add source code

.gitignore

@@ -0,0 +1,21 @@
+.venv/
+
+*.pyc
+__pycache__/
+
+instance/
+
+.pytest_cache/
+.coverage
+htmlcov/
+
+dist/
+build/
+*.egg-info/
+
+taekwondo_motion_learning/machine_learning/image_classification/dataset/
+
+# runs/
+# yolov8n-cls.pt
+# train.cache
+# val.cache

Dockerfile

@@ -0,0 +1,20 @@
+# read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
+# you will also find guides on how best to write your Dockerfile
+
+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+RUN pip install gunicorn
+
+RUN apt-get update && apt-get install -y libgl1-mesa-glx
+
+COPY . .
+
+RUN chmod 777 ./taekwondo_motion_learning/temp
+
+CMD ["gunicorn", "-b", "0.0.0.0:7860", "run:app"]

README.md

@@ -1,8 +1,8 @@
 ---
 title: Taekwondo Motion Learning
-emoji: 💻
-colorFrom: purple
-colorTo: red
+emoji: 🏆
+colorFrom: green
+colorTo: indigo
 sdk: docker
 pinned: false
 ---

run.py

@@ -0,0 +1,3 @@
+from taekwondo_motion_learning import create_app
+
+app = create_app()

taekwondo_motion_learning/__init__.py

@@ -0,0 +1,33 @@
+from flask import Flask
+import os
+from flask_cors import CORS
+
+def create_app(test_config=None):
+    # create and configure the app
+    app = Flask(__name__, instance_relative_config=True)
+    app.config.from_mapping(
+        SECRET_KEY='dev',
+    )
+    CORS(app)
+
+    if test_config is None:
+        # load the instance config, if it exists, when not testing
+        app.config.from_pyfile('config.py', silent=True)
+    else:
+        # load the test config if passed in
+        app.config.from_mapping(test_config)
+
+    # ensure the instance folder exists
+    try:
+        os.makedirs(app.instance_path)
+    except OSError:
+        pass
+
+    @app.route('/')
+    def index():
+        return 'Taekwondo Motion Learning backend app!'
+        
+    from . import image_classification
+    app.register_blueprint(image_classification.bp)
+
+    return app

taekwondo_motion_learning/image_classification.py

@@ -0,0 +1,22 @@
+from flask import Blueprint, request
+from werkzeug.utils import secure_filename
+import os
+import uuid
+
+from taekwondo_motion_learning.machine_learning.image_classification import predict
+
+bp = Blueprint('image_classification', __name__)
+
+@bp.route('/classify-taekwondo-movement-image', methods=('POST',))
+def classify_taekwondo_movement_image():
+    image = request.files['image']
+    uniqueId = uuid.uuid4()
+    savedImageDir = f"./taekwondo_motion_learning/temp/{secure_filename(str(uniqueId) + image.filename)}"
+    
+    image.save(savedImageDir)
+
+    prediction = predict.classifyImage(savedImageDir)
+
+    os.remove(savedImageDir)
+    
+    return prediction

taekwondo_motion_learning/machine_learning/image_classification/predict.py

@@ -0,0 +1,14 @@
+from ultralytics import YOLO
+import numpy as np
+
+model = YOLO('./taekwondo_motion_learning/machine_learning/image_classification/runs/classify/train/weights/best.pt')
+
+def classifyImage(image):
+    results = model(image)
+    classificationNames = results[0].names
+    probs = results[0].probs.data.tolist()
+
+    return {
+        "result": classificationNames[np.argmax(probs)],
+        "percentage": probs[np.argmax(probs)]*100
+    }

taekwondo_motion_learning/machine_learning/image_classification/runs/classify/train/args.yaml

