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import numpy as np
import cv2
import librosa
import librosa.display
from tensorflow.keras.models import load_model
from datetime import datetime
import matplotlib.pyplot as plt
# constants
starttime = datetime.now()
CAT6 = ['fear', 'angry', 'neutral', 'happy', 'sad', 'surprise']
CAT7 = ['fear', 'disgust', 'neutral', 'happy', 'sad', 'surprise', 'angry']
CAT3 = ["positive", "neutral", "negative"]
COLOR_DICT = {"neutral": "grey",
"positive": "green",
"happy": "green",
"surprise": "orange",
"fear": "purple",
"negative": "red",
"angry": "red",
"sad": "lightblue",
"disgust":"brown"}
TEST_CAT = ['fear', 'disgust', 'neutral', 'happy', 'sad', 'surprise', 'angry']
TEST_PRED = np.array([.3,.3,.4,.1,.6,.9,.1])
# page settings
# st.set_page_config(page_title="SER web-app", page_icon=":speech_balloon:", layout="wide")
def get_melspec(audio):
y, sr = librosa.load(audio, sr=44100)
X = librosa.stft(y)
Xdb = librosa.amplitude_to_db(abs(X))
img = np.stack((Xdb,) * 3,-1)
img = img.astype(np.uint8)
grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
grayImage = cv2.resize(grayImage, (224, 224))
rgbImage = np.repeat(grayImage[..., np.newaxis], 3, -1)
return (rgbImage, Xdb)
def get_title(predictions, categories, first_line=''):
txt = f"{first_line}\nDetected emotion: \
{categories[predictions.argmax()]} - {predictions.max() * 100:.2f}%"
return txt
def plot_colored_polar(fig, predictions, categories,
title="", colors=COLOR_DICT):
N = len(predictions)
ind = predictions.argmax()
COLOR = color_sector = colors[categories[ind]]
sector_colors = [colors[i] for i in categories]
fig.set_facecolor("#d1d1e0")
ax = plt.subplot(111, polar="True")
theta = np.linspace(0.0, 2 * np.pi, N, endpoint=False)
for sector in range(predictions.shape[0]):
radii = np.zeros_like(predictions)
radii[sector] = predictions[sector] * 10
width = np.pi / 1.8 * predictions
c = sector_colors[sector]
ax.bar(theta, radii, width=width, bottom=0.0, color=c, alpha=0.25)
angles = [i / float(N) * 2 * np.pi for i in range(N)]
angles += angles[:1]
data = list(predictions)
data += data[:1]
plt.polar(angles, data, color=COLOR, linewidth=2)
plt.fill(angles, data, facecolor=COLOR, alpha=0.25)
ax.spines['polar'].set_color('lightgrey')
ax.set_theta_offset(np.pi / 3)
ax.set_theta_direction(-1)
plt.xticks(angles[:-1], categories)
ax.set_rlabel_position(0)
plt.yticks([0, .25, .5, .75, 1], color="grey", size=8)
plt.suptitle(title, color="darkblue", size=10)
plt.title(f"BIG {N}\n", color=COLOR)
plt.ylim(0, 1)
plt.subplots_adjust(top=0.75)
def plot_melspec(path, tmodel=None, three=False,
CAT3=CAT3, CAT6=CAT6):
# load model if it is not loaded
if tmodel is None:
tmodel = load_model("tmodel_all.h5")
# mel-spec model results
mel = get_melspec(path)[0]
mel = mel.reshape(1, *mel.shape)
tpred = tmodel.predict(mel)[0]
cat = CAT6
if three:
pos = tpred[3] + tpred[5] * .5
neu = tpred[2] + tpred[5] * .5 + tpred[4] * .5
neg = tpred[0] + tpred[1] + tpred[4] * .5
tpred = np.array([pos, neu, neg])
cat = CAT3
txt = get_title(tpred, cat)
fig = plt.figure(figsize=(6, 4))
plot_colored_polar(fig, predictions=tpred, categories=cat, title=txt)
return (fig, tpred)
if __name__ == "__main__":
plot_melspec("test.wav") |