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import io
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.pyplot import figure
from PIL import Image
# defining the colors and shapes
color_code = [
"blue",
"orange",
"green",
"red",
"purple",
"brown",
"pink",
"grey",
"olive",
"cyan",
"lime",
"grey",
"firebrick",
"coral",
"chocolate",
"saddlebrown",
"bisque",
"goldenrod",
"gold",
"khaki",
"darkolivegreen",
"greenyellow",
"palegreen",
"springgreen",
"aquamarine",
"teal",
"deepskyblue",
"navy",
"mediumslateblue",
"royalblue",
"indigo",
"magenta",
"deeppink",
"crimson",
"violet",
"snow",
"lightgrey",
"wheat",
"dodgerblue",
"darkseagreen",
]
color_code = color_code * 10
shape_code = ["o", "s", "P", "*", "h", ">", "X", "d", "D", "v", "<", "p"]
shape_code = shape_code * int(len(color_code) / len(shape_code) + 1)
color_values = []
for color in color_code:
_ = plt.fill([0, 0, 1, 1, 0], [0, 1, 1, 0, 0], color)
buf = io.BytesIO()
_ = plt.savefig(buf, format="png")
_ = plt.close()
buf.seek(0)
img = np.array(Image.open(buf).convert("RGB"))
color_values.append(img[100, 300])
color_code_hex = []
for color_value in color_values:
step_color_rgb = tuple([s.item() for s in color_value])
color_code_hex.append("#%02x%02x%02x" % step_color_rgb)
def plot_alignment(
step_ids, frame_labels, step_colors, step_shapes, size=(15, 2), name="all_step_to_video", to_np=True, grid_on=True
):
N_steps = len(frame_labels)
plt.rcParams["figure.figsize"] = (size[0], size[1])
ax = plt.subplot(1, 1, 1)
_ = ax.set_title(name)
tick_freq = 50 if N_steps > 1500 else 20
_ = plt.xticks(np.arange(0, N_steps, tick_freq))
_ = plt.xlim(0, N_steps)
_ = plt.tick_params(bottom=True, top=False, left=True, right=True, labelright=True)
if grid_on:
_ = plt.grid()
else:
plt.plot(np.arange(len(frame_labels)), [1] * len(frame_labels), color="grey")
for si, step_id in enumerate(step_ids):
time, val = [], []
for i in range(N_steps):
if si + 1 == frame_labels[i]:
time.append(i)
val.append(1)
time, val = np.array(time), np.array(val)
_ = plt.plot(time, val, step_shapes[step_id], color=step_colors[step_id])
if to_np:
buf = io.BytesIO()
plt.savefig(buf, format="png")
plt.close()
buf.seek(0)
img = np.array(Image.open(buf).convert("RGB"))
return img
else:
return plt
def plot_step_to_video_alignment(corresp_mat, size=(15, 2)):
"""corresp_mat is of shape [K, N], where K is num_steps, and N is video_len"""
step_ids = np.arange(corresp_mat.size(0)) + 1
labels = corresp_mat.to(float).argmax(0) + 1 * corresp_mat.to(bool).any(0)
K_present = corresp_mat.to(bool).any(1).to(int).sum().item()
name = f"Video Segmentation | {K_present} steps present"
return plot_alignment(step_ids, labels, color_code, shape_code, name=name, size=size)
def plot_similarities(
sim,
drop_line=None,
colors=None,
select=None,
color_offset=0,
do_legend=True,
name="",
size=(15, 2),
grid_on=True,
to_np=True,
linewidth=1,
):
colors = colors if colors is not None else color_code
K, N = sim.shape
select = select if select is not None else np.arange(K)
plt.rcParams["figure.figsize"] = (size[0], size[1])
ax = plt.subplot(1, 1, 1)
_ = ax.set_title(name)
_ = plt.xticks(np.arange(0, N, 20))
_ = plt.xlim(0, N)
_ = plt.tick_params(bottom=True, top=False, left=True, right=True, labelright=True)
if grid_on:
_ = plt.grid()
for i in range(K):
if i in select:
_ = plt.plot(np.arange(N), sim[i], color=colors[i + color_offset], label=str(i), linewidth=linewidth)
if drop_line is not None:
_ = plt.plot(np.arange(N), drop_line * np.ones(N), "--")
if do_legend:
_ = plt.xlim(0, N + int(0.10 * N))
plt.legend()
if to_np:
buf = io.BytesIO()
plt.savefig(buf, format="png")
plt.close()
buf.seek(0)
img = np.array(Image.open(buf).convert("RGB"))
return img
else:
return plt
def plot_gt_seg(N, starts, ends, colors=None, shapes=None, name="GT Seg", clip_len=1, size=(15, 2), grid_on=True):
colors = colors if colors is not None else color_code
shapes = shapes if shapes is not None else shape_code
K = len(starts)
labels = -np.ones(N)
for i in range(K):
s, e = int(starts[i]), int(ends[i])
labels[s : e + 1] = i
step_ids = np.arange(K)
return plot_alignment(step_ids, labels, colors, shapes, to_np=False, name=name, size=size, grid_on=grid_on)
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