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import io
import matplotlib.pyplot as plt
import requests
import inflect
from PIL import Image
def load_image_from_url(url):
return Image.open(requests.get(url, stream=True).raw)
def render_results_in_image(in_pil_img, in_results):
plt.figure(figsize=(16, 10))
plt.imshow(in_pil_img)
ax = plt.gca()
for prediction in in_results:
x, y = prediction['box']['xmin'], prediction['box']['ymin']
w = prediction['box']['xmax'] - prediction['box']['xmin']
h = prediction['box']['ymax'] - prediction['box']['ymin']
ax.add_patch(plt.Rectangle((x, y),
w,
h,
fill=False,
color="green",
linewidth=2))
ax.text(
x,
y,
f"{prediction['label']}: {round(prediction['score']*100, 1)}%",
color='red'
)
plt.axis("off")
# Save the modified image to a BytesIO object
img_buf = io.BytesIO()
plt.savefig(img_buf, format='png',
bbox_inches='tight',
pad_inches=0)
img_buf.seek(0)
modified_image = Image.open(img_buf)
# Close the plot to prevent it from being displayed
plt.close()
return modified_image
def summarize_predictions_natural_language(predictions):
summary = {}
p = inflect.engine()
for prediction in predictions:
label = prediction['label']
if label in summary:
summary[label] += 1
else:
summary[label] = 1
result_string = "In this image, there are "
for i, (label, count) in enumerate(summary.items()):
count_string = p.number_to_words(count)
result_string += f"{count_string} {label}"
if count > 1:
result_string += "s"
result_string += " "
if i == len(summary) - 2:
result_string += "and "
# Remove the trailing comma and space
result_string = result_string.rstrip(', ') + "."
return result_string
##### To ignore warnings #####
import warnings
import logging
from transformers import logging as hf_logging
def ignore_warnings():
# Ignore specific Python warnings
warnings.filterwarnings("ignore", message="Some weights of the model checkpoint")
warnings.filterwarnings("ignore", message="Could not find image processor class")
warnings.filterwarnings("ignore", message="The `max_size` parameter is deprecated")
# Adjust logging for libraries using the logging module
logging.basicConfig(level=logging.ERROR)
hf_logging.set_verbosity_error()
########
import numpy as np
import torch
import matplotlib.pyplot as plt
def show_mask(mask, ax, random_color=False):
if random_color:
color = np.concatenate([np.random.random(3),
np.array([0.6])],
axis=0)
else:
color = np.array([30/255, 144/255, 255/255, 0.6])
h, w = mask.shape[-2:]
mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
ax.imshow(mask_image)
def show_box(box, ax):
x0, y0 = box[0], box[1]
w, h = box[2] - box[0], box[3] - box[1]
ax.add_patch(plt.Rectangle((x0, y0),
w,
h, edgecolor='green',
facecolor=(0,0,0,0),
lw=2))
def show_boxes_on_image(raw_image, boxes):
plt.figure(figsize=(10,10))
plt.imshow(raw_image)
for box in boxes:
show_box(box, plt.gca())
plt.axis('on')
plt.show()
def show_points_on_image(raw_image, input_points, input_labels=None):
plt.figure(figsize=(10,10))
plt.imshow(raw_image)
input_points = np.array(input_points)
if input_labels is None:
labels = np.ones_like(input_points[:, 0])
else:
labels = np.array(input_labels)
show_points(input_points, labels, plt.gca())
plt.axis('on')
plt.show()
def show_points_and_boxes_on_image(raw_image,
boxes,
input_points,
input_labels=None):
plt.figure(figsize=(10,10))
plt.imshow(raw_image)
input_points = np.array(input_points)
if input_labels is None:
labels = np.ones_like(input_points[:, 0])
else:
labels = np.array(input_labels)
show_points(input_points, labels, plt.gca())
for box in boxes:
show_box(box, plt.gca())
plt.axis('on')
plt.show()
def show_points_and_boxes_on_image(raw_image,
boxes,
input_points,
input_labels=None):
plt.figure(figsize=(10,10))
plt.imshow(raw_image)
input_points = np.array(input_points)
if input_labels is None:
labels = np.ones_like(input_points[:, 0])
else:
labels = np.array(input_labels)
show_points(input_points, labels, plt.gca())
for box in boxes:
show_box(box, plt.gca())
plt.axis('on')
plt.show()
def show_points(coords, labels, ax, marker_size=375):
pos_points = coords[labels==1]
neg_points = coords[labels==0]
ax.scatter(pos_points[:, 0],
pos_points[:, 1],
color='green',
marker='*',
s=marker_size,
edgecolor='white',
linewidth=1.25)
ax.scatter(neg_points[:, 0],
neg_points[:, 1],
color='red',
marker='*',
s=marker_size,
edgecolor='white',
linewidth=1.25)
def fig2img(fig):
"""Convert a Matplotlib figure to a PIL Image and return it"""
import io
buf = io.BytesIO()
fig.savefig(buf)
buf.seek(0)
img = Image.open(buf)
return img
def show_mask_on_image(raw_image, mask, return_image=False):
if not isinstance(mask, torch.Tensor):
mask = torch.Tensor(mask)
if len(mask.shape) == 4:
mask = mask.squeeze()
fig, axes = plt.subplots(1, 1, figsize=(15, 15))
mask = mask.cpu().detach()
axes.imshow(np.array(raw_image))
show_mask(mask, axes)
axes.axis("off")
plt.show()
if return_image:
fig = plt.gcf()
return fig2img(fig)
def show_pipe_masks_on_image(raw_image, outputs):
plt.imshow(np.array(raw_image))
ax = plt.gca()
for mask in outputs["masks"]:
show_mask(mask, ax=ax, random_color=True)
plt.axis("off")
plt.show() |