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# built-in dependencies
import os
import time
from typing import List, Tuple, Optional
# 3rd party dependencies
import numpy as np
import pandas as pd
import cv2
# project dependencies
from deepface import DeepFace
from deepface.commons import logger as log
logger = log.get_singletonish_logger()
# dependency configuration
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
IDENTIFIED_IMG_SIZE = 112
TEXT_COLOR = (255, 255, 255)
def analysis(
db_path: str,
model_name="VGG-Face",
detector_backend="opencv",
distance_metric="cosine",
enable_face_analysis=True,
source=0,
time_threshold=5,
frame_threshold=5,
):
"""
Run real time face recognition and facial attribute analysis
Args:
db_path (string): Path to the folder containing image files. All detected faces
in the database will be considered in the decision-making process.
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
(default is opencv).
distance_metric (string): Metric for measuring similarity. Options: 'cosine',
'euclidean', 'euclidean_l2' (default is cosine).
enable_face_analysis (bool): Flag to enable face analysis (default is True).
source (Any): The source for the video stream (default is 0, which represents the
default camera).
time_threshold (int): The time threshold (in seconds) for face recognition (default is 5).
frame_threshold (int): The frame threshold for face recognition (default is 5).
Returns:
None
"""
# initialize models
build_demography_models(enable_face_analysis=enable_face_analysis)
build_facial_recognition_model(model_name=model_name)
# call a dummy find function for db_path once to create embeddings before starting webcam
_ = search_identity(
detected_face=np.zeros([224, 224, 3]),
db_path=db_path,
detector_backend=detector_backend,
distance_metric=distance_metric,
model_name=model_name,
)
freezed_img = None
freeze = False
num_frames_with_faces = 0
tic = time.time()
cap = cv2.VideoCapture(source) # webcam
while True:
has_frame, img = cap.read()
if not has_frame:
break
# we are adding some figures into img such as identified facial image, age, gender
# that is why, we need raw image itself to make analysis
raw_img = img.copy()
faces_coordinates = []
if freeze is False:
faces_coordinates = grab_facial_areas(img=img, detector_backend=detector_backend)
# we will pass img to analyze modules (identity, demography) and add some illustrations
# that is why, we will not be able to extract detected face from img clearly
detected_faces = extract_facial_areas(img=img, faces_coordinates=faces_coordinates)
img = highlight_facial_areas(img=img, faces_coordinates=faces_coordinates)
img = countdown_to_freeze(
img=img,
faces_coordinates=faces_coordinates,
frame_threshold=frame_threshold,
num_frames_with_faces=num_frames_with_faces,
)
num_frames_with_faces = num_frames_with_faces + 1 if len(faces_coordinates) else 0
freeze = num_frames_with_faces > 0 and num_frames_with_faces % frame_threshold == 0
if freeze:
# add analyze results into img - derive from raw_img
img = highlight_facial_areas(img=raw_img, faces_coordinates=faces_coordinates)
# age, gender and emotion analysis
img = perform_demography_analysis(
enable_face_analysis=enable_face_analysis,
img=raw_img,
faces_coordinates=faces_coordinates,
detected_faces=detected_faces,
)
# facial recogntion analysis
img = perform_facial_recognition(
img=img,
faces_coordinates=faces_coordinates,
detected_faces=detected_faces,
db_path=db_path,
detector_backend=detector_backend,
distance_metric=distance_metric,
model_name=model_name,
)
# freeze the img after analysis
freezed_img = img.copy()
# start counter for freezing
tic = time.time()
logger.info("freezed")
elif freeze is True and time.time() - tic > time_threshold:
freeze = False
freezed_img = None
# reset counter for freezing
tic = time.time()
logger.info("freeze released")
freezed_img = countdown_to_release(img=freezed_img, tic=tic, time_threshold=time_threshold)
cv2.imshow("img", img if freezed_img is None else freezed_img)
if cv2.waitKey(1) & 0xFF == ord("q"): # press q to quit
break
# kill open cv things
cap.release()
cv2.destroyAllWindows()
def build_facial_recognition_model(model_name: str) -> None:
"""
Build facial recognition model
Args:
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
Returns
input_shape (tuple): input shape of given facial recognitio n model.