@@ -0,0 +1,98 @@
+task: classify
+mode: train
+model: yolov8n-cls.pt
+data: ./dataset
+epochs: 50
+patience: 50
+batch: 16
+imgsz: 64
+save: true
+save_period: -1
+cache: false
+device: null
+workers: 8
+project: null
+name: train
+exist_ok: false
+pretrained: true
+optimizer: auto
+verbose: true
+seed: 0
+deterministic: true
+single_cls: false
+rect: false
+cos_lr: false
+close_mosaic: 10
+resume: false
+amp: true
+fraction: 1.0
+profile: false
+freeze: null
+overlap_mask: true
+mask_ratio: 4
+dropout: 0.0
+val: true
+split: val
+save_json: false
+save_hybrid: false
+conf: null
+iou: 0.7
+max_det: 300
+half: false
+dnn: false
+plots: true
+source: null
+show: false
+save_txt: false
+save_conf: false
+save_crop: false
+show_labels: true
+show_conf: true
+vid_stride: 1
+stream_buffer: false
+line_width: null
+visualize: false
+augment: false
+agnostic_nms: false
+classes: null
+retina_masks: false
+boxes: true
+format: torchscript
+keras: false
+optimize: false
+int8: false
+dynamic: false
+simplify: false
+opset: null
+workspace: 4
+nms: false
+lr0: 0.01
+lrf: 0.01
+momentum: 0.937
+weight_decay: 0.0005
+warmup_epochs: 3.0
+warmup_momentum: 0.8
+warmup_bias_lr: 0.1
+box: 7.5
+cls: 0.5
+dfl: 1.5
+pose: 12.0
+kobj: 1.0
+label_smoothing: 0.0
+nbs: 64
+hsv_h: 0.015
+hsv_s: 0.7
+hsv_v: 0.4
+degrees: 0.0
+translate: 0.1
+scale: 0.5
+shear: 0.0
+perspective: 0.0
+flipud: 0.0
+fliplr: 0.5
+mosaic: 1.0
+mixup: 0.0
+copy_paste: 0.0
+cfg: null
+tracker: botsort.yaml
+save_dir: runs\classify\train

taekwondo_motion_learning/machine_learning/image_classification/runs/classify/train/results.csv

