Spaces:
Sleeping
Sleeping
File size: 5,744 Bytes
1000528 b0e7995 1000528 397efea | 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 |
import gradio as gr
import os
from io import BytesIO
from PIL import Image, ImageDraw, ImageFont
from PIL import ImageColor
import json
import google.generativeai as genai
from google.generativeai import types
from dotenv import load_dotenv
from IPython.display import display
# 1. SETUP API KEY
# ----------------
load_dotenv()
api_key = os.getenv("Gemini_API_Key")
# Configure the Google AI library
genai.configure(api_key=api_key)
# 2. DEFINE MODEL AND INSTRUCTIONS
bounding_box_system_instructions = """
Return bounding boxes as a JSON array with labels. Never return masks or code fencing. Limit to 25 objects.
If an object is present multiple times, name them according to their unique characteristic (colors, size, position, unique characteristics, etc..).
"""
model = genai.GenerativeModel( model_name='gemini-2.5-flash', system_instruction=bounding_box_system_instructions)
generation_config = genai.types.GenerationConfig(
temperature=0.5,
)
# 3. PREPARE IMAGE AND PROMPT
prompt = "Identify and label the objects in the image. Return only the JSON array of bounding boxes and labels as per the system instructions."
#image = "Images/cookies.jpg"
#img = Image.open(BytesIO(open(image, "rb").read()))
# print(f"Original image size: {img.size}")
# resize the image to a max width of 1024 while maintaining aspect ratio
#im = Image.open(image).resize((1024, int(1024 * img.size[1] / img.size[0])), Image.Resampling.LANCZOS)
#print(f"Resized image size: {im.size}")
#im.show()
# Run model to find bounding boxes
#response = model.generate_content([prompt, im], generation_config=generation_config)
#print(response.text)
# def generate_bounding_boxes(prompt, image):
# response = model.generate_content([prompt, image], generation_config=generation_config)
# return response.text
def parse_json(json_output):
lines = json_output.splitlines()
for i, line in enumerate(lines):
if line == "```json":
json_output = "\n".join(lines[i+1:]) # Remove everything before "```json"
json_output = json_output.split("```")[0] # Remove everything after the closing "```"
break
return json_output
#bounding_boxes=parse_json(response.text)
#def plot_bounding_boxes(im, bounding_boxes):
"""
Plots bounding boxes on an image with labels.
"""
image = im.copy()
draw = ImageDraw.Draw(image)
font = ImageFont.load_default()
bounding_boxes_json = json.loads(bounding_boxes)
for i, bounding_box in enumerate(bounding_boxes_json):
print(f"Processing bounding box {i}: {bounding_box}")
label = bounding_box["label"]
x1, y1, x2, y2 = bounding_box["box_2d"]
# Draw rectangle
draw.rectangle(
[(x1, y1), (x2, y2)],
outline="red",
width=10
)
# Draw label
draw.text((x1 + 5, y1 + 5), label, fill="red", font=font)
return im
def plot_bounding_boxes(im, bounding_boxes):
"""
Plots bounding boxes on an image with labels.
"""
additional_colors = [colorname for (colorname, colorcode) in ImageColor.colormap.items()]
im = im.copy()
width, height = im.size
draw = ImageDraw.Draw(im)
colors = [
'red', 'green', 'blue', 'yellow', 'orange', 'pink', 'purple', 'cyan',
'lime', 'magenta', 'violet', 'gold', 'silver'
] + additional_colors
try:
# Use a default font if NotoSansCJK is not available
try:
font = ImageFont.load_default()
except OSError:
print("NotoSansCJK-Regular.ttc not found. Using default font.")
font = ImageFont.load_default()
bounding_boxes_json = json.loads(bounding_boxes)
for i, bounding_box in enumerate(bounding_boxes_json):
color = colors[i % len(colors)]
abs_y1 = int(bounding_box["box_2d"][0] / 1000 * height)
abs_x1 = int(bounding_box["box_2d"][1] / 1000 * width)
abs_y2 = int(bounding_box["box_2d"][2] / 1000 * height)
abs_x2 = int(bounding_box["box_2d"][3] / 1000 * width)
if abs_x1 > abs_x2:
abs_x1, abs_x2 = abs_x2, abs_x1
if abs_y1 > abs_y2:
abs_y1, abs_y2 = abs_y2, abs_y1
# Draw bounding box and label
draw.rectangle(((abs_x1, abs_y1), (abs_x2, abs_y2)), outline=color, width=4)
if "label" in bounding_box:
draw.text((abs_x1 + 8, abs_y1 + 6), bounding_box["label"], fill=color, font=font)
except Exception as e:
print(f"Error drawing bounding boxes: {e}")
return im
#im_with_boxes = plot_bounding_boxes(im, bounding_boxes)
#display(im_with_boxes)
#im_with_boxes.save("output_imags/cookies_bounding_boxes.jpg")
#im_with_boxes.show()
#print("Bounding boxes plotted on image.")
def detect_objects(image , prompt):
# Resize image
image = image.resize((1024, int(1024 * image.size[1] / image.size[0])))
# Generate bounding boxes
response = model.generate_content([prompt, image], generation_config=generation_config)
bounding_boxes = parse_json(response.text)
# Draw boxes
output_image = plot_bounding_boxes(image, bounding_boxes)
return output_image, bounding_boxes
# ================== Gradio Interface ==================
interface = gr.Interface(
fn=detect_objects,
inputs=[gr.Image(type="pil"), gr.Textbox( label="Prompt", value="Identify and label the objects in the image. Return only the JSON array of bounding boxes.")],
outputs=[gr.Image(label="Detected Objects"), gr.Textbox(label="Bounding Boxes JSON")],
title="Object Detection with Gemini"
)
interface.launch(server_name="0.0.0.0", server_port=7860) |