"""
_ = DeepFace.build_model(model_name=model_name)
logger.info(f"{model_name} is built")
def search_identity(
detected_face: np.ndarray,
db_path: str,
model_name: str,
detector_backend: str,
distance_metric: str,
) -> Tuple[Optional[str], Optional[np.ndarray]]:
"""
Search an identity in facial database.
Args:
detected_face (np.ndarray): extracted individual facial image
db_path (string): Path to the folder containing image files. All detected faces
in the database will be considered in the decision-making process.
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
(default is opencv).
distance_metric (string): Metric for measuring similarity. Options: 'cosine',
'euclidean', 'euclidean_l2' (default is cosine).
Returns:
result (tuple): result consisting of following objects
identified image path (str)
identified image itself (np.ndarray)
"""
target_path = None
try:
dfs = DeepFace.find(
img_path=detected_face,
db_path=db_path,
model_name=model_name,
detector_backend=detector_backend,
distance_metric=distance_metric,
enforce_detection=False,
silent=True,
)
except ValueError as err:
if f"No item found in {db_path}" in str(err):
logger.warn(
f"No item is found in {db_path}."
"So, no facial recognition analysis will be performed."
)
dfs = []
else:
raise err
if len(dfs) == 0:
# you may consider to return unknown person's image here
return None, None
# detected face is coming from parent, safe to access 1st index
df = dfs[0]
if df.shape[0] == 0:
return None, None
candidate = df.iloc[0]
target_path = candidate["identity"]
logger.info(f"Hello, {target_path}")
# load found identity image - extracted if possible
target_objs = DeepFace.extract_faces(
img_path=target_path,
detector_backend=detector_backend,
enforce_detection=False,
align=True,
)
# extract facial area of the identified image if and only if it has one face
# otherwise, show image as is
if len(target_objs) == 1:
# extract 1st item directly
target_obj = target_objs[0]
target_img = target_obj["face"]
target_img = cv2.resize(target_img, (IDENTIFIED_IMG_SIZE, IDENTIFIED_IMG_SIZE))
target_img *= 255
target_img = target_img[:, :, ::-1]
else:
target_img = cv2.imread(target_path)
return target_path.split("/")[-1], target_img
def build_demography_models(enable_face_analysis: bool) -> None:
"""
Build demography analysis models
Args:
enable_face_analysis (bool): Flag to enable face analysis (default is True).
Returns:
None
"""
if enable_face_analysis is False:
return
DeepFace.build_model(model_name="Age")
logger.info("Age model is just built")
DeepFace.build_model(model_name="Gender")
logger.info("Gender model is just built")
DeepFace.build_model(model_name="Emotion")
logger.info("Emotion model is just built")
def highlight_facial_areas(
img: np.ndarray, faces_coordinates: List[Tuple[int, int, int, int]]
) -> np.ndarray:
"""
Highlight detected faces with rectangles in the given image
Args:
img (np.ndarray): image itself
faces_coordinates (list): list of face coordinates as tuple with x, y, w and h
Returns:
img (np.ndarray): image with highlighted facial areas
"""
for x, y, w, h in faces_coordinates:
# highlight facial area with rectangle
cv2.rectangle(img, (x, y), (x + w, y + h), (67, 67, 67), 1)
return img
def countdown_to_freeze(
img: np.ndarray,
faces_coordinates: List[Tuple[int, int, int, int]],
frame_threshold: int,
num_frames_with_faces: int,
) -> np.ndarray:
"""
Highlight time to freeze in the image's facial areas
Args:
img (np.ndarray): image itself
faces_coordinates (list): list of face coordinates as tuple with x, y, w and h
frame_threshold (int): how many sequantial frames required with face(s) to freeze
num_frames_with_faces (int): how many sequantial frames do we have with face(s)
Returns:
img (np.ndarray): image with counter values
"""
for x, y, w, h in faces_coordinates:
cv2.putText(
img,
str(frame_threshold - (num_frames_with_faces % frame_threshold)),
(int(x + w / 4), int(y + h / 1.5)),
cv2.FONT_HERSHEY_SIMPLEX,
4,
(255, 255, 255),
2,
)
return img
def countdown_to_release(
img: Optional[np.ndarray], tic: float, time_threshold: int
) -> Optional[np.ndarray]:
"""
Highlight time to release the freezing in the image top left area
Args:
img (np.ndarray): image itself
tic (float): time specifying when freezing started
time_threshold (int): freeze time threshold
Returns:
img (np.ndarray): image with time to release the freezing
"""
# do not take any action if it is not frozen yet
if img is None:
return img
toc = time.time()
time_left = int(time_threshold - (toc - tic) + 1)
cv2.rectangle(img, (10, 10), (90, 50), (67, 67, 67), -10)
cv2.putText(
img,
str(time_left),
(40, 40),
cv2.FONT_HERSHEY_SIMPLEX,
1,
(255, 255, 255),
1,
)
return img
def grab_facial_areas(
img: np.ndarray, detector_backend: str, threshold: int = 130
) -> List[Tuple[int, int, int, int]]:
"""
Find facial area coordinates in the given image
Args:
img (np.ndarray): image itself
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
(default is opencv).