@@ -0,0 +1,51 @@
+                  epoch,             train/loss,  metrics/accuracy_top1,  metrics/accuracy_top5,               val/loss,                 lr/pg0,                 lr/pg1,                 lr/pg2
+                      1,                0.29859,                0.34444,                      1,                0.51269,             0.00012852,             0.00012852,             0.00012852
+                      2,                 0.2343,                0.51111,                      1,                0.49288,             0.00025895,             0.00025895,             0.00025895
+                      3,                0.16821,                0.47778,                      1,                0.48214,             0.00038401,             0.00038401,             0.00038401
+                      4,                0.10545,                0.47778,                      1,                0.47167,             0.00050369,             0.00050369,             0.00050369
+                      5,                0.06801,                    0.5,                      1,                0.46781,               0.000618,               0.000618,               0.000618
+                      6,                0.04757,                0.57778,                      1,                0.44524,             0.00064331,             0.00064331,             0.00064331
+                      7,                 0.0295,                0.58889,                      1,                0.44106,             0.00064331,             0.00064331,             0.00064331
+                      8,                0.01747,                0.56667,                      1,                0.44738,             0.00062918,             0.00062918,             0.00062918
+                      9,                0.01127,                    0.6,                      1,                  0.442,             0.00061504,             0.00061504,             0.00061504
+                     10,                0.01444,                0.64444,                      1,                0.43844,              0.0006009,              0.0006009,              0.0006009
+                     11,                0.00992,                0.67778,                      1,                0.42865,             0.00058677,             0.00058677,             0.00058677
+                     12,                0.00447,                0.65556,                      1,                 0.4159,             0.00057263,             0.00057263,             0.00057263
+                     13,                0.00619,                0.68889,                      1,                0.41002,             0.00055849,             0.00055849,             0.00055849
+                     14,                0.00357,                    0.7,                      1,                0.41062,             0.00054435,             0.00054435,             0.00054435
+                     15,                0.00721,                0.66667,                      1,                0.42338,             0.00053022,             0.00053022,             0.00053022
+                     16,                0.00325,                0.65556,                      1,                0.42063,             0.00051608,             0.00051608,             0.00051608
+                     17,                0.00573,                0.65556,                      1,                0.41484,             0.00050194,             0.00050194,             0.00050194
+                     18,                0.00326,                0.62222,                      1,                0.42821,              0.0004878,              0.0004878,              0.0004878
+                     19,                0.00296,                0.62222,                      1,                0.43008,             0.00047367,             0.00047367,             0.00047367
+                     20,                0.00593,                    0.6,                      1,                0.43857,             0.00045953,             0.00045953,             0.00045953
+                     21,                0.00342,                    0.6,                      1,                0.44251,             0.00044539,             0.00044539,             0.00044539
+                     22,                0.00599,                    0.6,                      1,                0.44457,             0.00043126,             0.00043126,             0.00043126
+                     23,                0.00342,                0.61111,                      1,                0.43735,             0.00041712,             0.00041712,             0.00041712
+                     24,                0.00359,                0.62222,                      1,                0.43189,             0.00040298,             0.00040298,             0.00040298
+                     25,                0.00847,                0.61111,                      1,                0.44664,             0.00038884,             0.00038884,             0.00038884
+                     26,                0.00352,                0.63333,                      1,                0.44122,             0.00037471,             0.00037471,             0.00037471
+                     27,                0.00746,                0.62222,                      1,                0.43953,             0.00036057,             0.00036057,             0.00036057
+                     28,                0.00447,                0.61111,                      1,                0.44261,             0.00034643,             0.00034643,             0.00034643
+                     29,                0.00161,                0.57778,                      1,                0.44196,              0.0003323,              0.0003323,              0.0003323
+                     30,                0.00181,                0.61111,                      1,                0.43578,             0.00031816,             0.00031816,             0.00031816
+                     31,                 0.0025,                0.62222,                      1,                0.43366,             0.00030402,             0.00030402,             0.00030402
+                     32,                0.00153,                0.62222,                      1,                0.42898,             0.00028988,             0.00028988,             0.00028988
+                     33,                0.00136,                    0.6,                      1,                0.43659,             0.00027575,             0.00027575,             0.00027575
+                     34,                0.00152,                0.62222,                      1,                0.43015,             0.00026161,             0.00026161,             0.00026161
+                     35,                0.00183,                0.64444,                      1,                0.42306,             0.00024747,             0.00024747,             0.00024747
+                     36,                0.00155,                0.65556,                      1,                0.42204,             0.00023334,             0.00023334,             0.00023334
+                     37,                0.00124,                0.62222,                      1,                0.43082,              0.0002192,              0.0002192,              0.0002192
+                     38,                0.00343,                0.61111,                      1,                0.42536,             0.00020506,             0.00020506,             0.00020506
+                     39,                0.00264,                0.65556,                      1,                0.41905,             0.00019092,             0.00019092,             0.00019092
+                     40,                0.00499,                0.63333,                      1,                0.42504,             0.00017679,             0.00017679,             0.00017679
+                     41,                 0.0033,                0.63333,                      1,                0.42549,             0.00016265,             0.00016265,             0.00016265
+                     42,                0.00221,                0.64444,                      1,                0.42047,             0.00014851,             0.00014851,             0.00014851
+                     43,                0.00219,                0.62222,                      1,                0.42583,             0.00013437,             0.00013437,             0.00013437
+                     44,                0.00073,                0.63333,                      1,                0.43109,             0.00012024,             0.00012024,             0.00012024
+                     45,                0.00217,                0.62222,                      1,                 0.4301,              0.0001061,              0.0001061,              0.0001061
+                     46,                0.00246,                0.64444,                      1,                0.42774,             9.1963e-05,             9.1963e-05,             9.1963e-05
+                     47,                0.00104,                    0.6,                      1,                0.43763,             7.7826e-05,             7.7826e-05,             7.7826e-05
+                     48,                0.00284,                0.58889,                      1,                0.44167,             6.3689e-05,             6.3689e-05,             6.3689e-05
+                     49,                0.00057,                0.58889,                      1,                0.44289,             4.9552e-05,             4.9552e-05,             4.9552e-05
+                     50,                0.00053,                0.58889,                      1,                 0.4336,             3.5414e-05,             3.5414e-05,             3.5414e-05

taekwondo_motion_learning/machine_learning/image_classification/runs/classify/train/weights/best.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f51a2bfb08564c93fa587a5038e8fae26b3a150bb7ba266195e68a2ad9fdca90
+size 2964747

taekwondo_motion_learning/machine_learning/image_classification/runs/classify/train/weights/last.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:38047e7a80381d8a6f43cf7c6d7590f34d8c326a5464908c8fac19636b50640b
+size 2966859

taekwondo_motion_learning/machine_learning/image_classification/train.py

@@ -0,0 +1,10 @@
+from ultralytics import YOLO, settings
+import shutil
+
+shutil.rmtree('./runs', ignore_errors=True)
+
+settings.update({'runs_dir': './runs'})
+
+model = YOLO('yolov8n-cls.pt')  # Pretrained model (recommended for training)
+
+model.train(data='./dataset', epochs=50, imgsz=64)

taekwondo_motion_learning/machine_learning/image_classification/yolov8n-cls.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f5079cd980628313a2e62cd09d358e5c8debf0d8f75b6e8be7973d94e3a5da9f
+size 5533216