"
# Detection results - show all detected faces
if detections:
for i, detection in enumerate(detections):
face_image = detection.get("face", "")
first_face_index = detection.get("firstFaceIndex")
second_face_index = detection.get("secondFaceIndex")
# Matching results in the new table format
if matches:
html += """
| First Image Face |
Second Image Face |
Similarity Score |
Result |
"""
# Group matches by first image face index for better organization
match_groups = {}
for match in matches:
first_face_index = match.get("firstFaceIndex", "N/A")
if first_face_index not in match_groups:
match_groups[first_face_index] = []
match_groups[first_face_index].append(match)
row_number = 1
for first_face_index in sorted(match_groups.keys()):
for match in match_groups[first_face_index]:
first_face_index = match.get("firstFaceIndex", "N/A")
second_face_index = match.get("secondFaceIndex", "N/A")
similarity = match.get("similarity", 0)
# Get face images for display
first_face_img = ""
second_face_img = ""
for detection in detections:
if detection.get("firstFaceIndex") == first_face_index:
first_face_img = detection.get("face", "")
if detection.get("secondFaceIndex") == second_face_index:
second_face_img = detection.get("face", "")
# Determine result and color
if similarity >= 0.6: # Threshold for same person
result_text = "same person"
result_class = "result-same"
else:
result_text = "different person"
result_class = "result-different"
first_face_display = f"
" if first_face_img else f"Face {first_face_index}"
second_face_display = f"
" if second_face_img else f"Face {second_face_index}"
html += f"""
{first_face_display}
Face {first_face_index}
|
{second_face_display}
Face {second_face_index}
|
{similarity:.4f} |
{result_text} |
"""
row_number += 1
html += """
"""
else:
html += "
No face matches found.
"
html += "
