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2022-073
Commit e63e4e45 authored by Adithya Kahawanugoda's avatar Adithya Kahawanugoda
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Merge branch 'mentalChrom' into 'master' 

Clear backend in mental chronometry

See merge request !16
parents 06976858 12c4f5e8
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Showing with 198 additions and 42 deletions
web-app/backend/server.py
View file @ e63e4e45
......@@ -56,7 +56,6 @@ def predictAttention():
return scoreLst
@app.route('/parents/', methods=['POST', 'GET'])
@cross_origin()
def data2():
......@@ -77,6 +76,7 @@ def data2():
'status': 'Parents details are added to the system successfully!',
})
@app.route('/users/', methods=['POST', 'GET'])
@cross_origin()
def data():
......@@ -195,14 +195,155 @@ def onedata(id):
print('\n # Update successful # \n')
return jsonify({'status': 'Data id: ' + id + ' is updated!'})
# Mental Cronometry API
@app.route("/predictletters", methods=["POST"])
def predict():
letterClassificationmodel = load_model(
'letterClassification.h5', compile=False)
req = request.get_json(force=True)
encoded = req["image"]
decoded = base64.b64decode(encoded)
with open("imgt.png", "wb") as fh:
fh.write(base64.decodebytes(decoded))
pil_image = Image.open(io.BytesIO(decoded)).resize(
(224, 224), Image.LANCZOS).convert("RGB")
image = pil_image
image.save("imgtest.png")
data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
size = (224, 224)
image_array = np.asarray(image)
image_array = cv.resize(image_array, (224, 224))
normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
data[0] = normalized_image_array
word_dict = {0: 'B', 1: 'D', 2: 'M', 3: 'P',
4: 'R', 5: 'S', 6: 'U', 7: 'Y'}
prediction = word_dict[np.argmax(letterClassificationmodel.predict(data))]
prediction2 = letterClassificationmodel.predict(data)
response = {"prediction": prediction}
return jsonify(response)
@ app.route("/predicthanddrwnshapes", methods=["POST"])
def predictHandDrawnShapes():
handDrawnShapeClassificationmodel = load_model(
'shapeClassification2.h5', compile=False)
req = request.get_json(force=True)
encoded = req["image"]
decoded = base64.b64decode(encoded)
with open("imgt.png", "wb") as fh:
fh.write(base64.decodebytes(decoded))
pil_image = Image.open(io.BytesIO(decoded)).resize(
(224, 224), Image.LANCZOS).convert("RGB")
image = pil_image
image.save("imgtest.png")
data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
size = (224, 224)
image_array = np.asarray(image)
image_array = cv.resize(image_array, (224, 224))
normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
data[0] = normalized_image_array
word_dict = {0: 'circle', 1: 'square', 2: 'triangle'}
prediction = word_dict[np.argmax(
handDrawnShapeClassificationmodel.predict(data))]
prediction2 = handDrawnShapeClassificationmodel.predict(data)
response = {"prediction": prediction}
return jsonify(response)
@ app.route("/predictShapePattern", methods=["POST"])
def predictShapePattern():
shapePatternModel = load_model(
'shapeClassification1.h5', compile=False)
req = request.get_json(force=True)
encoded = req["image"]
decoded = base64.b64decode(encoded)
with open("imgt.png", "wb") as fh:
fh.write(base64.decodebytes(decoded))
pil_image = Image.open(io.BytesIO(decoded)).resize(
(224, 224), Image.LANCZOS).convert("RGB")
image = pil_image
image.save("imgtest1.png")
img = cv.imread('imgtest1.png')
h, w, channels = img.shape
half = w//2
left_part = img[:, :half]
right_part = img[:, half:]
half2 = h//2
top = img[:half2, :]
bottom = img[half2:, :]
right_part = cv.resize(right_part, (224, 224))
left_part = cv.resize(left_part, (224, 224))
cv.imwrite('right.jpg', right_part)
cv.imwrite('left.jpg', left_part)
imgArray = ["left.jpg", "right.jpg"]
for img in imgArray:
data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
image = Image.open(img)
size = (224, 224)
image_array = np.asarray(image)
image_array = cv.resize(image_array, (224, 224))
normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
data[0] = normalized_image_array
word_dict = {0: 'circle', 1: 'square', 2: 'star', 3: 'triangle'}
prediction = word_dict[np.argmax(
shapePatternModel.predict(data))]
prediction2 = shapePatternModel.predict(data)
response = {"prediction": prediction}
print(prediction)
return jsonify(response)
