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Face Recognition | Actual Time Face Recognition OpenCV Python


On this article, we’re going to learn the way to detect faces in real-time utilizing OpenCV. After detecting the face from the webcam stream, we’re going to save the frames containing the face. Later we are going to go these frames (pictures) to our masks detector classifier to seek out out if the individual is carrying a masks or not.

We’re additionally going to see make a customized masks detector utilizing Tensorflow and Keras however you may skip that as I can be attaching the skilled mannequin file beneath which you’ll obtain and use. Right here is the checklist of subtopics we’re going to cowl:

  1. What’s Face Detection?
  2. Face Detection Strategies
  3. Face detection algorithm
  4. Face recognition
  5. Face Detection utilizing Python
  6. Face Detection utilizing OpenCV
  7. Create a mannequin to recognise faces carrying a masks (Elective)
  8. The way to do Actual-time Masks detection 

What is Face Detection?

The objective of face detection is to find out if there are any faces within the picture or video. If a number of faces are current, every face is enclosed by a bounding field and thus we all know the placement of the faces

Human faces are tough to mannequin as there are a lot of variables that may change for instance facial features, orientation, lighting circumstances and partial occlusions reminiscent of sun shades, scarf, masks and so forth. The results of the detection provides the face location parameters and it could possibly be required in numerous types, as an example, a rectangle overlaying the central a part of the face, eye facilities or landmarks together with eyes, nostril and mouth corners, eyebrows, nostrils, and so forth.

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Face Detection Strategies

There are two important approaches for Face Detection:

  1. Function Base Method
  2. Picture Base Method

Function Base Method

Objects are normally acknowledged by their distinctive options. There are a lot of options in a human face, which may be acknowledged between a face and lots of different objects. It locates faces by extracting structural options like eyes, nostril, mouth and so forth. after which makes use of them to detect a face. Usually, some form of statistical classifier certified then useful to separate between facial and non-facial areas. As well as, human faces have explicit textures which can be utilized to distinguish between a face and different objects. Furthermore, the sting of options might help to detect the objects from the face. Within the coming part, we are going to implement a feature-based method through the use of OpenCV.

Picture Base Method

Typically, Picture-based strategies depend on strategies from statistical evaluation and machine studying to seek out the related traits of face and non-face pictures. The realized traits are within the type of distribution fashions or discriminant capabilities that’s consequently used for face detection. On this methodology, we use completely different algorithms reminiscent of Neural-networks, HMM, SVM, AdaBoost studying. Within the coming part, we are going to see how we will detect faces with MTCNN or Multi-Process Cascaded Convolutional Neural Community, which is an Picture-based method of face detection

Face detection algorithm

One of many fashionable algorithms that use a feature-based method is the Viola-Jones algorithm and right here I’m briefly going to debate it. If you wish to learn about it intimately, I might counsel going by way of this text, Face Detection utilizing Viola Jones Algorithm.

Viola-Jones algorithm is called after two pc imaginative and prescient researchers who proposed the strategy in 2001, Paul Viola and Michael Jones of their paper, “Fast Object Detection utilizing a Boosted Cascade of Easy Options”. Regardless of being an outdated framework, Viola-Jones is sort of highly effective, and its software has confirmed to be exceptionally notable in real-time face detection. This algorithm is painfully gradual to coach however can detect faces in real-time with spectacular velocity.

Given a picture(this algorithm works on grayscale picture), the algorithm appears to be like at many smaller subregions and tries to discover a face by in search of particular options in every subregion. It must test many various positions and scales as a result of a picture can comprise many faces of assorted sizes. Viola and Jones used Haar-like options to detect faces on this algorithm.

Face Recognition

Face detection and Face Recognition are sometimes used interchangeably however these are fairly completely different. In reality, Face detection is simply a part of Face Recognition.

Face recognition is a technique of figuring out or verifying the identification of a person utilizing their face. There are numerous algorithms that may do face recognition however their accuracy may range. Right here I’m going to explain how we do face recognition utilizing deep studying.

In reality right here is an article, Face Recognition Python which reveals implement Face Recognition.

