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9972c0a d30be59 9972c0a 201b796 9972c0a 95b4956 9972c0a 14a261b 9972c0a 14a261b 9972c0a 14a261b 9972c0a 14a261b 9972c0a 14a261b 9972c0a 14a261b | 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 86 87 | import gradio as gr
from PIL import Image
from ultralytics import YOLO
import requests
import json
import logging
logging.basicConfig(level=logging.INFO)
model = YOLO("Multiple_Object_BB_Detection_v1.pt")
def detect_objects(images):
results = model(images)
all_bboxes = []
all_bboxes2 = []
for result in results:
boxes = result.boxes.xywhn.tolist()
boxes2 = result.boxes.xywh.tolist()
all_bboxes.append(boxes)
all_bboxes2.append(boxes2)
return all_bboxes, all_bboxes2
def create_solutions(image_urls, all_bboxes, all_bboxes2):
solutions = []
img_id =1
box_id =1
cat_id =1
for image_url, bbox, bbox2 in zip(image_urls, all_bboxes, all_bboxes2): # Loop through each image ID and its corresponding prediction
for subbox, subbox2 in zip(bbox, bbox2):
w = subbox2[2]
h = subbox2[3]
area = w*h
seg=[[]]
ans = {"segmentation":seg, "area":area, 'iscrowd':0, "image_id":img_id, "bbox": subbox, "category_id":cat_id, "id":box_id }
ansx=[]
ansx.append(ans)
obj = {"url": image_url, 'answer':ansx}
box_id +=1
solutions.append(obj)
img_id +=1
return solutions
# def send_results_to_api(data, result_url):
# # Example function to send results to an API
# headers = {"Content-Type": "application/json"}
# response = requests.post(result_url, json=data, headers=headers)
# if response.status_code == 200:
# return response.json() # Return any response from the API if needed
# else:
# return {"error": f"Failed to send results to API: {response.status_code}"}
def process_images(params):
try:
params = json.loads(params)
except json.JSONDecodeError as e:
logging.error(f"Invalid JSON input: {e.msg} at line {e.lineno} column {e.colno}")
return {"error":f"Invalid JSON input: {e.msg} at line {e.lineno} column {e.colno}"}
image_urls = params.get("urls", [])
# api = params.get("api", "")
# job_id = params.get("job_id", "")
if not image_urls:
logging.error("Missing required parameters: 'urls'")
return {"error": "Missing required parameters: 'urls'"}
try:
images = [Image.open(requests.get(url, stream=True).raw) for url in image_urls] # images from URLs
except Exception as e:
logging.error(f"Error loading images: {e}")
return {"error": f"Error loading images: {str(e)}"}
all_bboxes, all_bboxes2 = detect_objects(images) # Perform object detection
solutions = create_solutions(image_urls, all_bboxes, all_bboxes2) # Create solutions with image URLs and bounding boxes
# result_url = f"{api}/{job_id}"
# send_results_to_api(solutions, result_url)
return json.dumps({"solutions": solutions})
inputt = gr.Textbox(label="Parameters (JSON format)")
outputs = gr.JSON()
application = gr.Interface(fn=process_images, inputs=inputt, outputs=outputs, title="Multiple Object Detection with API Integration")
application.launch() |