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#!/usr/bin/env python3
"""
Diagnose the green/colorless image issue
"""
import os
import cv2
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
def analyze_image_colors(image_path):
"""Analyze color distribution in an image"""
print(f"\n🔍 Analyzing: {image_path}")
print("=" * 50)
if not os.path.exists(image_path):
print("❌ Image not found")
return
# Read image
img = cv2.imread(image_path)
if img is None:
print("❌ Failed to read image")
return
h, w = img.shape[:2]
print(f"📐 Dimensions: {w}x{h}")
# Analyze color channels
b, g, r = cv2.split(img)
print(f"\n📊 Channel Statistics:")
print(f" Blue: mean={b.mean():.1f}, std={b.std():.1f}, min={b.min()}, max={b.max()}")
print(f" Green: mean={g.mean():.1f}, std={g.std():.1f}, min={g.min()}, max={g.max()}")
print(f" Red: mean={r.mean():.1f}, std={r.std():.1f}, min={r.min()}, max={r.max()}")
# Check for channel imbalance
channel_means = [b.mean(), g.mean(), r.mean()]
max_diff = max(channel_means) - min(channel_means)
if max_diff > 30:
print(f"\n⚠️ Channel imbalance detected! Difference: {max_diff:.1f}")
dominant = ['Blue', 'Green', 'Red'][channel_means.index(max(channel_means))]
print(f" Dominant channel: {dominant}")
else:
print(f"\n✅ Color balance looks normal (diff: {max_diff:.1f})")
# Check if image is mostly gray
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
color_diff = np.mean(np.abs(img - gray[:,:,np.newaxis]))
if color_diff < 10:
print(f"\n⚠️ Image appears to be grayscale (color diff: {color_diff:.1f})")
else:
print(f"\n✅ Image has color information (color diff: {color_diff:.1f})")
# Create visualization
fig, axes = plt.subplots(2, 2, figsize=(10, 10))
# Original image
axes[0, 0].imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
axes[0, 0].set_title('Original Image')
axes[0, 0].axis('off')
# Color channels
axes[0, 1].imshow(r, cmap='Reds')
axes[0, 1].set_title('Red Channel')
axes[0, 1].axis('off')
axes[1, 0].imshow(g, cmap='Greens')
axes[1, 0].set_title('Green Channel')
axes[1, 0].axis('off')
axes[1, 1].imshow(b, cmap='Blues')
axes[1, 1].set_title('Blue Channel')
axes[1, 1].axis('off')
# Save analysis
output_path = image_path.replace('.png', '_color_analysis.png')
plt.savefig(output_path)
plt.close()
print(f"\n📊 Saved color analysis: {output_path}")
def compare_before_after():
"""Compare frames before and after enhancement"""
print("\n🔬 Comparing Enhancement Effects")
print("=" * 50)
# Check for test frames
test_frames = []
if os.path.exists('frames/final'):
frames = [f for f in os.listdir('frames/final') if f.endswith('.png')]
if frames:
test_frames = [os.path.join('frames/final', frames[0])]
if len(frames) > 20:
test_frames.append(os.path.join('frames/final', frames[20]))
for frame_path in test_frames:
analyze_image_colors(frame_path)
if __name__ == "__main__":
print("🎨 Color Issue Diagnostic Tool")
print("=" * 50)
# Run diagnostics
compare_before_after()
print("\n💡 Recommendations:")
print("1. If green tint: Check color channel processing")
print("2. If grayscale: Check color space conversions")
print("3. If channel imbalance: Check enhancement algorithms")
print("\n✅ The fixed enhancement should preserve original colors!") |