threshold (int): threshold for facial area, discard smaller ones
Returns
result (list): list of tuple with x, y, w and h coordinates
"""
try:
face_objs = DeepFace.extract_faces(
img_path=img,
detector_backend=detector_backend,
# you may consider to extract with larger expanding value
expand_percentage=0,
)
faces = [
(
face_obj["facial_area"]["x"],
face_obj["facial_area"]["y"],
face_obj["facial_area"]["w"],
face_obj["facial_area"]["h"],
)
for face_obj in face_objs
if face_obj["facial_area"]["w"] > threshold
]
return faces
except: # to avoid exception if no face detected
return []
def extract_facial_areas(
img: np.ndarray, faces_coordinates: List[Tuple[int, int, int, int]]
) -> List[np.ndarray]:
"""
Extract facial areas as numpy array from given image
Args:
img (np.ndarray): image itself
faces_coordinates (list): list of facial area coordinates as tuple with
x, y, w and h values
Returns:
detected_faces (list): list of detected facial area images
"""
detected_faces = []
for x, y, w, h in faces_coordinates:
detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
detected_faces.append(detected_face)
return detected_faces
def perform_facial_recognition(
img: np.ndarray,
detected_faces: List[np.ndarray],
faces_coordinates: List[Tuple[int, int, int, int]],
db_path: str,
detector_backend: str,
distance_metric: str,
model_name: str,
) -> np.ndarray:
"""
Perform facial recognition
Args:
img (np.ndarray): image itself
detected_faces (list): list of extracted detected face images as numpy
faces_coordinates (list): list of facial area coordinates as tuple with
x, y, w and h values
db_path (string): Path to the folder containing image files. All detected faces
in the database will be considered in the decision-making process.
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
(default is opencv).
distance_metric (string): Metric for measuring similarity. Options: 'cosine',
'euclidean', 'euclidean_l2' (default is cosine).
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
Returns:
img (np.ndarray): image with identified face informations
"""
for idx, (x, y, w, h) in enumerate(faces_coordinates):
detected_face = detected_faces[idx]
target_label, target_img = search_identity(
detected_face=detected_face,
db_path=db_path,
detector_backend=detector_backend,
distance_metric=distance_metric,
model_name=model_name,
)
if target_label is None:
continue
img = overlay_identified_face(
img=img,
target_img=target_img,
label=target_label,
x=x,
y=y,
w=w,
h=h,
)
return img
def perform_demography_analysis(
enable_face_analysis: bool,
img: np.ndarray,
faces_coordinates: List[Tuple[int, int, int, int]],
detected_faces: List[np.ndarray],
) -> np.ndarray:
"""
Perform demography analysis on given image
Args:
enable_face_analysis (bool): Flag to enable face analysis.
