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from PIL import Image
import base64
import supervision as sv
import numpy as np
import cv2
colors = sv.ColorPalette.from_hex(
[
"#a1c9f4",
"#ffb482",
"#8de5a1",
"#ff9f9b",
"#d0bbff",
"#debb9b",
"#fab0e4",
"#cfcfcf",
"#fffea3",
"#b9f2f0",
"#a1c9f4",
"#ffb482",
"#8de5a1",
"#ff9f9b",
"#d0bbff",
"#debb9b",
"#fab0e4",
"#cfcfcf",
"#fffea3",
"#b9f2f0",
]
)
def image_w_box(image,objxbox):
box_annotator = sv.BoxCornerAnnotator(thickness=10, corner_length=30, color=colors)
label_annotator = sv.LabelAnnotator(color=colors)
mask_annotator = sv.MaskAnnotator(opacity=0.2, color=colors)
xyxys = np.array([v for boxes in objxbox.values() for v in boxes])
unique_labels = sorted(objxbox.keys())
class_id_map = dict(enumerate(unique_labels))
labels = [l for l, boxes in objxbox.items() for _ in boxes]
class_id = [list(class_id_map.values()).index(label) for label in labels]
masks = np.zeros((len(xyxys), image.shape[0], image.shape[1]), dtype=bool)
for i, (x1, y1, x2, y2) in enumerate(xyxys):
masks[i, int(y1):int(y2), int(x1):int(x2)] = labels[i]
if len(xyxys) == 0:
return image
detections = sv.Detections(
xyxy=xyxys,
mask=masks,
class_id=np.array(class_id),
)
# Convert RGB to BGR for annotation
image_bgr = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
# After annotation, convert back to RGB
annotated_image = box_annotator.annotate(scene=image_bgr.copy(), detections=detections)
annotated_image = label_annotator.annotate(scene=annotated_image, detections=detections, labels=labels)
annotated_image = mask_annotator.annotate(scene=annotated_image, detections=detections)
return cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB)
def encode_image(img) -> tuple[str, str]:
arr = np.array(img.convert("RGB")) if isinstance(img, Image.Image) else img
if not isinstance(arr, np.ndarray):
raise ValueError("Unsupported image type")
ok, buf = cv2.imencode('.jpg', cv2.cvtColor(arr, cv2.COLOR_RGB2BGR))
if not ok:
raise ValueError("Encoding failed")
b64 = base64.b64encode(buf).decode('utf-8')
return b64, "image/jpeg" |