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import io
import json
import os
import tempfile
import zipfile
import datetime
import requests
import random
import gradio as gr
from PIL import Image, ImageDraw, ImageFont
API_URL = os.getenv("API_URL")
API_KEY = os.getenv("API_KEY")
MODEL = os.getenv("MODEL")
ALLOWED_EXTENSIONS = (
".png", ".jpg", ".jpeg", ".bmp", ".gif", ".webp",
".tif", ".tiff", ".avif", ".ico", ".ppm", ".pgm", ".pbm", ".eps", ".icns"
)
def get_color_for_label(label, color_mapping):
if label in color_mapping:
return color_mapping[label]
color = (random.randint(0,255), random.randint(0,255), random.randint(0,255))
color_mapping[label] = color
return color
def process_single_image(image, prompt, confidence, file_format):
if "," in prompt:
prompt_list = [p.strip() for p in prompt.split(",")]
else:
prompt_list = [prompt.strip()]
prompt_list_lower = [p.lower() for p in prompt_list]
buffered = io.BytesIO()
image.save(buffered, format="PNG")
buffered.seek(0)
files = {"image": buffered}
data = {
"prompts": prompt_list,
"model": MODEL,
"confidence": confidence
}
headers = {"Authorization": "Basic " + API_KEY}
response = requests.post(API_URL, files=files, data=data, headers=headers)
response_data = response.json()
print("Response data:", response_data)
detections_list = response_data.get("data", [])
if detections_list and isinstance(detections_list, list):
detections = detections_list[0]
else:
detections = []
annotated_image = image.copy()
draw = ImageDraw.Draw(annotated_image)
try:
font = ImageFont.truetype("arial.ttf", size=15)
except IOError:
font = ImageFont.load_default()
color_mapping = {}
for det in detections:
box = det["bounding_box"]
score = det["score"]
detection_label = det.get("label", prompt).strip()
box_color = get_color_for_label(detection_label, color_mapping)
draw.rectangle(box, outline=box_color, width=2)
text = f"{detection_label} {score:.2f}"
bbox = draw.textbbox((0, 0), text, font=font)
text_width = bbox[2] - bbox[0]
text_height = bbox[3] - bbox[1]
text_origin = (box[0], box[1] - text_height)
draw.rectangle([text_origin, (box[0] + text_width, box[1])], fill=box_color)
draw.text(text_origin, text, fill="white", font=font)
img_width, img_height = image.size
if file_format.lower() == "json":
detection_info = {"detections": detections}
ext = ".json"
content = json.dumps(detection_info, indent=2)
elif file_format.lower() == "txt":
lines = []
for det in detections:
detection_label = det.get("label", prompt).strip().lower()
if detection_label not in prompt_list_lower:
continue
class_id = prompt_list_lower.index(detection_label)
x1, y1, x2, y2 = det["bounding_box"]
x_center = ((x1 + x2) / 2) / img_width
y_center = ((y1 + y2) / 2) / img_height
bbox_width = (x2 - x1) / img_width
bbox_height = (y2 - y1) / img_height
line = f"{class_id} {x_center:.6f} {y_center:.6f} {bbox_width:.6f} {bbox_height:.6f}"
lines.append(line)
content = "\n".join(lines)
ext = ".txt"
else:
detection_info = {"detections": detections}
ext = ".json"
content = json.dumps(detection_info, indent=2)
temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=ext, mode="w", encoding="utf-8")
temp_file.write(content)
temp_file.close()
return annotated_image, temp_file.name
def auto_annotate_batch(input_files, prompt, confidence, file_format):
images_and_names = []
if len(input_files) == 1 and str(input_files[0]).lower().endswith(".zip"):
zip_path = input_files[0]
extract_dir = tempfile.mkdtemp()
with zipfile.ZipFile(zip_path, 'r') as zip_ref:
zip_ref.extractall(extract_dir)
for root, _, files in os.walk(extract_dir):
for file in files:
if file.lower().endswith(ALLOWED_EXTENSIONS):
img_path = os.path.join(root, file)
try:
img = Image.open(img_path).convert("RGB")
images_and_names.append((img, file))
except Exception as e:
print(f"Gagal membuka {img_path}: {e}")
else:
for file_path in input_files:
if file_path.lower().endswith(ALLOWED_EXTENSIONS):
try:
img = Image.open(file_path).convert("RGB")
original_filename = os.path.basename(file_path)
images_and_names.append((img, original_filename))
except Exception as e:
print(f"Gagal membuka {file_path}: {e}")
annotated_images = []
detection_file_paths = []
for img, original_filename in images_and_names:
ann_img, temp_det_file = process_single_image(img, prompt, confidence, file_format)
annotated_images.append(ann_img)
base_name, _ = os.path.splitext(original_filename)
new_name = base_name + (".json" if file_format.lower() == "json" else ".txt")
new_path = os.path.join(os.path.dirname(temp_det_file), new_name)
os.rename(temp_det_file, new_path)
detection_file_paths.append(new_path)
timestamp = datetime.datetime.now().strftime("%d-%m-%Y_%H-%M-%S")
zip_filename = f"Data_Annotate_{timestamp}.zip"
zip_out_path = os.path.join(tempfile.gettempdir(), zip_filename)
with zipfile.ZipFile(zip_out_path, 'w') as zipf:
for file_path in detection_file_paths:
zipf.write(file_path, os.path.basename(file_path))
return annotated_images, zip_out_path, detection_file_paths
iface = gr.Interface(
fn=auto_annotate_batch,
inputs=[
gr.File(file_count="multiple", type="filepath", label="Upload Image (multiple files or ZIP)"),
gr.Textbox(lines=1, placeholder="Enter object prompt (separate with comma if more than one)", label="Prompt"),
gr.Slider(minimum=0.0, maximum=1.0, step=0.05, value=0.25, label="Confidence Threshold"),
gr.Radio(choices=["json", "txt"], value="json", label="Format Annotation File")
],
outputs=[
gr.Gallery(label="Annotated Images"),
gr.File(label="Download All Annotations (ZIP)"),
gr.File(label="Download Individual Annotation Files")
],
title="Auto Annotate")
if __name__ == "__main__":
iface.launch(debug=True) |