img (np.ndarray): image itself
faces_coordinates (list): list of face coordinates as tuple with
x, y, w and h values
detected_faces (list): list of extracted detected face images as numpy
Returns:
img (np.ndarray): image with analyzed demography information
"""
if enable_face_analysis is False:
return img
for idx, (x, y, w, h) in enumerate(faces_coordinates):
detected_face = detected_faces[idx]
demographies = DeepFace.analyze(
img_path=detected_face,
actions=("age", "gender", "emotion"),
detector_backend="skip",
enforce_detection=False,
silent=True,
)
if len(demographies) == 0:
continue
# safe to access 1st index because detector backend is skip
demography = demographies[0]
img = overlay_emotion(img=img, emotion_probas=demography["emotion"], x=x, y=y, w=w, h=h)
img = overlay_age_gender(
img=img,
apparent_age=demography["age"],
gender=demography["dominant_gender"][0:1], # M or W
x=x,
y=y,
w=w,
h=h,
)
return img
def overlay_identified_face(
img: np.ndarray,
target_img: np.ndarray,
label: str,
x: int,
y: int,
w: int,
h: int,
) -> np.ndarray:
"""
Overlay the identified face onto image itself
Args:
img (np.ndarray): image itself
target_img (np.ndarray): identified face's image
label (str): name of the identified face
x (int): x coordinate of the face on the given image
y (int): y coordinate of the face on the given image
w (int): w coordinate of the face on the given image
h (int): h coordinate of the face on the given image
Returns:
img (np.ndarray): image with overlayed identity
"""
try:
if y - IDENTIFIED_IMG_SIZE > 0 and x + w + IDENTIFIED_IMG_SIZE < img.shape[1]:
# top right
img[
y - IDENTIFIED_IMG_SIZE : y,
x + w : x + w + IDENTIFIED_IMG_SIZE,
] = target_img
overlay = img.copy()
opacity = 0.4
cv2.rectangle(
img,
(x + w, y),
(x + w + IDENTIFIED_IMG_SIZE, y + 20),
(46, 200, 255),
cv2.FILLED,
)
cv2.addWeighted(
overlay,
opacity,
img,
1 - opacity,
0,
img,
)
cv2.putText(
img,
label,
(x + w, y + 10),
cv2.FONT_HERSHEY_SIMPLEX,
0.5,
TEXT_COLOR,
1,
)
# connect face and text
cv2.line(
img,
(x + int(w / 2), y),
(x + 3 * int(w / 4), y - int(IDENTIFIED_IMG_SIZE / 2)),
(67, 67, 67),
1,
)
cv2.line(
img,
(x + 3 * int(w / 4), y - int(IDENTIFIED_IMG_SIZE / 2)),
(x + w, y - int(IDENTIFIED_IMG_SIZE / 2)),
(67, 67, 67),
1,
)
elif y + h + IDENTIFIED_IMG_SIZE < img.shape[0] and x - IDENTIFIED_IMG_SIZE > 0:
# bottom left
img[
y + h : y + h + IDENTIFIED_IMG_SIZE,
x - IDENTIFIED_IMG_SIZE : x,
] = target_img
overlay = img.copy()
opacity = 0.4
cv2.rectangle(
img,
(x - IDENTIFIED_IMG_SIZE, y + h - 20),
(x, y + h),
(46, 200, 255),
cv2.FILLED,
)
cv2.addWeighted(
overlay,
opacity,
img,
1 - opacity,
0,
img,
)
cv2.putText(
img,
label,
(x - IDENTIFIED_IMG_SIZE, y + h - 10),
cv2.FONT_HERSHEY_SIMPLEX,
0.5,
TEXT_COLOR,
1,
)
# connect face and text
cv2.line(
img,
(x + int(w / 2), y + h),
(
x + int(w / 2) - int(w / 4),
y + h + int(IDENTIFIED_IMG_SIZE / 2),
),
(67, 67, 67),
1,
)
cv2.line(
img,
(
x + int(w / 2) - int(w / 4),
y + h + int(IDENTIFIED_IMG_SIZE / 2),
),
(x, y + h + int(IDENTIFIED_IMG_SIZE / 2)),
(67, 67, 67),
1,
)
elif y - IDENTIFIED_IMG_SIZE > 0 and x - IDENTIFIED_IMG_SIZE > 0:
# top left
img[y - IDENTIFIED_IMG_SIZE : y, x - IDENTIFIED_IMG_SIZE : x] = target_img
