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import io
import matplotlib.pyplot as plt
import inflect
from PIL import Image
import warnings
import logging
from transformers import logging as hf_logging
def render_results_in_image(in_pil_img, in_results):
plt.figure(figsize=(12, 8))
plt.imshow(in_pil_img)
ax = plt.gca()
for prediction in in_results:
box = prediction["box"]
score = prediction["score"]
label = prediction["label"]
x, y = box['xmin'], box['ymin']
w = box['xmax'] - box['xmin']
h = box['ymax'] - box['ymin']
ax.add_patch(plt.Rectangle((x, y), w, h,
fill=False,
color="lime",
linewidth=2))
ax.text(
x, y - 5,
f"{label}: {score:.2f}",
color="yellow",
fontsize=10,
backgroundcolor="black"
)
plt.axis("off")
# Save to buffer
buf = io.BytesIO()
plt.savefig(buf, format="png", bbox_inches="tight", pad_inches=0)
buf.seek(0)
modified_img = Image.open(buf)
plt.close()
return modified_img
def summarize_predictions_natural_language(predictions):
if not predictions:
return "No objects detected."
summary = {}
p = inflect.engine()
for pred in predictions:
label = pred["label"]
summary[label] = summary.get(label, 0) + 1
result = "In this image, there are "
for i, (label, count) in enumerate(summary.items()):
count_str = p.number_to_words(count)
result += f"{count_str} {label}"
if count > 1:
result += "s"
if i < len(summary) - 1:
result += ", "
result += "."
return result
def ignore_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")
logging.basicConfig(level=logging.ERROR)
hf_logging.set_verbosity_error()
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