Face Detection utilizing Python

As talked about earlier than, right here we’re going to see how we will detect faces through the use of an Picture-based method. MTCNN or Multi-Process Cascaded Convolutional Neural Community is definitely one of the fashionable and most correct face detection instruments that work this precept. As such, it’s based mostly on a deep studying structure, it particularly consists of three neural networks (P-Web, R-Web, and O-Web) linked in a cascade.

So, let’s see how we will use this algorithm in Python to detect faces in real-time. First, you’ll want to set up MTCNN library which accommodates a skilled mannequin that may detect faces.

pip set up mtcnn

Now allow us to see use MTCNN:

from mtcnn import MTCNN
import cv2
detector = MTCNN()
#Load a videopip TensorFlow
video_capture = cv2.VideoCapture(0)

whereas (True):
    ret, body = video_capture.learn()
    body = cv2.resize(body, (600, 400))
    containers = detector.detect_faces(body)
    if containers:

        field = containers[0]['box']
        conf = containers[0]['confidence']
        x, y, w, h = field[0], field[1], field[2], field[3]

        if conf > 0.5:
            cv2.rectangle(body, (x, y), (x + w, y + h), (255, 255, 255), 1)

    cv2.imshow("Body", body)
    if cv2.waitKey(25) & 0xFF == ord('q'):
        break

video_capture.launch()
cv2.destroyAllWindows()

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Face Detection utilizing OpenCV

On this part, we’re going to use OpenCV to do real-time face detection from a stay stream by way of our webcam.

As you realize movies are mainly made up of frames, that are nonetheless pictures. We carry out the face detection for every body in a video. So in relation to detecting a face in nonetheless picture and detecting a face in a real-time video stream, there may be not a lot distinction between them.

We can be utilizing Haar Cascade algorithm, also referred to as Voila-Jones algorithm to detect faces. It’s mainly a machine studying object detection algorithm which is used to determine objects in a picture or video. In OpenCV, we now have a number of skilled  Haar Cascade fashions that are saved as XML information. As a substitute of making and coaching the mannequin from scratch, we use this file. We’re going to use “haarcascade_frontalface_alt2.xml” file on this venture. Now allow us to begin coding this up

Step one is to seek out the trail to the “haarcascade_frontalface_alt2.xml” file. We do that through the use of the os module of Python language.

import os
cascPath = os.path.dirname(
    cv2.__file__) + "/knowledge/haarcascade_frontalface_alt2.xml"

The following step is to load our classifier. The trail to the above XML file goes as an argument to CascadeClassifier() methodology of OpenCV.

faceCascade = cv2.CascadeClassifier(cascPath)

After loading the classifier, allow us to open the webcam utilizing this easy OpenCV one-liner code

video_capture = cv2.VideoCapture(0)

Subsequent, we have to get the frames from the webcam stream, we do that utilizing the learn() perform. We use it in infinite loop to get all of the frames till the time we need to shut the stream.

whereas True:
    # Seize frame-by-frame
    ret, body = video_capture.learn()

The learn() perform returns:

  1. The precise video body learn (one body on every loop)
  2. A return code

The return code tells us if we now have run out of frames, which can occur if we’re studying from a file. This doesn’t matter when studying from the webcam since we will document endlessly, so we are going to ignore it.

For this particular classifier to work, we have to convert the body into greyscale.

grey = cv2.cvtColor(body, cv2.COLOR_BGR2GRAY)

The faceCascade object has a technique detectMultiScale(), which receives a body(picture) as an argument and runs the classifier cascade over the picture. The time period MultiScale signifies that the algorithm appears to be like at subregions of the picture in a number of scales, to detect faces of various sizes.

  faces = faceCascade.detectMultiScale(grey,
                                         scaleFactor=1.1,
                                         minNeighbors=5,
                                         minSize=(60, 60),
                                         flags=cv2.CASCADE_SCALE_IMAGE)

Allow us to undergo these arguments of this perform:

  • scaleFactor – Parameter specifying how a lot the picture measurement is lowered at every picture scale. By rescaling the enter picture, you may resize a bigger face to a smaller one, making it detectable by the algorithm. 1.05 is an effective attainable worth for this, which implies you employ a small step for resizing, i.e. cut back the scale by 5%, you improve the possibility of an identical measurement with the mannequin for detection is discovered.
  • minNeighbors – Parameter specifying what number of neighbours every candidate rectangle ought to should retain it. This parameter will have an effect on the standard of the detected faces. Greater worth ends in fewer detections however with larger high quality. 3~6 is an effective worth for it.
  • flags –Mode of operation
  • minSize – Minimal attainable object measurement. Objects smaller than which can be ignored.

