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"""
File: plots.py
Author: Dmitry Ryumin, Maxim Markitantov, Elena Ryumina, Anastasia Dvoynikova, and Alexey Karpov
Description: Plotting functions.
License: MIT License
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import cv2
def plot_audio(time_axis, waveform, frame_indices, fps, figsize=(10, 4)) -> plt.Figure:
frame_times = np.array(frame_indices) / fps
fig, ax = plt.subplots(figsize=figsize)
ax.plot(time_axis, waveform[0])
ax.set_xlabel("Time (frames)")
ax.set_ylabel("Amplitude")
ax.grid(True)
ax.set_xticks(frame_times)
ax.set_xticklabels([f"{int(frame_time * fps) + 1}" for frame_time in frame_times])
fig.tight_layout()
return fig
def plot_images(image_paths):
fig, axes = plt.subplots(1, len(image_paths), figsize=(12, 2))
for ax, img_path in zip(axes, image_paths):
ax.imshow(img_path)
ax.axis("off")
fig.tight_layout()
return fig
def get_evenly_spaced_frame_indices(total_frames, num_frames=10):
if total_frames <= num_frames:
return list(range(total_frames))
step = total_frames / num_frames
return [int(np.round(i * step)) for i in range(num_frames)]
def plot_predictions(
df: pd.DataFrame,
column: str,
title: str,
y_labels: list[str],
figsize: tuple[int, int],
x_ticks: list[int],
line_width: float = 2.0,
) -> None:
fig, ax = plt.subplots(figsize=figsize)
df[column] += 1
ax.plot(df.index, df[column], linestyle="dotted", linewidth=line_width)
ax.set_title(title)
ax.set_xlabel("Frames")
ax.set_ylabel(title)
ax.set_xticks(x_ticks)
ax.set_yticks(range(len(y_labels) + 2))
ax.set_yticklabels([" "] + y_labels + [" "])
ax.grid(True)
fig.tight_layout()
return fig
def display_frame_info(img, text, margin=1.0, box_scale=1.0, scale=1.5):
img_copy = img.copy()
img_h, img_w, _ = img_copy.shape
line_width = int(min(img_h, img_w) * 0.001)
thickness = max(int(line_width / 3), 1)
font_face = cv2.FONT_HERSHEY_SIMPLEX
font_color = (0, 0, 0)
font_scale = thickness / scale
t_w, t_h = cv2.getTextSize(text, font_face, font_scale, None)[0]
margin_n = int(t_h * margin)
sub_img = img_copy[
0 + margin_n : 0 + margin_n + t_h + int(2 * t_h * box_scale),
img_w - t_w - margin_n - int(2 * t_h * box_scale) : img_w - margin_n,
]
white_rect = np.ones(sub_img.shape, dtype=np.uint8) * 255
img_copy[
0 + margin_n : 0 + margin_n + t_h + int(2 * t_h * box_scale),
img_w - t_w - margin_n - int(2 * t_h * box_scale) : img_w - margin_n,
] = cv2.addWeighted(sub_img, 0.5, white_rect, 0.5, 1.0)
cv2.putText(
img=img_copy,
text=text,
org=(
img_w - t_w - margin_n - int(2 * t_h * box_scale) // 2,
0 + margin_n + t_h + int(2 * t_h * box_scale) // 2,
),
fontFace=font_face,
fontScale=font_scale,
color=font_color,
thickness=thickness,
lineType=cv2.LINE_AA,
bottomLeftOrigin=False,
)
return img_copy
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