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df81d76 ef5b9ad bd0dbc5 ef5b9ad df81d76 ef5b9ad df81d76 ef5b9ad df81d76 ef5b9ad df81d76 ef5b9ad df81d76 ef5b9ad df81d76 ef5b9ad df81d76 ef5b9ad df81d76 ef5b9ad fb14343 df81d76 | 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 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 | # import gradio as gr
# from PIL import Image
# from ultralytics import YOLO
# import requests
# import json
# model = YOLO("BP_Multiple_Objects_Complicated_v1.pt")
# def detect_objects(images):
# results = model(images)
# all_bboxes = []
# all_bboxes2 = []
# all_segments = []
# for result in results:
# boxes = result.boxes.xywhn.tolist()
# boxes2 = result.boxes.xywh.tolist()
# all_bboxes.append(boxes)
# all_bboxes2.append(boxes2)
# masks = result.masks.xyn
# sub_arrays = [arr.tolist() for arr in masks]
# all_segments.append(sub_arrays)
# return all_bboxes, all_bboxes2, all_segments
# def create_solutions(image_urls, all_bboxes, all_bboxes2, all_segments):
# solutions = []
# img_id =1
# box_id =1
# cat_id =1
# for image_url, bbox, bbox2, segmnt in zip(image_urls, all_bboxes, all_bboxes2, all_segments):
# for subbox, subbox2, subsegmnt in zip(bbox, bbox2, segmnt):
# w = subbox2[2]
# h = subbox2[3]
# area = w*h
# flattened_segmnt = [item for sublist in subsegmnt for item in sublist]
# obj = {"image_id":img_id, "image_url": image_url, "id":box_id, "area":area, "category_id":cat_id, "bbox": subbox, "segment":flattened_segmnt} # Create an object for each image
# 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):
# # Parse the JSON string into a dictionary
# params = json.loads(params)
# image_urls = params.get("image_urls", [])
# api = params.get("api", "")
# job_id = params.get("job_id", "")
# images = [Image.open(requests.get(url, stream=True).raw) for url in image_urls] # images from URLs
# all_bboxes, all_bboxes2, all_segments = detect_objects(images) # Perform object detection
# solutions = create_solutions(image_urls, all_bboxes, all_bboxes2, all_segments) # 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}, indent=4)
# inputt = gr.Textbox(label="Parameters (JSON format)")
# outputs = gr.JSON()
# application = gr.Interface(fn=process_images, inputs=inputt, outputs=outputs, title="Multiple Object Segmentation with API Integration")
# application.launch()
import gradio as gr
from PIL import Image
from ultralytics import YOLO
import requests
import json
model = YOLO("BP_Multiple_Objects_Complicated_v1.pt")
def detect_objects(images):
results = model(images)
all_bboxes = []
all_bboxes2 = []
all_segments = []
for result in results:
boxes = result.boxes.xywhn.tolist()
boxes2 = result.boxes.xywh.tolist()
all_bboxes.append(boxes)
all_bboxes2.append(boxes2)
masks = result.masks.xyn
sub_arrays = [arr.tolist() for arr in masks]
all_segments.append(sub_arrays)
return all_bboxes, all_bboxes2, all_segments
def create_solutions(image_urls, all_bboxes, all_bboxes2, all_segments):
solutions = []
img_id = 1
box_id = 1
cat_id = 1
for image_url, bbox, bbox2, segmnt in zip(image_urls, all_bboxes, all_bboxes2, all_segments):
for subbox, subbox2, subsegmnt in zip(bbox, bbox2, segmnt):
w = subbox2[2]
h = subbox2[3]
area = w * h
flattened_segmnt = [item for sublist in subsegmnt for item in sublist]
obj = {
"image_id": img_id,
"image_url": image_url,
"id": box_id,
"area": area,
"category_id": cat_id,
"bbox": subbox,
"segment": flattened_segmnt
}
solutions.append(obj)
box_id += 1
img_id += 1
return solutions
def send_results_to_api(data, result_url):
headers = {"Content-Type": "application/json"}
response = requests.post(result_url, json=data, headers=headers)
if response.status_code == 200:
return response.json()
else:
return {"error": f"Failed to send results to API: {response.status_code}"}
def process_images(params):
params = json.loads(params)
image_urls = params.get("image_urls", [])
api = params.get("api", "")
job_id = params.get("job_id", "")
images = [Image.open(requests.get(url, stream=True).raw) for url in image_urls]
all_bboxes, all_bboxes2, all_segments = detect_objects(images)
solutions = create_solutions(image_urls, all_bboxes, all_bboxes2, all_segments)
result_url = f"{api}/{job_id}"
# Uncomment the next line if you want to send results to an API
# send_results_to_api(solutions, result_url)
return json.dumps({"solutions": solutions}, indent=4)
inputt = gr.Textbox(label="Parameters (JSON format)")
outputs = gr.JSON()
application = gr.Interface(fn=process_images, inputs=inputt, outputs=outputs, title="Multiple Object Segmentation with API Integration")
application.launch()
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