The variable faces now comprise all of the detections for the goal picture. Detections are saved as pixel coordinates. Every detection is outlined by its top-left nook coordinates and width and top of the rectangle that encompasses the detected face.

To point out the detected face, we are going to draw a rectangle over it.OpenCV’s rectangle() attracts rectangles over pictures, and it must know the pixel coordinates of the top-left and bottom-right nook. The coordinates point out the row and column of pixels within the picture. We are able to simply get these coordinates from the variable face.

for (x,y,w,h) in faces:
        cv2.rectangle(body, (x, y), (x + w, y + h),(0,255,0), 2)

rectangle() accepts the next arguments:

  • The unique picture
  • The coordinates of the top-left level of the detection
  • The coordinates of the bottom-right level of the detection
  • The color of the rectangle (a tuple that defines the quantity of pink, inexperienced, and blue (0-255)).In our case, we set as inexperienced simply holding the inexperienced element as 255 and relaxation as zero.
  • The thickness of the rectangle traces

Subsequent, we simply show the ensuing body and likewise set a option to exit this infinite loop and shut the video feed. By urgent the ‘q’ key, we will exit the script right here

 cv2.imshow('Video', body)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

The following two traces are simply to scrub up and launch the image.

video_capture.launch()
cv2.destroyAllWindows()

Listed below are the total code and output.

import cv2
import os
cascPath = os.path.dirname(
    cv2.__file__) + "/knowledge/haarcascade_frontalface_alt2.xml"
faceCascade = cv2.CascadeClassifier(cascPath)
video_capture = cv2.VideoCapture(0)
whereas True:
    # Seize frame-by-frame
    ret, body = video_capture.learn()
    grey = cv2.cvtColor(body, cv2.COLOR_BGR2GRAY)
    faces = faceCascade.detectMultiScale(grey,
                                         scaleFactor=1.1,
                                         minNeighbors=5,
                                         minSize=(60, 60),
                                         flags=cv2.CASCADE_SCALE_IMAGE)
    for (x,y,w,h) in faces:
        cv2.rectangle(body, (x, y), (x + w, y + h),(0,255,0), 2)
        # Show the ensuing body
    cv2.imshow('Video', body)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
video_capture.launch()
cv2.destroyAllWindows()

Output:

Create a mannequin to recognise faces carrying a masks

On this part, we’re going to make a classifier that may differentiate between faces with masks and with out masks. In case you need to skip this half, here’s a hyperlink to obtain the pre-trained mannequin. Put it aside and transfer on to the following part to know use it to detect masks utilizing OpenCV.

So for creating this classifier, we want knowledge within the type of Pictures. Fortunately we now have a dataset containing pictures faces with masks and with no masks. Since these pictures are very much less in quantity, we can not prepare a neural community from scratch. As a substitute, we finetune a pre-trained community known as MobileNetV2 which is skilled on the Imagenet dataset.

Allow us to first import all the required libraries we’re going to want.

from tensorflow.keras.preprocessing.picture import ImageDataGenerator
from tensorflow.keras.functions import MobileNetV2
from tensorflow.keras.layers import AveragePooling2D
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Enter
from tensorflow.keras.fashions import Mannequin
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.functions.mobilenet_v2 import preprocess_input
from tensorflow.keras.preprocessing.picture import img_to_array
from tensorflow.keras.preprocessing.picture import load_img
from tensorflow.keras.utils import to_categorical
from sklearn.preprocessing import LabelBinarizer
from sklearn.model_selection import train_test_split
from imutils import paths
import matplotlib.pyplot as plt
import numpy as np
import os

