Merge branch 'master' of github.com:Tocard-Inc/Deep-Learning
This commit is contained in:
commit
fb68c035cb
2
.vscode/settings.json
vendored
2
.vscode/settings.json
vendored
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@ -11,5 +11,5 @@
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"source.organizeImports": true
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}
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},
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"jupyter.jupyterServerType": "remote"
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"jupyter.jupyterServerType": "local"
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}
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40
src/live.py
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src/live.py
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import subprocess
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import numpy as np
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import PIL
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import ppadb.client
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import ppadb.command.serial
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import ppadb.device
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import tensorflow
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from keras import models
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import utils
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RESIZED_SIZE = (100, 50, 3)
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LABELS = ['octane', 'werewolf', 'breakout', 'aftershock']
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# Load model
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model = models.load_model('../models/model')
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utils.startup(need_focus=False)
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running = True
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while running:
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utils.screenshot(filename='live', folder='../models')
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# Lecture de l'image
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img = PIL.Image.open('../models/live')
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# print(f"img.size = {img.size}")
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# Redimensionnement de l'image
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img = img.resize(RESIZED_SIZE[:-1], PIL.Image.ANTIALIAS)
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# print(f"img.size = {img.size}")
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X = np.asarray(img)
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Y = model.evaluate(X)
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print(f"Model detected : {LABELS[Y]}")
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@ -549,6 +549,16 @@
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"\n",
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"plot_training_analysis()\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Save the weights\n",
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"model.save('../models/model')"
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]
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}
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],
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"metadata": {
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11
src/utils.py
11
src/utils.py
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@ -118,7 +118,7 @@ def start_scrpy() -> None:
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)
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def startup() -> None:
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def startup(need_focus=True) -> None:
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subprocess.call(("sudo modprobe v4l2loopback").split())
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@ -127,7 +127,7 @@ def startup() -> None:
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users = detect_game(device, users)
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start_game(device, users)
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while not (is_zen_mode(device) and is_focused(device) and is_v4l2_loaded()):
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while need_focus and not (is_zen_mode(device) and is_focused(device) and is_v4l2_loaded()):
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time.sleep(DELAY_DETECT)
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time.sleep(DELAY_DETECT)
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@ -135,11 +135,12 @@ def startup() -> None:
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start_scrpy()
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def screenshot() -> uuid.UUID:
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filename = uuid.uuid1()
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def screenshot(filename=None, folder=DATA_FOLDER) -> uuid.UUID:
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if filename == None:
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filename = uuid.uuid1()
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subprocess.Popen(
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f"ffmpeg -f video4linux2 -i /dev/video2 -frames:v 1 -filter:v crop=400:150:45:30 {DATA_FOLDER}/{filename}.jpg".split(),
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f"ffmpeg -f video4linux2 -i /dev/video2 -frames:v 1 -filter:v crop=400:150:45:30 {folder}/{filename}.jpg".split(),
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stdout=subprocess.PIPE,
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stderr=subprocess.STDOUT,
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)
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