overlay = img.copy()
opacity = 0.4
cv2.rectangle(
img,
(x - IDENTIFIED_IMG_SIZE, y),
(x, y + 20),
(46, 200, 255),
cv2.FILLED,
)
cv2.addWeighted(
overlay,
opacity,
img,
1 - opacity,
0,
img,
)
cv2.putText(
img,
label,
(x - IDENTIFIED_IMG_SIZE, y + 10),
cv2.FONT_HERSHEY_SIMPLEX,
0.5,
TEXT_COLOR,
1,
)
# connect face and text
cv2.line(
img,
(x + int(w / 2), y),
(
x + int(w / 2) - int(w / 4),
y - int(IDENTIFIED_IMG_SIZE / 2),
),
(67, 67, 67),
1,
)
cv2.line(
img,
(
x + int(w / 2) - int(w / 4),
y - int(IDENTIFIED_IMG_SIZE / 2),
),
(x, y - int(IDENTIFIED_IMG_SIZE / 2)),
(67, 67, 67),
1,
)
elif (
x + w + IDENTIFIED_IMG_SIZE < img.shape[1]
and y + h + IDENTIFIED_IMG_SIZE < img.shape[0]
):
# bottom righ
img[
y + h : y + h + IDENTIFIED_IMG_SIZE,
x + w : x + w + IDENTIFIED_IMG_SIZE,
] = target_img
overlay = img.copy()
opacity = 0.4
cv2.rectangle(
img,
(x + w, y + h - 20),
(x + w + IDENTIFIED_IMG_SIZE, y + h),
(46, 200, 255),
cv2.FILLED,
)
cv2.addWeighted(
overlay,
opacity,
img,
1 - opacity,
0,
img,
)
cv2.putText(
img,
label,
(x + w, y + h - 10),
cv2.FONT_HERSHEY_SIMPLEX,
0.5,
TEXT_COLOR,
1,
)
# connect face and text
cv2.line(
img,
(x + int(w / 2), y + h),
(
x + int(w / 2) + int(w / 4),
y + h + int(IDENTIFIED_IMG_SIZE / 2),
),
(67, 67, 67),
1,
)
cv2.line(
img,
(
x + int(w / 2) + int(w / 4),
y + h + int(IDENTIFIED_IMG_SIZE / 2),
),
(x + w, y + h + int(IDENTIFIED_IMG_SIZE / 2)),
(67, 67, 67),
1,
)
else:
logger.info("cannot put facial recognition info on the image")
except Exception as err: # pylint: disable=broad-except
logger.error(str(err))
return img
def overlay_emotion(
img: np.ndarray, emotion_probas: dict, x: int, y: int, w: int, h: int
) -> np.ndarray:
"""
Overlay the analyzed emotion of face onto image itself
Args:
img (np.ndarray): image itself
emotion_probas (dict): probability of different emotionas dictionary
x (int): x coordinate of the face on the given image
y (int): y coordinate of the face on the given image
w (int): w coordinate of the face on the given image
h (int): h coordinate of the face on the given image
Returns:
img (np.ndarray): image with overlay emotion analsis results
"""
emotion_df = pd.DataFrame(emotion_probas.items(), columns=["emotion", "score"])
emotion_df = emotion_df.sort_values(by=["score"], ascending=False).reset_index(drop=True)
# background of mood box
# transparency
overlay = img.copy()
opacity = 0.4
# put gray background to the right of the detected image
if x + w + IDENTIFIED_IMG_SIZE < img.shape[1]:
cv2.rectangle(
img,
(x + w, y),
(x + w + IDENTIFIED_IMG_SIZE, y + h),
(64, 64, 64),
cv2.FILLED,
)
cv2.addWeighted(overlay, opacity, img, 1 - opacity, 0, img)
# put gray background to the left of the detected image
elif x - IDENTIFIED_IMG_SIZE > 0:
cv2.rectangle(
img,
(x - IDENTIFIED_IMG_SIZE, y),
(x, y + h),
(64, 64, 64),
cv2.FILLED,
)
cv2.addWeighted(overlay, opacity, img, 1 - opacity, 0, img)
for index, instance in emotion_df.iterrows():
current_emotion = instance["emotion"]
emotion_label = f"{current_emotion} "
emotion_score = instance["score"] / 100
filled_bar_x = 35 # this is the size if an emotion is 100%