The following step is to learn all the photographs and assign them to some checklist. Right here we get all of the paths related to these pictures after which label them accordingly. Bear in mind our dataset is contained in two folders viz- with_masks and without_masks. So we will simply get the labels by extracting the folder title from the trail. Additionally, we preprocess the picture and resize it to 224x 224 dimensions.

imagePaths = checklist(paths.list_images('/content material/drive/My Drive/dataset'))
knowledge = []
labels = []
# loop over the picture paths
for imagePath in imagePaths:
	# extract the category label from the filename
	label = imagePath.break up(os.path.sep)[-2]
	# load the enter picture (224x224) and preprocess it
	picture = load_img(imagePath, target_size=(224, 224))
	picture = img_to_array(picture)
	picture = preprocess_input(picture)
	# replace the information and labels lists, respectively
	knowledge.append(picture)
	labels.append(label)
# convert the information and labels to NumPy arrays
knowledge = np.array(knowledge, dtype="float32")
labels = np.array(labels)

The following step is to load the pre-trained mannequin and customise it in keeping with our downside. So we simply take away the highest layers of this pre-trained mannequin and add few layers of our personal. As you may see the final layer has two nodes as we now have solely two outputs. That is known as switch studying.

baseModel = MobileNetV2(weights="imagenet", include_top=False,
	input_shape=(224, 224, 3))
# assemble the pinnacle of the mannequin that can be positioned on high of the
# the bottom mannequin
headModel = baseModel.output
headModel = AveragePooling2D(pool_size=(7, 7))(headModel)
headModel = Flatten(title="flatten")(headModel)
headModel = Dense(128, activation="relu")(headModel)
headModel = Dropout(0.5)(headModel)
headModel = Dense(2, activation="softmax")(headModel)

# place the pinnacle FC mannequin on high of the bottom mannequin (this can develop into
# the precise mannequin we are going to prepare)
mannequin = Mannequin(inputs=baseModel.enter, outputs=headModel)
# loop over all layers within the base mannequin and freeze them so they'll
# *not* be up to date through the first coaching course of
for layer in baseModel.layers:
	layer.trainable = False

Now we have to convert the labels into one-hot encoding. After that, we break up the information into coaching and testing units to judge them. Additionally, the following step is knowledge augmentation which considerably will increase the variety of knowledge out there for coaching fashions, with out truly gathering new knowledge. Information augmentation strategies reminiscent of cropping, rotation, shearing and horizontal flipping are generally used to coach giant neural networks.

lb = LabelBinarizer()
labels = lb.fit_transform(labels)
labels = to_categorical(labels)
# partition the information into coaching and testing splits utilizing 80% of
# the information for coaching and the remaining 20% for testing
(trainX, testX, trainY, testY) = train_test_split(knowledge, labels,
	test_size=0.20, stratify=labels, random_state=42)
# assemble the coaching picture generator for knowledge augmentation
aug = ImageDataGenerator(
	rotation_range=20,
	zoom_range=0.15,
	width_shift_range=0.2,
	height_shift_range=0.2,
	shear_range=0.15,
	horizontal_flip=True,
	fill_mode="nearest")

The following step is to compile the mannequin and prepare it on the augmented knowledge.

INIT_LR = 1e-4
EPOCHS = 20
BS = 32
print("[INFO] compiling mannequin...")
decide = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)
mannequin.compile(loss="binary_crossentropy", optimizer=decide,
	metrics=["accuracy"])
# prepare the pinnacle of the community
print("[INFO] coaching head...")
H = mannequin.match(
	aug.circulation(trainX, trainY, batch_size=BS),
	steps_per_epoch=len(trainX) // BS,
	validation_data=(testX, testY),
	validation_steps=len(testX) // BS,
	epochs=EPOCHS)

Now that our mannequin is skilled, allow us to plot a graph to see its studying curve. Additionally, we save the mannequin for later use. Here’s a hyperlink to this skilled mannequin.

