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Merge branch 'master' of github.com:Tocard-Inc/Deep-Learning

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Laureηt 2022-05-12 11:50:20 +02:00
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@ -87,6 +87,14 @@ category = "dev"
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mypy = [
{file = "mypy-0.931-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:3c5b42d0815e15518b1f0990cff7a705805961613e701db60387e6fb663fe78a"},
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@ -803,6 +999,10 @@ opt-einsum = [
{file = "opt_einsum-3.3.0-py3-none-any.whl", hash = "sha256:2455e59e3947d3c275477df7f5205b30635e266fe6dc300e3d9f9646bfcea147"},
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]
packaging = [
{file = "packaging-21.3-py3-none-any.whl", hash = "sha256:ef103e05f519cdc783ae24ea4e2e0f508a9c99b2d4969652eed6a2e1ea5bd522"},
{file = "packaging-21.3.tar.gz", hash = "sha256:dd47c42927d89ab911e606518907cc2d3a1f38bbd026385970643f9c5b8ecfeb"},
]
pathspec = [
{file = "pathspec-0.9.0-py2.py3-none-any.whl", hash = "sha256:7d15c4ddb0b5c802d161efc417ec1a2558ea2653c2e8ad9c19098201dc1c993a"},
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@ -907,6 +1107,14 @@ pyasn1-modules = [
{file = "pyasn1_modules-0.2.8-py3.6.egg", hash = "sha256:cbac4bc38d117f2a49aeedec4407d23e8866ea4ac27ff2cf7fb3e5b570df19e0"},
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pyparsing = [
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python-dateutil = [
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requests = [
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@ -919,6 +1127,10 @@ rsa = [
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1
pyproject.toml
View file

@ -15,6 +15,7 @@ numpy = "^1.22.3"
pure-python-adb = "^0.3.0-alpha.0"
python = ">=3.10,<3.11"
tensorflow = "^2.8.0"
matplotlib = "^3.5.2"
[tool.poetry.dev-dependencies]
black = "^22.1"

109
src/live.py
View file

@ -1,36 +1,106 @@
import os
import subprocess
import time
import matplotlib.pyplot as plt
import numpy as np
import PIL
import ppadb.client
import ppadb.command.serial
import ppadb.device
import tensorflow
import tensorflow as tf
from keras import models
import utils
def make_heatmap(img_array, model, last_conv_layer_name, pred_index=None):
# First, we create a model that maps the input image to the activations
# of the last conv layer as well as the output predictions
grad_model = tf.keras.models.Model([model.inputs], [model.get_layer(last_conv_layer_name).output, model.output])
# Then, we compute the gradient of the top predicted class for our input image
# with respect to the activations of the last conv layer
with tf.GradientTape() as tape:
last_conv_layer_output, preds = grad_model(img_array)
if pred_index is None:
pred_index = tf.argmax(preds[0])
class_channel = preds[:, pred_index]
# This is the gradient of the output neuron (top predicted or chosen)
# with regard to the output feature map of the last conv layer
grads = tape.gradient(class_channel, last_conv_layer_output)
# This is a vector where each entry is the mean intensity of the gradient
# over a specific feature map channel
pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))
# We multiply each channel in the feature map array
# by "how important this channel is" with regard to the top predicted class
# then sum all the channels to obtain the heatmap class activation
last_conv_layer_output = last_conv_layer_output[0]
heatmap = last_conv_layer_output @ pooled_grads[..., tf.newaxis]
heatmap = tf.squeeze(heatmap)
# For visualization purpose, we will also normalize the heatmap between 0 & 1
heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap)
return heatmap.numpy()
def make_gradcam(img, heatmap, alpha=0.5):
# convert img to float32 to support alpha blending
img = tf.image.convert_image_dtype(img, dtype=tf.float32)
# Rescale heatmap to a range 0-255
heatmap = np.uint8(255 * heatmap)
# Use jet colormap to colorize heatmap
jet = plt.get_cmap("jet")
# Use RGB values of the colormap
jet_colors = jet(np.arange(256))[:, :3]
jet_heatmap = jet_colors[heatmap]
# Create an image with RGB colorized heatmap
jet_heatmap = tf.keras.preprocessing.image.array_to_img(jet_heatmap)
jet_heatmap = jet_heatmap.resize((img.shape[1], img.shape[0]))
jet_heatmap = tf.keras.preprocessing.image.img_to_array(jet_heatmap)
jet_heatmap = jet_heatmap / 255
# Superimpose the heatmap on original image
superimposed_img = jet_heatmap * alpha + img * (1 - alpha)
superimposed_img = tf.keras.preprocessing.image.array_to_img(superimposed_img)
# Display Grad CAM
return superimposed_img
RESIZED_SIZE = (100, 50, 3)
LABELS = ["octane", "werewolf", "breakout", "aftershock"]
MODELS_PATH = "models"
MODEL_FILENAME = "rot_25e"
MODEL_FILENAME = "full_aug_5e"
last_conv_layer_name = "C2D_last"
# Load model
model = models.load_model(MODELS_PATH + "/" + MODEL_FILENAME)
utils.startup(need_focus=False)
utils.screenshot(filename="live", folder=MODELS_PATH)
# Attendre que la première image soit créée
time.sleep(10)
running = True
X = np.zeros((1, RESIZED_SIZE[1], RESIZED_SIZE[0], RESIZED_SIZE[2]))
while running:
utils.screenshot(filename="live", folder=MODELS_PATH)
plt.ion()
plt.show()
# time.sleep(1)
while running:
utils.screenshot(filename="live", folder=MODELS_PATH)
# Lecture de l'image
img = PIL.Image.open(MODELS_PATH + "/live.jpg")
@ -40,11 +110,28 @@ while running:
X[0] = np.asarray(img)
Y = model.predict(X)
index = int(np.dot(Y, np.array([0, 1, 2, 3]).T))
preds = model.predict(X)
index = np.argmax(preds)
os.system("clear")
print(f"Model detected : {LABELS[index]}")
for i in range(len(LABELS)):
print(f"\t- {LABELS[i]} {Y[0,i]:.03f}")
print(f"\t- {LABELS[i]} {preds[0,i]:.03f}")
plt.subplot(1, 5, 1)
plt.imshow(img)
plt.title(f"prediction: {LABELS[index]}")
for i in range(4):
# generate class activation heatmap
heatmap = make_heatmap(X, model, last_conv_layer_name, pred_index=i)
# generate gradmap
gradcam = make_gradcam(img, heatmap)
plt.subplot(1, 5, i + 2)
plt.imshow(gradcam)
plt.title(f"{LABELS[i]} ({preds[0][i]:.4f})")
plt.tight_layout()
plt.draw()
plt.pause(0.001)