image size limit
-app.config['MAX_CONTENT_LENGTH'] = 4 * 1024 * 1024
+app.config['MAX_CONTENT_LENGTH'] = 4 * 1024 * 1024 
 
 # Default route just shows simple text
 @app.route('/')
 def index():
     return 'CustomVision.ai model host harness'
 
 # Like the CustomVision.ai Prediction service /image route handles either
-#     - octet-stream image file
+#     - octet-stream image file 
 #     - a multipart/form-data with files in the imageData parameter
 @app.route('/image', methods=['POST'])
-def predict_image_handler():
+@app.route('/<project>/image', methods=['POST'])
+@app.route('/<project>/image/nostore', methods=['POST'])
+@app.route('/<project>/classify/iterations/<publishedName>/image', methods=['POST'])
+@app.route('/<project>/classify/iterations/<publishedName>/image/nostore', methods=['POST'])
+@app.route('/<project>/detect/iterations/<publishedName>/image', methods=['POST'])
+@app.route('/<project>/detect/iterations/<publishedName>/image/nostore', methods=['POST'])
+def predict_image_handler(project=None, publishedName=None):
     try:
         imageData = None
         if ('imageData' in request.files):
             imageData = request.files['imageData']
+        elif ('imageData' in request.form):
+            imageData = request.form['imageData']
         else:
             imageData = io.BytesIO(request.get_data())
 
-        #img = scipy.misc.imread(imageData)
         img = Image.open(imageData)
         results = predict_image(img)
-        return json.dumps(results)
+        return jsonify(results)
     except Exception as e:
         print('EXCEPTION:', str(e))
         return 'Error processing image', 500
 
 
 # Like the CustomVision.ai Prediction service /url route handles url's
 # in the body of hte request of the form:
-#     { 'Url': '<http url>'}
+#     { 'Url': '<http url>'}  
 @app.route('/url', methods=['POST'])
-def predict_url_handler():
+@app.route('/<project>/url', methods=['POST'])
+@app.route('/<project>/url/nostore', methods=['POST'])
+@app.route('/<project>/classify/iterations/<publishedName>/url', methods=['POST'])
+@app.route('/<project>/classify/iterations/<publishedName>/url/nostore', methods=['POST'])
+@app.route('/<project>/detect/iterations/<publishedName>/url', methods=['POST'])
+@app.route('/<project>/detect/iterations/<publishedName>/url/nostore', methods=['POST'])
+def predict_url_handler(project=None, publishedName=None):
     try:
-        image_url = json.loads(request.get_data())['Url']
+        image_url = json.loads(request.get_data().decode('utf-8'))['url']
         results = predict_url(image_url)
-        return json.dumps(results)
+        return jsonify(results)
     except Exception as e:
         print('EXCEPTION:', str(e))
         return 'Error processing image'
 
-
 if __name__ == '__main__':
     # Load and intialize the model
     initialize()
 
     # Run the server
     app.run(host='0.0.0.0', port=80)
+