N = EPOCHS
plt.type.use("ggplot")
plt.determine()
plt.plot(np.arange(0, N), H.historical past["loss"], label="train_loss")
plt.plot(np.arange(0, N), H.historical past["val_loss"], label="val_loss")
plt.plot(np.arange(0, N), H.historical past["accuracy"], label="train_acc")
plt.plot(np.arange(0, N), H.historical past["val_accuracy"], label="val_acc")
plt.title("Coaching Loss and Accuracy")
plt.xlabel("Epoch #")
plt.ylabel("Loss/Accuracy")
plt.legend(loc="decrease left")

Output:

face detection
#To save lots of the skilled mannequin
mannequin.save('mask_recog_ver2.h5')

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The way to do Actual-time Masks detection 

Earlier than shifting to the following half, ensure to obtain the above mannequin from this hyperlink and place it in the identical folder because the python script you’ll write the beneath code in.

Now that our mannequin is skilled, we will modify the code within the first part in order that it will possibly detect faces and likewise inform us if the individual is carrying a masks or not.

To ensure that our masks detector mannequin to work, it wants pictures of faces. For this, we are going to detect the frames with faces utilizing the strategies as proven within the first part after which go them to our mannequin after preprocessing them. So allow us to first import all of the libraries we want.

import cv2
import os
from tensorflow.keras.preprocessing.picture import img_to_array
from tensorflow.keras.fashions import load_model
from tensorflow.keras.functions.mobilenet_v2 import preprocess_input
import numpy as np

The primary few traces are precisely the identical as the primary part. The one factor that’s completely different is that we now have assigned our pre-trained masks detector mannequin to the variable mannequin.

ascPath = os.path.dirname(
    cv2.__file__) + "/knowledge/haarcascade_frontalface_alt2.xml"
faceCascade = cv2.CascadeClassifier(cascPath)
mannequin = load_model("mask_recog1.h5")

video_capture = cv2.VideoCapture(0)
whereas True:
    # Seize frame-by-frame
    ret, body = video_capture.learn()
    grey = cv2.cvtColor(body, cv2.COLOR_BGR2GRAY)
    faces = faceCascade.detectMultiScale(grey,
                                         scaleFactor=1.1,
                                         minNeighbors=5,
                                         minSize=(60, 60),
                                         flags=cv2.CASCADE_SCALE_IMAGE)

Subsequent, we outline some lists. The faces_list accommodates all of the faces which can be detected by the faceCascade mannequin and the preds checklist is used to retailer the predictions made by the masks detector mannequin.

faces_list=[]
preds=[]

Additionally for the reason that faces variable accommodates the top-left nook coordinates, top and width of the rectangle encompassing the faces, we will use that to get a body of the face after which preprocess that body in order that it may be fed into the mannequin for prediction. The preprocessing steps are identical which can be adopted when coaching the mannequin within the second part. For instance, the mannequin is skilled on RGB pictures so we convert the picture into RGB right here

    for (x, y, w, h) in faces:
        face_frame = body[y:y+h,x:x+w]
        face_frame = cv2.cvtColor(face_frame, cv2.COLOR_BGR2RGB)
        face_frame = cv2.resize(face_frame, (224, 224))
        face_frame = img_to_array(face_frame)
        face_frame = np.expand_dims(face_frame, axis=0)
        face_frame =  preprocess_input(face_frame)
        faces_list.append(face_frame)
        if len(faces_list)>0:
            preds = mannequin.predict(faces_list)
        for pred in preds:
        #masks comprise probabily of carrying a masks and vice versa
            (masks, withoutMask) = pred 

After getting the predictions, we draw a rectangle over the face and put a label in keeping with the predictions.

label = "Masks" if masks > withoutMask else "No Masks"
        colour = (0, 255, 0) if label == "Masks" else (0, 0, 255)
        label = "{}: {:.2f}%".format(label, max(masks, withoutMask) * 100)
        cv2.putText(body, label, (x, y- 10),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.45, colour, 2)

        cv2.rectangle(body, (x, y), (x + w, y + h),colour, 2)

The remainder of the steps are the identical as the primary part.

cv2.imshow('Video', body)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
video_capture.launch()
cv2.destroyAllWindows()

