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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: # landmark-scale
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
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