test teachable machine

test-python-module/my_model/metadata.json

+{"tfjsVersion":"1.3.1","tmVersion":"2.2.2","packageVersion":"0.8.4","packageName":"@teachablemachine/image","timeStamp":"2020-10-05T14:23:52.574Z","userMetadata":{},"modelName":"tm-my-image-model","labels":["Left Hand","Right Hand"]}
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test-python-module/tf.py

+import tensorflow.keras
+from PIL import Image, ImageOps
+import numpy as np
+import cv2
+import os
+# Disable scientific notation for clarity
+np.set_printoptions(suppress=True)
+
+# Load the model
+model = tensorflow.keras.models.load_model(os.path.join(os.path.dirname(__file__),'keras_model.h5'))
+
+# Create the array of the right shape to feed into the keras model
+# The 'length' or number of images you can put into the array is
+# determined by the first position in the shape tuple, in this case 1.
+data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
+
+# Replace this with the path to your image
+#image = Image.open('test_photo.jpg')
+cap = cv2.VideoCapture(0)
+ret, image = cap.read()
+#resize the image to a 224x224 with the same strategy as in TM2:
+#resizing the image to be at least 224x224 and then cropping from the center
+size = (224, 224)
+#image = ImageOps.fit(image, size, Image.ANTIALIAS)
+
+#turn the image into a numpy array
+image_array = np.asarray(image)
+
+# display the resized image
+image.show()
+
+# Normalize the image
+normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
+
+# Load the image into the array
+data[0] = normalized_image_array
+
+# run the inference
+prediction = model.predict(data)
+print(prediction)
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test-python-module/tm.html

+<div>Teachable Machine Image Model</div>
+<button type="button" onclick="init()">Start</button>
+<div id="webcam-container"></div>
+<div id="label-container"></div>
+<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@1.3.1/dist/tf.min.js"></script>
+<script src="https://cdn.jsdelivr.net/npm/@teachablemachine/image@0.8/dist/teachablemachine-image.min.js"></script>
+<script type="text/javascript">
+    // More API functions here:
+    // https://github.com/googlecreativelab/teachablemachine-community/tree/master/libraries/image
+
+    // the link to your model provided by Teachable Machine export panel
+    const URL = "./my_model/";
+
+    let model, webcam, labelContainer, maxPredictions;
+
+    // Load the image model and setup the webcam
+    async function init() {
+        const modelURL = URL + "model.json";
+        const metadataURL = URL + "metadata.json";
+
+        // load the model and metadata
+        // Refer to tmImage.loadFromFiles() in the API to support files from a file picker
+        // or files from your local hard drive
+        // Note: the pose library adds "tmImage" object to your window (window.tmImage)
+        model = await tmImage.load(modelURL, metadataURL);
+        maxPredictions = model.getTotalClasses();
+
+        // Convenience function to setup a webcam
+        const flip = true; // whether to flip the webcam
+        webcam = new tmImage.Webcam(200, 200, flip); // width, height, flip
+        await webcam.setup(); // request access to the webcam
+        await webcam.play();
+        window.requestAnimationFrame(loop);
+
+        // append elements to the DOM
+        document.getElementById("webcam-container").appendChild(webcam.canvas);
+        labelContainer = document.getElementById("label-container");
+        for (let i = 0; i < maxPredictions; i++) { // and class labels
+            labelContainer.appendChild(document.createElement("div"));
+        }
+    }
+
+    async function loop() {
+        webcam.update(); // update the webcam frame
+        await predict();
+        window.requestAnimationFrame(loop);
+    }
+
+    // run the webcam image through the image model
+    async function predict() {
+        // predict can take in an image, video or canvas html element
+        const prediction = await model.predict(webcam.canvas);
+        for (let i = 0; i < maxPredictions; i++) {
+            const classPrediction =
+                prediction[i].className + ": " + prediction[i].probability.toFixed(2);
+            labelContainer.childNodes[i].innerHTML = classPrediction;
+        }
+        
+    }
+</script>