Right here is the entire code and output:

import cv2
import os
from tensorflow.keras.preprocessing.picture import img_to_array
from tensorflow.keras.fashions import load_model
from tensorflow.keras.functions.mobilenet_v2 import preprocess_input
import numpy as np

cascPath = os.path.dirname(
    cv2.__file__) + "/knowledge/haarcascade_frontalface_alt2.xml"
faceCascade = cv2.CascadeClassifier(cascPath)
mannequin = load_model("mask_recog1.h5")

video_capture = cv2.VideoCapture(0)
whereas True:
    # Seize frame-by-frame
    ret, body = video_capture.learn()
    grey = cv2.cvtColor(body, cv2.COLOR_BGR2GRAY)
    faces = faceCascade.detectMultiScale(grey,
                                         scaleFactor=1.1,
                                         minNeighbors=5,
                                         minSize=(60, 60),
                                         flags=cv2.CASCADE_SCALE_IMAGE)
    faces_list=[]
    preds=[]
    for (x, y, w, h) in faces:
        face_frame = body[y:y+h,x:x+w]
        face_frame = cv2.cvtColor(face_frame, cv2.COLOR_BGR2RGB)
        face_frame = cv2.resize(face_frame, (224, 224))
        face_frame = img_to_array(face_frame)
        face_frame = np.expand_dims(face_frame, axis=0)
        face_frame =  preprocess_input(face_frame)
        faces_list.append(face_frame)
        if len(faces_list)>0:
            preds = mannequin.predict(faces_list)
        for pred in preds:
            (masks, withoutMask) = pred
        label = "Masks" if masks > withoutMask else "No Masks"
        colour = (0, 255, 0) if label == "Masks" else (0, 0, 255)
        label = "{}: {:.2f}%".format(label, max(masks, withoutMask) * 100)
        cv2.putText(body, label, (x, y- 10),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.45, colour, 2)

        cv2.rectangle(body, (x, y), (x + w, y + h),colour, 2)
        # Show the ensuing body
    cv2.imshow('Video', body)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
video_capture.launch()
cv2.destroyAllWindows()

Output:

This brings us to the tip of this text the place we realized detect faces in real-time and likewise designed a mannequin that may detect faces with masks. Utilizing this mannequin we had been capable of modify the face detector to masks detector.

Replace: I skilled one other mannequin which might classify pictures into carrying a masks, not carrying a masks and never correctly carrying a masks. Here’s a hyperlink of the Kaggle pocket book of this mannequin. You may modify it and likewise obtain the mannequin from there and use it in as a substitute of the mannequin we skilled on this article. Though this mannequin is just not as environment friendly because the mannequin we skilled right here, it has an additional characteristic of detecting not correctly worn masks.

If you’re utilizing this mannequin you’ll want to make some minor modifications to the code. Exchange the earlier traces with these traces.

#Listed below are some minor modifications in opencv code
for (field, pred) in zip(locs, preds):
        # unpack the bounding field and predictions
        (startX, startY, endX, endY) = field
        (masks, withoutMask,notproper) = pred

        # decide the category label and colour we'll use to attract
        # the bounding field and textual content
        if (masks > withoutMask and masks>notproper):
            label = "With out Masks"
        elif ( withoutMask > notproper and withoutMask > masks):
            label = "Masks"
        else:
            label = "Put on Masks Correctly"

        if label == "Masks":
            colour = (0, 255, 0)
        elif label=="With out Masks":
            colour = (0, 0, 255)
        else:
            colour = (255, 140, 0)

        # embrace the likelihood within the label
        label = "{}: {:.2f}%".format(label,
                                     max(masks, withoutMask, notproper) * 100)

        # show the label and bounding field rectangle on the output
        # body
        cv2.putText(body, label, (startX, startY - 10),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.45, colour, 2)
        cv2.rectangle(body, (startX, startY), (endX, endY), colour, 2)

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Additional Studying

  1. Actual-Time Object Detection Utilizing TensorFlow
  2. YOLO object detection utilizing OpenCV
  3. Object Detection in Pytorch | What’s Object Detection?

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