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import torch
import cv2
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
from transformers import pipeline
import gradio as gr
from sklearn.cluster import KMeans
# Emotion detection pipeline for text (if any text is included in assets)
emotion_classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True)
# Function to analyze colors in an image
def analyze_colors(image):
print(f"Image type: {type(image)}, Image mode: {image.mode}") # Debugging line
try:
# Ensure the image is in RGB format
if image.mode != "RGB":
image = image.convert("RGB")
# Resize the image for faster processing
image = image.resize((150, 150))
# Convert to numpy array
img_array = np.array(image)
# Check if the image has a valid shape
if img_array.ndim != 3 or img_array.shape[2] != 3:
raise ValueError("Invalid image array shape: Expected a 3D array with 3 channels.")
# Reshape image to be a list of pixels
pixels = img_array.reshape((-1, 3))
print(f"Image shape: {img_array.shape}") # Debugging line
print(f"Number of pixels: {len(pixels)}") # Debugging line
if len(pixels) < 5:
return "Image has too few pixels for analysis"
kmeans = KMeans(n_clusters=5, random_state=0)
kmeans.fit(pixels)
dominant_colors = kmeans.cluster_centers_
# Plot the colors for visualization
plt.figure(figsize=(8, 6))
plt.imshow([dominant_colors.astype(int)])
plt.axis('off')
plt.show()
return dominant_colors
except Exception as e:
print(f"Error in analyze_colors: {e}") # Capture the error
return None
# Function to detect emotions from colors (simplified emotion-color mapping)
def color_emotion_analysis(dominant_colors):
try:
emotions = []
for color in dominant_colors:
# Simple logic: darker tones could indicate sadness
if np.mean(color) < 85:
emotions.append("Sadness")
elif np.mean(color) > 170:
emotions.append("Happiness")
else:
emotions.append("Neutral")
return emotions
except Exception as e:
print(f"Error in color_emotion_analysis: {e}")
return ["Error analyzing emotions"]
# Function to analyze patterns and shapes using OpenCV
def analyze_patterns(image):
try:
# Convert to grayscale for edge detection
gray_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2GRAY)
edges = cv2.Canny(gray_image, 100, 200)
# Calculate the number of edges (chaos metric)
num_edges = np.sum(edges > 0)
if num_edges > 10000: # Arbitrary threshold for "chaos"
return "Chaotic patterns - possibly distress"
else:
return "Orderly patterns - possibly calm"
except Exception as e:
print(f"Error in analyze_patterns: {e}")
return "Error analyzing patterns"
# Main function to process image and analyze emotional expression
def analyze_emotion_from_image(image):
try:
# Ensure the input image is a PIL image
if isinstance(image, np.ndarray):
image = Image.fromarray(image) # Convert to PIL Image if it's a NumPy array
# Analyze colors
dominant_colors = analyze_colors(image)
if dominant_colors is None:
return "Error analyzing colors"
color_emotions = color_emotion_analysis(dominant_colors)
# Analyze patterns
pattern_analysis = analyze_patterns(image)
return f"Color-based emotions: {color_emotions}\nPattern analysis: {pattern_analysis}"
except Exception as e:
return f"Error processing image: {str(e)}"
# Gradio interface to upload image files and perform analysis
iface = gr.Interface(fn=analyze_emotion_from_image, inputs="image", outputs="text")
# Launch the interface
if __name__ == "__main__":
iface.launch()
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