|
import os |
|
import cv2 |
|
import numpy as np |
|
from tqdm import tqdm |
|
from utils import scale_bbox_from_center |
|
|
|
detect_conditions = [ |
|
"best detection", |
|
"left most", |
|
"right most", |
|
"top most", |
|
"bottom most", |
|
"middle", |
|
"biggest", |
|
"smallest", |
|
] |
|
|
|
swap_options_list = [ |
|
"All Face", |
|
"Specific Face", |
|
"Age less than", |
|
"Age greater than", |
|
"All Male", |
|
"All Female", |
|
"Left Most", |
|
"Right Most", |
|
"Top Most", |
|
"Bottom Most", |
|
"Middle", |
|
"Biggest", |
|
"Smallest", |
|
] |
|
|
|
def get_single_face(faces, method="best detection"): |
|
total_faces = len(faces) |
|
if total_faces == 1: |
|
return faces[0] |
|
|
|
print(f"{total_faces} face detected. Using {method} face.") |
|
if method == "best detection": |
|
return sorted(faces, key=lambda face: face["det_score"])[-1] |
|
elif method == "left most": |
|
return sorted(faces, key=lambda face: face["bbox"][0])[0] |
|
elif method == "right most": |
|
return sorted(faces, key=lambda face: face["bbox"][0])[-1] |
|
elif method == "top most": |
|
return sorted(faces, key=lambda face: face["bbox"][1])[0] |
|
elif method == "bottom most": |
|
return sorted(faces, key=lambda face: face["bbox"][1])[-1] |
|
elif method == "middle": |
|
return sorted(faces, key=lambda face: ( |
|
(face["bbox"][0] + face["bbox"][2]) / 2 - 0.5) ** 2 + |
|
((face["bbox"][1] + face["bbox"][3]) / 2 - 0.5) ** 2)[len(faces) // 2] |
|
elif method == "biggest": |
|
return sorted(faces, key=lambda face: (face["bbox"][2] - face["bbox"][0]) * (face["bbox"][3] - face["bbox"][1]))[-1] |
|
elif method == "smallest": |
|
return sorted(faces, key=lambda face: (face["bbox"][2] - face["bbox"][0]) * (face["bbox"][3] - face["bbox"][1]))[0] |
|
|
|
|
|
def analyse_face(image, model, return_single_face=True, detect_condition="best detection", scale=1.0): |
|
faces = model.get(image) |
|
if scale != 1: |
|
for i, face in enumerate(faces): |
|
landmark = face['kps'] |
|
center = np.mean(landmark, axis=0) |
|
landmark = center + (landmark - center) * scale |
|
faces[i]['kps'] = landmark |
|
|
|
if not return_single_face: |
|
return faces |
|
|
|
return get_single_face(faces, method=detect_condition) |
|
|
|
|
|
def cosine_distance(a, b): |
|
a /= np.linalg.norm(a) |
|
b /= np.linalg.norm(b) |
|
return 1 - np.dot(a, b) |
|
|
|
|
|
def get_analysed_data(face_analyser, image_sequence, source_data, swap_condition="All face", detect_condition="left most", scale=1.0): |
|
if swap_condition != "Specific Face": |
|
source_path, age = source_data |
|
source_image = cv2.imread(source_path) |
|
analysed_source = analyse_face(source_image, face_analyser, return_single_face=True, detect_condition=detect_condition, scale=scale) |
|
else: |
|
analysed_source_specifics = [] |
|
source_specifics, threshold = source_data |
|
for source, specific in zip(*source_specifics): |
|
if source is None or specific is None: |
|
continue |
|
analysed_source = analyse_face(source, face_analyser, return_single_face=True, detect_condition=detect_condition, scale=scale) |
|
analysed_specific = analyse_face(specific, face_analyser, return_single_face=True, detect_condition=detect_condition, scale=scale) |
|
analysed_source_specifics.append([analysed_source, analysed_specific]) |
|
|
|
analysed_target_list = [] |
|
analysed_source_list = [] |
|
whole_frame_eql_list = [] |
|
num_faces_per_frame = [] |
|
|
|
total_frames = len(image_sequence) |
|
curr_idx = 0 |
|
for curr_idx, frame_path in tqdm(enumerate(image_sequence), total=total_frames, desc="Analysing face data"): |
|
frame = cv2.imread(frame_path) |
|
analysed_faces = analyse_face(frame, face_analyser, return_single_face=False, detect_condition=detect_condition, scale=scale) |
