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import streamlit as st
import cv2
import numpy as np
import tempfile
from PIL import Image
import torch
from torchvision import transforms, models
import time
# Set page config
st.set_page_config(
page_title="Dog Language Understanding",
page_icon="🐕",
layout="wide"
)
class DogBehaviorAnalyzer:
def __init__(self):
# Initialize model (using pretrained ResNet for this example)
self.model = models.resnet50(pretrained=True)
self.model.eval()
# Define image transformations
self.transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
# Define behavior mappings
self.behaviors = {
'tail_wagging': 'Your dog is happy and excited!',
'barking': 'Your dog is trying to communicate or alert you.',
'ears_perked': 'Your dog is alert and interested.',
'lying_down': 'Your dog is relaxed and comfortable.',
'jumping': 'Your dog is energetic and playful!'
}
def analyze_frame(self, frame):
# Convert frame to PIL Image
image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
# Transform image
input_tensor = self.transform(image)
input_batch = input_tensor.unsqueeze(0)
# Simulate behavior detection
# In a real implementation, you'd use a properly trained model
behaviors = []
confidence_scores = np.random.random(len(self.behaviors))
for behavior, score in zip(self.behaviors.keys(), confidence_scores):
if score > 0.7: # Threshold for detection
behaviors.append(behavior)
return behaviors
def main():
st.title("🐕 Dog Language Understanding")
st.write("Upload a video of your dog to analyze their behavior!")
# Initialize analyzer
analyzer = DogBehaviorAnalyzer()
# File uploader
video_file = st.file_uploader("Upload Video", type=['mp4', 'avi', 'mov'])
if video_file is not None:
# Save uploaded file temporarily
tfile = tempfile.NamedTemporaryFile(delete=False)
tfile.write(video_file.read())
# Video analysis
cap = cv2.VideoCapture(tfile.name)
# Create columns for layout
col1, col2 = st.columns(2)
with col1:
st.subheader("Video Preview")
video_placeholder = st.empty()
with col2:
st.subheader("Real-time Analysis")
analysis_placeholder = st.empty()
# Progress bar
progress_bar = st.progress(0)
# Analysis results storage
behavior_counts = {behavior: 0 for behavior in analyzer.behaviors.keys()}
frame_count = 0
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
frame_count += 1
progress = frame_count / total_frames
progress_bar.progress(progress)
# Update video preview
video_placeholder.image(
cv2.cvtColor(frame, cv2.COLOR_BGR2RGB),
channels="RGB",
use_column_width=True
)
# Analyze frame
detected_behaviors = analyzer.analyze_frame(frame)
for behavior in detected_behaviors:
behavior_counts[behavior] += 1
# Update analysis display
analysis_text = "Detected Behaviors:\n\n"
for behavior, count in behavior_counts.items():
if count > 0:
analysis_text += f"• {behavior.replace('_', ' ').title()}: {count} times\n"
analysis_text += f" {analyzer.behaviors[behavior]}\n\n"
analysis_placeholder.text_area(
"Analysis Results",
analysis_text,
height=300
)
time.sleep(0.1) # Add small delay for visualization
cap.release()
# Final summary
st.subheader("Analysis Summary")
st.write("Overall behavior analysis of your dog:")
# Create summary metrics
col1, col2, col3 = st.columns(3)
with col1:
most_common = max(behavior_counts.items(), key=lambda x: x[1])[0]
st.metric("Most Common Behavior", most_common.replace('_', ' ').title())
with col2:
total_behaviors = sum(behavior_counts.values())
st.metric("Total Behaviors Detected", total_behaviors)
with col3:
behavior_variety = len([b for b in behavior_counts.values() if b > 0])
st.metric("Behavior Variety", f"{behavior_variety} types")
# Recommendations
st.subheader("Recommendations")
if total_behaviors > 0:
st.write("""
Based on the analysis, here are some recommendations:
- Maintain regular exercise routines
- Provide mental stimulation through toys and training
- Continue positive reinforcement training
- Monitor your dog's body language for better communication
""")
else:
st.write("No behaviors detected. Try uploading a different video with clearer dog movements.")
if __name__ == "__main__":
main() |