Python keras.applications.imagenet_utils.decode_predictions() Examples

The following are 19 code examples of keras.applications.imagenet_utils.decode_predictions(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module keras.applications.imagenet_utils , or try the search function

Example #1

Source File: model.py From picasso with Eclipse Public License 1.0

6 votes

def decode_prob(self, class_probabilities):
        r = imagenet_utils.decode_predictions(class_probabilities,
                                              top=self.top_probs)
        results = [
            [{'code': entry[0],
              'name': entry[1],
              'prob': '{:.3f}'.format(entry[2])}
             for entry in row]
            for row in r
        ]
        classes = imagenet_utils.CLASS_INDEX
        class_keys = list(classes.keys())
        class_values = list(classes.values())

        for result in results:
            for entry in result:
                entry['index'] = int(
                    class_keys[class_values.index([entry['code'],
                                                   entry['name']])])
        return results

Example #2

Source File: test_imagenet.py From dfc2019 with MIT License

6 votes

def test_model(model, preprocessing_func, sample, ground_truth):

    x = preprocessing_func(sample)
    x = np.expand_dims(x, 0)
    y = model.predict(x)

    print('[INFO]', decode_predictions(y))

    pred = get_top(y)
    if is_equal(pred, ground_truth):
        print('[INFO] Test passed...\n')
    else:
        print('[WARN] TEST FAILED...')
        print('[WARN] PREDICTION', pred)
        print('[WARN] GROUND TRUTH', ground_truth)
        print()

Example #3

Source File: test_imagenet.py From SpaceNet_Off_Nadir_Solutions with Apache License 2.0

6 votes

def test_model(model, preprocessing_func, sample, ground_truth):

    x = preprocessing_func(sample)
    x = np.expand_dims(x, 0)
    y = model.predict(x)

    print('[INFO]', decode_predictions(y))

    pred = get_top(y)
    if is_equal(pred, ground_truth):
        print('[INFO] Test passed...\n')
    else:
        print('[WARN] TEST FAILED...')
        print('[WARN] PREDICTION', pred)
        print('[WARN] GROUND TRUTH', ground_truth)
        print()

Example #4

Source File: named_image.py From spark-deep-learning with Apache License 2.0

6 votes

def _decodeOutputAsPredictions(self, df):
        # If we start having different weights than imagenet, we'll need to
        # move this logic to individual model building in NamedImageTransformer.
        # Also, we could put the computation directly in the main computation
        # graph or use a scala UDF for potentially better performance.
        topK = self.getOrDefault(self.topK)

        def decode(predictions):
            pred_arr = np.expand_dims(np.array(predictions), axis=0)
            decoded = decode_predictions(pred_arr, top=topK)[0]
            # convert numpy dtypes to python native types
            return [(t[0], t[1], t[2].item()) for t in decoded]

        decodedSchema = ArrayType(
            StructType([
                StructField("class", StringType(), False),
                StructField("description", StringType(), False),
                StructField("probability", FloatType(), False)
            ]))
        decodeUDF = udf(decode, decodedSchema)
        interim_output = self._getIntermediateOutputCol()
        return df \
            .withColumn(self.getOutputCol(), decodeUDF(df[interim_output])) \
            .drop(interim_output)

Example #5

Source File: lambda_code.py From keras-lambda with MIT License

5 votes

def lambda_handler(event, context):
    
    url = ''
    try:
        # API Gateway GET method
        if event['httpMethod'] == 'GET':
            url = event['queryStringParameters']['url']
        # API Gateway POST method
        elif event['httpMethod'] == 'POST':
            data = json.loads(event['body'])
            url = data['url']
    except KeyError:
        # direct invocation
        url = event['url']
    
    handler_start_time = time.time()
    start_time = time.time()
    x = download_image(url)
    #requires scipy lib, can't use it since that puts us over 50MB zip limit for Lambda
    #x = preprocess_input(x) 
    preds = model.predict(x)
    end_time = time.time()
    print('Predicted:', decode_predictions(preds))
    h_time = end_time - handler_start_time
    p_time = end_time - start_time
    print("handler:", h_time ,"pred time:", p_time)
    return "%s,%s" % (h_time, p_time)

Example #6

Source File: keras_server.py From Mastering-OpenCV-4-with-Python with MIT License

5 votes

def predict():
    # Initialize result:
    result = {"success": False}

    if flask.request.method == "POST":
        if flask.request.files.get("image"):
            # Read input image in PIL format:
            image = flask.request.files["image"].read()
            image = Image.open(io.BytesIO(image))

            # Pre-process the image to be classified:
            image = preprocessing_image(image, target=(224, 224))

            # Classify the input image:
            with graph.as_default():
                predictions = model.predict(image)
            results = imagenet_utils.decode_predictions(predictions)
            result["predictions"] = []

            # Add the predictions to the result:
            for (imagenet_id, label, prob) in results[0]:
                r = {"label": label, "probability": float(prob)}
                result["predictions"].append(r)

            # At this point we can say that the request was dispatched successfully:
            result["success"] = True

    # Return result as a JSON response:
    return flask.jsonify(result)

Example #7

Source File: run_keras_server.py From simple-keras-rest-api with MIT License

5 votes

def predict():
	# initialize the data dictionary that will be returned from the
	# view
	data = {"success": False}

	# ensure an image was properly uploaded to our endpoint
	if flask.request.method == "POST":
		if flask.request.files.get("image"):
			# read the image in PIL format
			image = flask.request.files["image"].read()
			image = Image.open(io.BytesIO(image))

			# preprocess the image and prepare it for classification
			image = prepare_image(image, target=(224, 224))

			# classify the input image and then initialize the list
			# of predictions to return to the client
			preds = model.predict(image)
			results = imagenet_utils.decode_predictions(preds)
			data["predictions"] = []

			# loop over the results and add them to the list of
			# returned predictions
			for (imagenetID, label, prob) in results[0]:
				r = {"label": label, "probability": float(prob)}
				data["predictions"].append(r)

			# indicate that the request was a success
			data["success"] = True

	# return the data dictionary as a JSON response
	return flask.jsonify(data)

# if this is the main thread of execution first load the model and
# then start the server

Example #8

Source File: test.py From keras-squeezenet with MIT License

5 votes

def testTHPrediction(self):
        keras.backend.set_image_dim_ordering('th')
        model = SqueezeNet()
        img = image.load_img('images/cat.jpeg', target_size=(227, 227))
        x = image.img_to_array(img)
        x = np.expand_dims(x, axis=0)
        x = preprocess_input(x)
        preds = model.predict(x)
        decoded_preds = decode_predictions(preds)
        #print('Predicted:', decoded_preds)
        self.assertIn(decoded_preds[0][0][1], 'tabby')
        #self.assertAlmostEqual(decode_predictions(preds)[0][0][2], 0.82134342)

Example #9

Source File: test.py From keras-squeezenet with MIT License

5 votes

def testTFwPrediction(self):
        keras.backend.set_image_dim_ordering('tf')
        model = SqueezeNet()
        img = image.load_img('images/cat.jpeg', target_size=(227, 227))
        x = image.img_to_array(img)
        x = np.expand_dims(x, axis=0)
        x = preprocess_input(x)
        preds = model.predict(x)
        decoded_preds = decode_predictions(preds)
        #print('Predicted:', decoded_preds)
        self.assertIn(decoded_preds[0][0][1], 'tabby')
        #self.assertAlmostEqual(decode_predictions(preds)[0][0][2], 0.82134342)

Example #10