import gradio as gr
import cv2
import torch
import numpy as np
from PIL import Image
from collections import Counter
# Load the YOLOv5 model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
# Function to run inference on an image
def run_inference(image):
# Convert the image from PIL format to a format compatible with OpenCV
image = np.array(image)
# Run YOLOv5 inference
results = model(image)
# Convert the annotated image from BGR to RGB for display
annotated_image = results.render()[0]
annotated_image = cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB)
return Image.fromarray(annotated_image)
# Function to generate a summary for the detected objects with counts
def generate_summary_with_counts(image):
results = model(image)
detected_objects = results.pandas().xyxy[0]
# Count detected objects
object_names = detected_objects['name'].tolist()
object_counts = Counter(object_names)
# Create a summary
summary = "Detected objects:\n\n"
for obj, count in object_counts.items():
summary += f"- {obj}: {count}\n"
return summary, object_counts
# Function to generate a scene description based on the detected objects
def generate_scene_description(object_counts):
"""
Generate a possible scene description based on detected objects and their counts.
"""
if "person" in object_counts and "dog" in object_counts:
return "This scene seems to capture people spending time outdoors with pets, possibly in a park or recreational area."
elif "person" in object_counts and "laptop" in object_counts:
return "This might be a workplace or a study environment, featuring individuals using laptops for work or study."
elif "car" in object_counts or "truck" in object_counts:
return "This appears to be a street or traffic scene with vehicles in motion or parked."
elif "cat" in object_counts and "sofa" in object_counts:
return "This scene seems to capture a cozy indoor environment, likely a home with pets relaxing."
elif "bicycle" in object_counts and "person" in object_counts:
return "This could depict an outdoor activity, such as cycling or commuting by bike."
elif "boat" in object_counts or "ship" in object_counts:
return "This seems to be a water-based setting, possibly near a harbor, river, or open sea."
elif "bird" in object_counts and "tree" in object_counts:
return "This scene depicts a natural setting, possibly a park or forest, with birds and trees."
elif "person" in object_counts and "microwave" in object_counts:
return "This is likely an indoor setting, such as a kitchen, where cooking or meal preparation is taking place."
elif "cow" in object_counts or "sheep" in object_counts:
return "This scene appears to capture a rural or farming environment, featuring livestock in open fields or farms."
elif "horse" in object_counts and "person" in object_counts:
return "This might depict an equestrian scene, possibly involving horseback riding or ranch activities."
elif "dog" in object_counts and "ball" in object_counts:
return "This scene seems to show playful activities, possibly a game of fetch with a dog."
elif "umbrella" in object_counts and "person" in object_counts:
return "This might capture a rainy day or a sunny outdoor activity where umbrellas are being used."
elif "train" in object_counts or "railway" in object_counts:
return "This scene could involve a railway station or a train passing through a scenic route."
elif "surfboard" in object_counts or "person" in object_counts:
return "This is likely a beach or coastal scene featuring activities like surfing or water sports."
elif "dining table" in object_counts and "person" in object_counts:
return "This is likely a scene of a Person eating in a Resaturant or Food Court."
elif "book" in object_counts and "person" in object_counts:
return "This scene could depict a quiet reading environment, such as a library or a study room."
elif "traffic light" in object_counts and "car" in object_counts:
return "This seems to capture an urban street scene with traffic and signals controlling the flow."
elif "chair" in object_counts and "dining table" in object_counts:
return "This is likely an indoor dining area, possibly a family meal or a restaurant setting."
elif "flower" in object_counts and "person" in object_counts:
return "This scene could depict a garden or a floral setting, possibly involving gardening or photography."
elif "airplane" in object_counts:
return "This appears to capture an airport or an aerial view, featuring an airplane in flight or on the ground."
elif "person" in object_counts and "whiteboard" in object_counts:
return "This could be a classroom or seminar setting, with individuals engaged in a lecture or discussion."
elif "person" in object_counts and "book" in object_counts:
return "This scene might depict a library or a study area, where individuals are reading or preparing for exams."
elif "person" in object_counts and "bicycle" in object_counts:
return "This is likely a college or urban area, with students or commuters cycling to their destinations."
elif "person" in object_counts and "water bottle" in object_counts:
return "This could be a casual setting, such as a study group or a break during classes, with hydration in focus."
elif "person" in object_counts and "notebook" in object_counts:
return "This scene might depict students taking notes during a lecture or brainstorming in a group study."
elif "person" in object_counts and "coffee cup" in object_counts:
return "This scene could represent a casual hangout in a café, study break, or an informal meeting."
elif "person" in object_counts and "calculator" in object_counts:
return "This is likely an exam hall or a math-focused study session, where calculations are being performed."
elif "laptop" in object_counts and "coffee cup" in object_counts:
return "This might depict a college café or a workspace where students are multitasking with work and refreshments."
elif "pen" in object_counts and "notebook" in object_counts:
return "This scene seems to involve note-taking or journaling, possibly in a classroom or a quiet study area."
elif "headphones" in object_counts and "person" in object_counts:
return "This is likely a casual setting where someone is listening to music, attending an online class, or watching videos."
