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%% Cell type:code id: tags:
``` python
from retinaface import RetinaFace
from deepface import DeepFace
import matplotlib.pyplot as plt
import cv2
import numpy as np
from utilpack.util import *
import os
import mediapipe as mp
import time
```
%% Cell type:code id: tags:
``` python
from keras.preprocessing import image
def resize_img(img,target_size,grayscale=False, return_region = False):
if img.shape[0] > 0 and img.shape[1] > 0:
factor_0 = target_size[0] / img.shape[0]
factor_1 = target_size[1] / img.shape[1]
factor = min(factor_0, factor_1)
dsize = (int(img.shape[1] * factor), int(img.shape[0] * factor))
img = cv2.resize(img, dsize)
# Then pad the other side to the target size by adding black pixels
diff_0 = target_size[0] - img.shape[0]
diff_1 = target_size[1] - img.shape[1]
if grayscale == False:
# Put the base image in the middle of the padded image
img = np.pad(img, ((diff_0 // 2, diff_0 - diff_0 // 2), (diff_1 // 2, diff_1 - diff_1 // 2), (0, 0)), 'constant')
else:
img = np.pad(img, ((diff_0 // 2, diff_0 - diff_0 // 2), (diff_1 // 2, diff_1 - diff_1 // 2)), 'constant')
#------------------------------------------
#double check: if target image is not still the same size with target.
if img.shape[0:2] != target_size:
img = cv2.resize(img, target_size)
#---------------------------------------------------
#normalizing the image pixels
img_pixels = image.img_to_array(img) #what this line doing? must?
img_pixels = np.expand_dims(img_pixels, axis = 0)
img_pixels /= 255 #normalize input in [0, 1]
#---------------------------------------------------
if return_region == True:
return img_pixels, region
else:
return img_pixels
```
%% Cell type:code id: tags:
``` python
#conda install -c conda-forge dlib
backends = ['opencv', 'ssd', 'Dlib', 'mtcnn', 'retinaface', 'mediapipe']
models = ["VGG-Face", "Facenet", "openFace", "DeepFace", "DeepID", "ArcFace", "Dlib"]
img1_path = "images/frame_screenshot_07.02.2022_1.png"
img2_path = "images/frame_screenshot_07.02.2022.png"
face = DeepFace.detectFace(img1_path,(224,224),backends[5], enforce_detection=False)
result = DeepFace.verify(img1_path, img2_path, model_name=models[5])
plt.imshow(face)
print(result)
face.shape
```
%% Output
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-35-302a201e2cc2> in <module>()
4 img1_path = "images/frame_screenshot_07.02.2022_1.png"
5 img2_path = "images/frame_screenshot_07.02.2022.png"
----> 6 face = DeepFace.detectFace(img1_path,(224,224),backends[5], enforce_detection=False)
7 result = DeepFace.verify(img1_path, img2_path, model_name=models[5])
8 plt.imshow(face)
/home/beyondem/miniconda3/envs/compare/lib/python3.6/site-packages/deepface/DeepFace.py in detectFace(img_path, target_size, detector_backend, enforce_detection, align)
816
817 img = functions.preprocess_face(img = img_path, target_size = target_size, detector_backend = detector_backend
--> 818 , enforce_detection = enforce_detection, align = align)[0] #preprocess_face returns (1, 224, 224, 3)
819 return img[:, :, ::-1] #bgr to rgb
820
/home/beyondem/miniconda3/envs/compare/lib/python3.6/site-packages/deepface/commons/functions.py in preprocess_face(img, target_size, grayscale, enforce_detection, detector_backend, return_region, align)
176 base_img = img.copy()
177
--> 178 img, region = detect_face(img = img, detector_backend = detector_backend, grayscale = grayscale, enforce_detection = enforce_detection, align = align)
179
180 #--------------------------
/home/beyondem/miniconda3/envs/compare/lib/python3.6/site-packages/deepface/commons/functions.py in detect_face(img, detector_backend, grayscale, enforce_detection, align)
108 #this call should be completed very fast because it will return found in memory
109 #it will not build face detector model in each call (consider for loops)
--> 110 face_detector = FaceDetector.build_model(detector_backend)
111
112 try:
/home/beyondem/miniconda3/envs/compare/lib/python3.6/site-packages/deepface/detectors/FaceDetector.py in build_model(detector_backend)
28 #print(detector_backend," built")
29 else:
---> 30 raise ValueError("invalid detector_backend passed - " + detector_backend)
31
32 return face_detector_obj[detector_backend]
ValueError: invalid detector_backend passed - mediapipe
%% Cell type:code id: tags:
``` python
```
%% Cell type:code id: tags:
``` python
filename = 'video.mp4'
frames_per_second = 24.0
res = '720p'
# Set resolution for the video capture
# Function adapted from https://kirr.co/0l6qmh
def change_res(cap, width, height):
cap.set(3, width)
cap.set(4, height)