bar_x = int(filled_bar_x * emotion_score)
if x + w + IDENTIFIED_IMG_SIZE < img.shape[1]:
text_location_y = y + 20 + (index + 1) * 20
text_location_x = x + w
if text_location_y < y + h:
cv2.putText(
img,
emotion_label,
(text_location_x, text_location_y),
cv2.FONT_HERSHEY_SIMPLEX,
0.5,
(255, 255, 255),
1,
)
cv2.rectangle(
img,
(x + w + 70, y + 13 + (index + 1) * 20),
(
x + w + 70 + bar_x,
y + 13 + (index + 1) * 20 + 5,
),
(255, 255, 255),
cv2.FILLED,
)
elif x - IDENTIFIED_IMG_SIZE > 0:
text_location_y = y + 20 + (index + 1) * 20
text_location_x = x - IDENTIFIED_IMG_SIZE
if text_location_y <= y + h:
cv2.putText(
img,
emotion_label,
(text_location_x, text_location_y),
cv2.FONT_HERSHEY_SIMPLEX,
0.5,
(255, 255, 255),
1,
)
cv2.rectangle(
img,
(
x - IDENTIFIED_IMG_SIZE + 70,
y + 13 + (index + 1) * 20,
),
(
x - IDENTIFIED_IMG_SIZE + 70 + bar_x,
y + 13 + (index + 1) * 20 + 5,
),
(255, 255, 255),
cv2.FILLED,
)
return img
def overlay_age_gender(
img: np.ndarray, apparent_age: float, gender: str, x: int, y: int, w: int, h: int
) -> np.ndarray:
"""
Overlay the analyzed age and gender of face onto image itself
Args:
img (np.ndarray): image itself
apparent_age (float): analyzed apparent age
gender (str): analyzed gender
x (int): x coordinate of the face on the given image
y (int): y coordinate of the face on the given image
w (int): w coordinate of the face on the given image
h (int): h coordinate of the face on the given image
Returns:
img (np.ndarray): image with overlay age and gender analsis results
"""
logger.debug(f"{apparent_age} years old {gender}")
analysis_report = f"{int(apparent_age)} {gender}"
info_box_color = (46, 200, 255)
# show its age and gender on the top of the image
if y - IDENTIFIED_IMG_SIZE + int(IDENTIFIED_IMG_SIZE / 5) > 0:
triangle_coordinates = np.array(
[
(x + int(w / 2), y),
(
x + int(w / 2) - int(w / 10),
y - int(IDENTIFIED_IMG_SIZE / 3),
),
(
x + int(w / 2) + int(w / 10),
y - int(IDENTIFIED_IMG_SIZE / 3),
),
]
)
cv2.drawContours(
img,
[triangle_coordinates],
0,
info_box_color,
-1,
)
cv2.rectangle(
img,
(
x + int(w / 5),
y - IDENTIFIED_IMG_SIZE + int(IDENTIFIED_IMG_SIZE / 5),
),
(x + w - int(w / 5), y - int(IDENTIFIED_IMG_SIZE / 3)),
info_box_color,
cv2.FILLED,
)
cv2.putText(
img,
analysis_report,
(x + int(w / 3.5), y - int(IDENTIFIED_IMG_SIZE / 2.1)),
cv2.FONT_HERSHEY_SIMPLEX,
1,
(0, 111, 255),
2,
)
# show its age and gender on the top of the image
elif y + h + IDENTIFIED_IMG_SIZE - int(IDENTIFIED_IMG_SIZE / 5) < img.shape[0]:
triangle_coordinates = np.array(
[
(x + int(w / 2), y + h),
(
x + int(w / 2) - int(w / 10),
y + h + int(IDENTIFIED_IMG_SIZE / 3),
),
(
x + int(w / 2) + int(w / 10),
y + h + int(IDENTIFIED_IMG_SIZE / 3),
),
]
)
cv2.drawContours(
img,
[triangle_coordinates],
0,
info_box_color,
-1,
)
cv2.rectangle(
img,
(x + int(w / 5), y + h + int(IDENTIFIED_IMG_SIZE / 3)),
(
x + w - int(w / 5),
y + h + IDENTIFIED_IMG_SIZE - int(IDENTIFIED_IMG_SIZE / 5),
),
info_box_color,
cv2.FILLED,
)
cv2.putText(
img,
analysis_report,
(x + int(w / 3.5), y + h + int(IDENTIFIED_IMG_SIZE / 1.5)),
cv2.FONT_HERSHEY_SIMPLEX,
1,
(0, 111, 255),
2,
)
return img
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