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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 = [
"left most",
"right most",
"top most",
"bottom most",
"most width",
"most height",
"best detection",
]
swap_options_list = [
"All face",
"Age less than",
"Age greater than",
"All Male",
"All Female",
"Specific Face",
]
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
total_faces = len(faces)
if total_faces == 1:
return faces[0]
print(f"{total_faces} face detected. Using {detect_condition} face.")
if detect_condition == "left most":
return sorted(faces, key=lambda face: face["bbox"][0])[0]
elif detect_condition == "right most":
return sorted(faces, key=lambda face: face["bbox"][0])[-1]
elif detect_condition == "top most":
return sorted(faces, key=lambda face: face["bbox"][1])[0]
elif detect_condition == "bottom most":
return sorted(faces, key=lambda face: face["bbox"][1])[-1]
elif detect_condition == "most width":
return sorted(faces, key=lambda face: face["bbox"][2])[-1]
elif detect_condition == "most height":
return sorted(faces, key=lambda face: face["bbox"][3])[-1]
elif detect_condition == "best detection":
return sorted(faces, key=lambda face: face["det_score"])[-1]
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
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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