"""
Visualize all template matches on the page to understand what's happening
"""
import cv2
import numpy as np
from pathlib import Path

# Load page image
page_img_path = "test_reports_full/YDQ23_001838.pdf/doc_page.png"
page_img = cv2.imread(str(page_img_path))
if page_img is None:
    print("ERROR: Could not load page image")
    exit(1)

h, w = page_img.shape[:2]
print(f"Page size: {w}x{h}")

# Load template
template_path = "template/CMA_Logo.png"
template = cv2.imread(str(template_path), cv2.IMREAD_GRAYSCALE)
if template is None:
    print("ERROR: Could not load template")
    exit(1)

template_h, template_w = template.shape
print(f"Template size: {template_w}x{template_h}")

# Convert page to grayscale
page_gray = cv2.cvtColor(page_img, cv2.COLOR_BGR2GRAY)

# Run template matching
result = cv2.matchTemplate(page_gray, template, cv2.TM_CCORR_NORMED)

# Find all matches above different thresholds
print("\nFinding matches at different thresholds:")
for threshold in [0.3, 0.5, 0.7, 0.8, 0.9]:
    loc = np.where(result >= threshold)
    num_matches = len(loc[0])
    print(f"  Threshold {threshold}: {num_matches} matches")

# Find top 10 matches
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
print(f"\nBest match:")
print(f"  Confidence: {max_val:.3f}")
print(f"  Location: {max_loc}")
print(f"  Center: ({max_loc[0] + template_w // 2}, {max_loc[1] + template_h // 2})")

# Calculate relative position
rel_x = (max_loc[0] + template_w // 2) / w * 100
rel_y = (max_loc[1] + template_h // 2) / h * 100
print(f"  Relative position: ({rel_x:.1f}%, {rel_y:.1f}%)")

# Find all matches above 0.3
threshold = 0.3
loc = np.where(result >= threshold)

print(f"\nAll matches above {threshold}:")
matches = []
for pt in zip(*loc[::-1]):
    conf = result[pt[1], pt[0]]
    center_x = pt[0] + template_w // 2
    center_y = pt[1] + template_h // 2
    rel_x = center_x / w * 100
    rel_y = center_y / h * 100

    matches.append({
        'pos': pt,
        'conf': conf,
        'center': (center_x, center_y),
        'rel': (rel_x, rel_y)
    })

# Sort by confidence
matches.sort(key=lambda x: x['conf'], reverse=True)

for i, m in enumerate(matches[:20]):
    print(f"  Match #{i+1}:")
    print(f"    Position: {m['pos']}")
    print(f"    Center: {m['center']}")
    print(f"    Relative: ({m['rel'][0]:.1f}%, {m['rel'][1]:.1f}%)")
    print(f"    Confidence: {m['conf']:.3f}")
    print()

# Visualize top 5 matches
viz = page_img.copy()
for i, m in enumerate(matches[:5]):
    pt = m['pos']
    cv2.rectangle(viz, pt, (pt[0] + template_w, pt[1] + template_h), (0, 255, 0), 2)
    cv2.putText(viz, f"#{i+1}:{m['conf']:.2f}", (pt[0], pt[1] - 10),
                cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)

# Draw 60% threshold line
threshold_y = int(h * 0.6)
cv2.line(viz, (0, threshold_y), (w, threshold_y), (255, 0, 0), 2)
cv2.putText(viz, "60% threshold", (10, threshold_y - 10),
            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1)

output_path = "test_reports_full/YDQ23_001838.pdf/all_matches_visualization.png"
cv2.imwrite(output_path, viz)
print(f"\nVisualization saved to: {output_path}")
print(f"Top 5 matches marked with green boxes")
print(f"Red line shows 60% threshold (matches below are filtered)")