# Standard Video Dimensions Sizes
STD_DIMENSIONS = {
"480p": (640, 480),
"720p": (1280, 720),
"1080p": (1920, 1080),
"4k": (3840, 2160),
}
# grab resolution dimensions and set video capture to it.
def get_dims(cap, res='720'):
width, height = STD_DIMENSIONS["480p"]
if res in STD_DIMENSIONS:
width,height = STD_DIMENSIONS[res]
## change the current caputre device
## to the resulting resolution
change_res(cap, width, height)
return width, height
# Video Encoding, might require additional installs
# Types of Codes: http://www.fourcc.org/codecs.php
VIDEO_TYPE = {
'avi': cv2.VideoWriter_fourcc(*'XVID'),
#'mp4': cv2.VideoWriter_fourcc(*'H264'),
'mp4': cv2.VideoWriter_fourcc(*'XVID'),
}
def get_video_type(filename):
filename, ext = os.path.splitext(filename)
if ext in VIDEO_TYPE:
return VIDEO_TYPE[ext]
return VIDEO_TYPE['avi']
# FPS = 1/30
# FPS_MS = int(FPS * 1000)
cap = cv2.VideoCapture(0)
out = cv2.VideoWriter(filename, get_video_type(filename), 25, get_dims(cap, res))
while True:
ret, frame = cap.read()
out.write(frame)
img = resize_img(frame,(220,220))
print(type(img))
print(img.shape)
print(img.size)
obj = RetinaFace.detect_faces(img)
# obj, region = DeepFace.functions.initialize_input(img)
# img = np.asfarray(obj)
# dsize = (int(img.shape[2] * 224), int(img.shape[3] * 224))
# img = cv2.resize(img, (1,(img.shape[2] * 224), (img.shape[2] * 224), )
# print(type(img))
# print(img.shape)
# print(img.size)
# img = DeepFace.functions.preprocess_face(img,(224,224),False,False)
# print(img)
# DeepFace.functions.detect_face(np.asfarray(img_list))
# for key in obj.keys():
# identity = obj[key]
# # print(identity)
# facial_area = identity["facial_area"]
# cv2.rectangle(frame, (facial_area[2],facial_area[3]),(facial_area[0],facial_area[1]),(255,255,255),1)
# # plt.figure(figsize=(20,20))
# plt.imshow(frame[:,:,::-1])
# plt.show
img_cv = cv2.imshow('frame',frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
out.release()
cv2.destroyAllWindows()
```
%% Output
<class 'numpy.ndarray'>
(1, 220, 220, 3)
145200
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-4-a2440682971e> in <module>()
57 print(img.size)
58
---> 59 obj = RetinaFace.detect_faces(img)
60
61 # obj, region = DeepFace.functions.initialize_input(img)
/home/beyondem/miniconda3/envs/compare/lib/python3.6/site-packages/retinaface/RetinaFace.py in detect_faces(img_path, threshold, model, allow_upscaling)
61 """
62
---> 63 img = get_image(img_path)
64
65 #---------------------------
/home/beyondem/miniconda3/envs/compare/lib/python3.6/site-packages/retinaface/RetinaFace.py in get_image(img_path)
52 # Validate image shape
53 if len(img.shape) != 3 or np.prod(img.shape) == 0:
---> 54 raise ValueError("Input image needs to have 3 channels at must not be empty.")
55
56 return img
ValueError: Input image needs to have 3 channels at must not be empty.
%% Cell type:code id: tags:
``` python
```
%% Cell type:code id: tags:
``` python
```
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