# Other common and general scenarios
elif "person" in object_counts and "dog" in object_counts:
return "This scene seems to capture people spending time outdoors with pets, possibly in a park or recreational area."
elif "person" in object_counts and "laptop" in object_counts:
return "This might be a workplace or a study environment, featuring individuals using laptops for work or study."
elif "car" in object_counts or "truck" in object_counts:
return "This appears to be a street or traffic scene with vehicles in motion or parked."
elif "cat" in object_counts and "sofa" in object_counts:
return "This scene seems to capture a cozy indoor environment, likely a home with pets relaxing."
elif "bicycle" in object_counts and "person" in object_counts:
return "This could depict an outdoor activity, such as cycling or commuting by bike."
elif "boat" in object_counts or "ship" in object_counts:
return "This seems to be a water-based setting, possibly near a harbor, river, or open sea."
elif "bird" in object_counts and "tree" in object_counts:
return "This scene depicts a natural setting, possibly a park or forest, with birds and trees."
elif "person" in object_counts and "microwave" in object_counts:
return "This is likely an indoor setting, such as a kitchen, where cooking or meal preparation is taking place."
elif "cow" in object_counts or "sheep" in object_counts:
return "This scene appears to capture a rural or farming environment, featuring livestock in open fields or farms."
elif "horse" in object_counts and "person" in object_counts:
return "This might depict an equestrian scene, possibly involving horseback riding or ranch activities."
elif "dog" in object_counts and "ball" in object_counts:
return "This scene seems to show playful activities, possibly a game of fetch with a dog."
elif "umbrella" in object_counts and "person" in object_counts:
return "This might capture a rainy day or a sunny outdoor activity where umbrellas are being used."
elif "train" in object_counts or "railway" in object_counts:
return "This scene could involve a railway station or a train passing through a scenic route."
elif "surfboard" in object_counts or "person" in object_counts:
return "This is likely a beach or coastal scene featuring activities like surfing or water sports."
elif "book" in object_counts and "person" in object_counts:
return "This scene could depict a quiet reading environment, such as a library or a study room."
elif "traffic light" in object_counts and "car" in object_counts:
return "This seems to capture an urban street scene with traffic and signals controlling the flow."
elif "chair" in object_counts and "dining table" in object_counts:
return "This is likely an indoor dining area, possibly a family meal or a restaurant setting."
elif "flower" in object_counts and "person" in object_counts:
return "This scene could depict a garden or a floral setting, possibly involving gardening or photography."
elif "airplane" in object_counts:
return "This appears to capture an airport or an aerial view, featuring an airplane in flight or on the ground."
else:
return "This scene involves various objects, indicating a dynamic or diverse setting."
# Create the Gradio interface with enhanced UI
with gr.Blocks(css="""
body {
font-family: 'Poppins', sans-serif;
margin: 0;
background: linear-gradient(135deg, #3D52A0, #7091E6, #8697C4, #ADBBDA, #EDE8F5);
background-size: 400% 400%;
animation: gradient-animation 15s ease infinite;
color: #FFFFFF;
}
@keyframes gradient-animation {
0% { background-position: 0% 50%; }
50% { background-position: 100% 50%; }
100% { background-position: 0% 50%; }
}
h1 {
text-align: center;
color: #FFFFFF;
font-size: 2.5em;
font-weight: bold;
margin-bottom: 0.5em;
text-shadow: 2px 2px 5px rgba(0, 0, 0, 0.3);
}
footer {
text-align: center;
margin-top: 20px;
padding: 10px;
font-size: 1em;
color: #FFFFFF;
background: rgba(61, 82, 160, 0.8);
border-radius: 8px;
}
.gr-button {
font-size: 1em;
padding: 12px 24px;
background: linear-gradient(90deg, #7091E6, #8697C4);
color: #FFFFFF;
border: none;
border-radius: 5px;
transition: all 0.3s ease-in-out;
}
.gr-button:hover {
background: linear-gradient(90deg, #8697C4, #7091E6);
transform: scale(1.05);
box-shadow: 0 5px 15px rgba(0, 0, 0, 0.2);
}
.gr-box {
background: rgba(255, 255, 255, 0.2);
border: 1px solid rgba(255, 255, 255, 0.3);
border-radius: 10px;
padding: 15px;
box-shadow: 0 4px 10px rgba(0, 0, 0, 0.3);
color: #FFFFFF;
}
""") as demo:
with gr.Row():
gr.Markdown("