Spaces:
Sleeping
Sleeping
File size: 3,576 Bytes
5b680d4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 | import platform, gradio as gr
import io
from typing import List, Tuple
from PIL import Image, ImageDraw, ImageFont
from transformers import pipeline
# Load an object-detection pipeline (pick any model you like)
#detector = pipeline("object-detection", model="facebook/detr-resnet-50")
detector = pipeline("object-detection", model="haiquanua/weed_detr")
def draw_boxes(im: Image.Image, preds, threshold: float = 0.25, class_map={"LABEL_0":"Luttuce", "LABEL_1":"Weed"}) -> Image.Image:
"""Draw bounding boxes + labels on a PIL image."""
im = im.convert("RGB")
draw = ImageDraw.Draw(im)
try:
# A small default bitmap font (portable in Spaces)
font = ImageFont.load_default()
except Exception:
font = None
for p in preds:
if p.get("score", 0) < threshold:
continue
box = p["box"] # {'xmin','ymin','xmax','ymax'}
class_label=class_map.get(p['label'], 'others')
label = f"{class_label} {p['score']:.2f}"
xy = [(box["xmin"], box["ymin"]), (box["xmax"], box["ymax"])]
if p['label']=='LABEL_0':
col=(0, 255, 0) #green
elif p['label']=='LABEL_1':
col=(255, 0, 0) #red
else:
col='yellow'
# rectangle + label background
draw.rectangle(xy, outline=(255, 0, 0), width=3)
tw, th = draw.textlength(label, font=font), 14 if font is None else font.size + 6
x0, y0 = box["xmin"], max(0, box["ymin"] - th - 2)
draw.rectangle([x0, y0, x0 + tw + 6, y0 + th + 2], fill=(0, 0, 0))
draw.text((x0 + 3, y0 + 2), label, fill=(255, 255, 255), font=font)
counts = {}
for p in preds:
if p.get("score", 0) >= threshold:
counts[p["label"]] = counts.get(p["label"], 0) + 1
caption = ", ".join(f"{k}: {v}" for k, v in sorted(counts.items())) or "No detections"
return im
def detect_multiple(images: List[Image.Image], threshold: float = 0.25) -> List[Tuple[Image.Image, str]]:
"""
Accepts a list of PIL images, returns a list of (image, caption) pairs
suitable for gr.Gallery. Each image is annotated with boxes.
"""
outputs = []
# Batch through the HF pipeline (it accepts lists)
results = detector(images) # list of lists of predictions
if not isinstance(images, list):
annotated = draw_boxes(images.copy(), results, threshold)
outputs.append(annotated)
else:
for img, preds in zip(images, results):
annotated = draw_boxes(img.copy(), preds, threshold)
outputs.append(annotated)
return outputs
with gr.Blocks(title="Multi-Image Object Detection") as demo:
gr.Markdown("# Multi-Image Object Detection\nUpload several images; I’ll draw boxes and labels for each.")
with gr.Row():
#img_in = gr.Image(type="pil", label="Upload images", tool="select", image_mode="RGB", source="upload", elem_id="img_in", interactive=True, multiple=True)
img_in = gr.Image(type="pil", label="Upload images") # tool="select", image_mode="RGB", source="upload", elem_id="img_in", interactive=True, multiple=True)
gallery = gr.Gallery(label="Detections", columns=3, show_label=True) #height=500,
thr = gr.Slider(0.0, 1.0, value=0.25, step=0.01, label="Confidence threshold")
btn = gr.Button("Run Detection", variant="primary")
btn.click(fn=detect_multiple, inputs=[img_in, thr], outputs=gallery)
gr.Markdown("Tip: You can drag-select multiple files in the picker or paste from clipboard.")
demo.queue(max_size=16).launch()
|