# @app.route("/testDB")
# def test():
# db.db.collection.insert_one({"name": "John"})
# return "Connected to the data base!"
# Running app
if __name__ == "__main__":
app.secret_key = os.urandom(24)
app.run(debug=True, use_reloader=False)
flask_cors.CORS(app, expose_headers='Authorization')
web-app/frontend/src/components/activities/ActivityContainer.jsx
View file @ e63e4e45
......@@ -11,65 +11,83 @@ const ActivityContainer = () => {
const nextActivityHandler = () => {
setCurrentActivityNo(currentActivityNo + 1);
};
const changeColor = () => {
let currentDiv = document.getElementsById({ currentActivityNo });
alert("hi");
};
return (
<div className="activity-container-angry-grid">
<div id="activity-container-item-0">
<div
id="1"
className="activity-container-navigator-item"
onClick={() => {
setCurrentActivityNo(1);
changeColor();
}}
>
Picture Concept
1
</div>
<div
id="2"
className="activity-container-navigator-item"
onClick={() => {
setCurrentActivityNo(2);
changeColor();
}}
>
Arithmetic
2
</div>
<div
id="3"
className="activity-container-navigator-item"
onClick={() => {
setCurrentActivityNo(3);
changeColor();
}}
>
Letter Pattern
3
</div>
<div
id="4"
className="activity-container-navigator-item"
onClick={() => {
setCurrentActivityNo(4);
changeColor();
}}
>
Coding
4
</div>
<div
id="5"
className="activity-container-navigator-item"
onClick={() => {
setCurrentActivityNo(5);
changeColor();
}}
>
Pair Cancerlation
5
</div>
<div
id="6"
className="activity-container-navigator-item"
onClick={() => {
setCurrentActivityNo(6);
changeColor();
}}
>
Colour Numbers
6
</div>
<div
id="7"
className="activity-container-navigator-item"
onClick={() => {
setCurrentActivityNo(7);
changeColor();
}}
>
Digit Span
7
</div>
</div>
<div id="activity-container-item-1">
......
web-app/frontend/src/components/activities/mentalChronometryEval/pairCancerlation/PairCancerlationContainer.jsx
View file @ e63e4e45
......@@ -48,17 +48,10 @@ const PairCancerlationContainer = () => {
calculateTime();
});
const [setImage] = useState(null);
const [crop, setCrop] = useState({
aspect: 16 / 9,
unit: "px",
width: 120,
height: 60,
x: 25,
y: 25,
});
const [crop, setCrop] = useState({ aspect: 16 / 9 });
const getCroppedImg = async () => {
const canvas = document.createElement("canvas");
const canvas = document.getElementById("canvas");
const image = document.getElementById("result");
const ctx = canvas.getContext("2d");
ctx.clearRect(0, 0, canvas.width, canvas.height);
......@@ -72,7 +65,7 @@ const PairCancerlationContainer = () => {
const imageWidthRatio = image.naturalWidth / image.width;
const imageHeightRatio = image.naturalHeight / image.height;
// console.log(crop);
console.log(crop);
ctx.drawImage(
image,
crop.x * imageWidthRatio,
......@@ -84,14 +77,13 @@ const PairCancerlationContainer = () => {
crop.width,
crop.height
);
console.log(crop.width);
console.log(crop.height);
const base64 = canvas.toDataURL("image/png");
let imagetoPred = base64.replace("data:image/png;base64,", "");
console.log(imagetoPred);
console.log(base64);
let message = { image: imagetoPred };
......@@ -143,12 +135,7 @@ const PairCancerlationContainer = () => {
{" "}
<div>
{" "}
<img
id="result"
src={PCA1}
alt="description"
onLoad={setImage}
/>{" "}
<img src={PCA1} alt="description" />{" "}
</div>
</div>
<div className="grid grid-cols-1 h-fit m-10">
......@@ -203,18 +190,16 @@ const PairCancerlationContainer = () => {
/>
</button>
</div>
{false && (
<canvas
id="canvas"
width={crop.width}
height={crop.height}
style={{
border: "1px solid black",
objectFit: "contain",
}}
/>
)}
(
<canvas
id="canvas"
width={crop.width}
height={crop.height}
style={{
border: "1px solid black",
objectFit: "contain",
}}
/>
</div>
</div>
</div>
......
web-app/frontend/src/pages/student/Student.jsx
View file @ e63e4e45
......@@ -7,6 +7,7 @@ import { Popover, Transition } from "@headlessui/react";
import { MenuIcon, XIcon } from "@heroicons/react/outline";
import logo from "../../assets/logo1.png";
import sl_kids from "../../assets/image.png";
import info from "../../assets/info.png";
const Student = () => {
const navigate = useNavigate();
......@@ -48,7 +49,7 @@ const Student = () => {
<span className="sr-only">Workflow</span>
<img
alt="Workflow"
className="h-8 w-auto sm:h-20"
className="h-40 w-auto sm:h-40"
src={logo}
/>
</a>
......@@ -65,7 +66,7 @@ const Student = () => {
</div>
</div>
</div>
<div className="hidden md:block md:ml-10 md:pr-4 md:space-x-8">
{/* <div className="hidden md:block md:ml-10 md:pr-4 md:space-x-8">
{navigation.map((item) => (
<a
key={item.name}
......@@ -76,7 +77,7 @@ const Student = () => {
{item.name}
</a>
))}
</div>
</div> */}
</nav>
</div>
......@@ -172,6 +173,17 @@ const Student = () => {
</a>
</div>
</div>
<div className="mt-5 sm:mt-8 sm:flex sm:justify-center lg:justify-start">
<div className="rounded-md shadow">
<a
href="/features"
className="w-full flex items-center justify-center px-8 py-3 border border-transparent text-base font-medium rounded-md text-black bg-[#b4c2f5] hover:bg-indigo-700 md:py-4 md:text-lg md:px-10"
>
<img className="h-8 w-auto" src={info}></img>{" "}
Info
</a>
</div>
</div>
</div>
</main>
</div>
......
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