|
|
|
n_faces = 0 |
|
for analysed_face in analysed_faces: |
|
if swap_condition == "All Face": |
|
analysed_target_list.append(analysed_face) |
|
analysed_source_list.append(analysed_source) |
|
whole_frame_eql_list.append(frame_path) |
|
n_faces += 1 |
|
elif swap_condition == "Age less than" and analysed_face["age"] < age: |
|
analysed_target_list.append(analysed_face) |
|
analysed_source_list.append(analysed_source) |
|
whole_frame_eql_list.append(frame_path) |
|
n_faces += 1 |
|
elif swap_condition == "Age greater than" and analysed_face["age"] > age: |
|
analysed_target_list.append(analysed_face) |
|
analysed_source_list.append(analysed_source) |
|
whole_frame_eql_list.append(frame_path) |
|
n_faces += 1 |
|
elif swap_condition == "All Male" and analysed_face["gender"] == 1: |
|
analysed_target_list.append(analysed_face) |
|
analysed_source_list.append(analysed_source) |
|
whole_frame_eql_list.append(frame_path) |
|
n_faces += 1 |
|
elif swap_condition == "All Female" and analysed_face["gender"] == 0: |
|
analysed_target_list.append(analysed_face) |
|
analysed_source_list.append(analysed_source) |
|
whole_frame_eql_list.append(frame_path) |
|
n_faces += 1 |
|
elif swap_condition == "Specific Face": |
|
for analysed_source, analysed_specific in analysed_source_specifics: |
|
distance = cosine_distance(analysed_specific["embedding"], analysed_face["embedding"]) |
|
if distance < threshold: |
|
analysed_target_list.append(analysed_face) |
|
analysed_source_list.append(analysed_source) |
|
whole_frame_eql_list.append(frame_path) |
|
n_faces += 1 |
|
|
|
if swap_condition == "Left Most": |
|
analysed_face = get_single_face(analysed_faces, method="left most") |
|
analysed_target_list.append(analysed_face) |
|
analysed_source_list.append(analysed_source) |
|
whole_frame_eql_list.append(frame_path) |
|
n_faces += 1 |
|
|
|
elif swap_condition == "Right Most": |
|
analysed_face = get_single_face(analysed_faces, method="right most") |
|
analysed_target_list.append(analysed_face) |
|
analysed_source_list.append(analysed_source) |
|
whole_frame_eql_list.append(frame_path) |
|
n_faces += 1 |
|
|
|
elif swap_condition == "Top Most": |
|
analysed_face = get_single_face(analysed_faces, method="top most") |
|
analysed_target_list.append(analysed_face) |
|
analysed_source_list.append(analysed_source) |
|
whole_frame_eql_list.append(frame_path) |
|
n_faces += 1 |
|
|
|
elif swap_condition == "Bottom Most": |
|
analysed_face = get_single_face(analysed_faces, method="bottom most") |
|
analysed_target_list.append(analysed_face) |
|
analysed_source_list.append(analysed_source) |
|
whole_frame_eql_list.append(frame_path) |
|
n_faces += 1 |
|
|
|
elif swap_condition == "Middle": |
|
analysed_face = get_single_face(analysed_faces, method="middle") |
|
analysed_target_list.append(analysed_face) |
|
analysed_source_list.append(analysed_source) |
|
whole_frame_eql_list.append(frame_path) |
|
n_faces += 1 |
|
|
|
elif swap_condition == "Biggest": |
|
analysed_face = get_single_face(analysed_faces, method="biggest") |
|
analysed_target_list.append(analysed_face) |
|
analysed_source_list.append(analysed_source) |
|
whole_frame_eql_list.append(frame_path) |
|
n_faces += 1 |
|
|
|
elif swap_condition == "Smallest": |
|
analysed_face = get_single_face(analysed_faces, method="smallest") |
|
analysed_target_list.append(analysed_face) |
|
analysed_source_list.append(analysed_source) |
|
whole_frame_eql_list.append(frame_path) |
|
n_faces += 1 |
|
|
|
num_faces_per_frame.append(n_faces) |
|
|
|
return analysed_target_list, analysed_source_list, whole_frame_eql_list, num_faces_per_frame |
|
|
