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Optimized Code structure and readibility #1845

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Optimized Code structure and readibility #1845

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160 changes: 93 additions & 67 deletions Real-Time-Age-Calculation/main.py
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Original file line number Diff line number Diff line change
@@ -1,22 +1,22 @@
import cv2
import math
import argparse
import math


def highlightFace(net, frame, conf_threshold=0.7):
'''
This function detects faces on the image using the 'net' passed (if any) and returns the detection output
as well as the cordinates of the faces detected
'''
"""
Detect faces on the image using the provided 'net' and return the detection output
as well as the coordinates of the faces detected.
"""

frameOpencvDnn=frame.copy()
#--------saving the image dimensions as height and width-------#
frameOpencvDnn = frame.copy()
# --------saving the image dimensions as height and width-------#
frameHeight = frameOpencvDnn.shape[0]
frameWidth = frameOpencvDnn.shape[1]

#-----------blob-> Preprocessing the image to required input of the model---------#
blob=cv2.dnn.blobFromImage(frameOpencvDnn, 1.0, (300, 300), [104, 117, 123], True, False)
net.setInput(blob) #setting the image blob as input
# -----------blob-> Preprocessing the image to required input of the model---------#
blob = cv2.dnn.blobFromImage(frameOpencvDnn, 1.0, (300, 300), [104, 117, 123], True, False)
net.setInput(blob) # setting the image blob as input
detections = net.forward()
'''3rd dimension helps you iterate over predictions and
in the 4th dimension, there are actual results
Expand All @@ -28,83 +28,109 @@ def highlightFace(net, frame, conf_threshold=0.7):
YOLO the predictions are done at 3 different layers. you can iterate over these predictions using
2nd dimension like [:,i,:,:]
'''
faceBoxes=[]
faceBoxes = []

for i in range(detections.shape[2]):
confidence=detections[0,0,i,2]
if confidence>conf_threshold:
confidence = detections[0, 0, i, 2]
if confidence > conf_threshold:
# TopLeftX,TopLeftY, BottomRightX, BottomRightY = inference_results[0, 0, i, 3:7] --> gives co-ordinates bounding boxes for resized small image
x1=int(detections[0,0,i,3]*frameWidth)
y1=int(detections[0,0,i,4]*frameHeight)
x2=int(detections[0,0,i,5]*frameWidth)
y2=int(detections[0,0,i,6]*frameHeight)
x1 = int(detections[0, 0, i, 3] * frameWidth)
y1 = int(detections[0, 0, i, 4] * frameHeight)
x2 = int(detections[0, 0, i, 5] * frameWidth)
y2 = int(detections[0, 0, i, 6] * frameHeight)
# box = detections[0, 0, i, 3:7] * np.array([frameWidth, frameHeight, frameWidth, frameHeight])
# faceBoxes.append(box.astype("int"))
faceBoxes.append([x1,y1,x2,y2])

cv2.rectangle(frameOpencvDnn, (x1,y1), (x2,y2), (0,255,0), int(round(frameHeight/150)), 8)
return frameOpencvDnn,faceBoxes
faceBoxes.append([x1, y1, x2, y2])
cv2.rectangle(frameOpencvDnn, (x1, y1), (x2, y2), (0, 255, 0), int(round(frameHeight / 150)), 8)

return frameOpencvDnn, faceBoxes


#-------Creating and Parsing through the argument passed on the terminal-------------#
parser=argparse.ArgumentParser()
# -------Creating and Parsing through the argument passed on the terminal-------------#
parser = argparse.ArgumentParser()
parser.add_argument('--image')

args=parser.parse_args()

#-----------Model File Paths----------------#
faceProto="Models/opencv_face_detector.pbtxt"
faceModel="Models/opencv_face_detector_uint8.pb"
ageProto="Models/age_deploy.prototxt"
ageModel="Models/age_net.caffemodel"
genderProto="Models/gender_deploy.prototxt"
genderModel="Models/gender_net.caffemodel"


#-----------Model Variables---------------#
MODEL_MEAN_VALUES=(78.4263377603, 87.7689143744, 114.895847746)
ageList=['(0-2)', '(4-6)', '(8-12)', '(15-20)', '(25-32)', '(38-43)', '(48-53)', '(60-100)']
genderList=['Male','Female']

#-------------Creating the DNN------------#
faceNet= cv2.dnn.readNet(faceModel,faceProto)
ageNet= cv2.dnn.readNet(ageModel,ageProto)
genderNet= cv2.dnn.readNet(genderModel,genderProto)

#---------Instantiate the Video Capture Object-----------#
video=cv2.VideoCapture(args.image if args.image else 0) #check whether image was passed or not otherwise use the webcam
padding=20

while cv2.waitKey(1)<0:
hasFrame,frame=video.read()
args = parser.parse_args()

# -----------Model File Paths----------------#
"""
The models required for face detection, age prediction, and gender prediction.
"""

faceProto = "Models/opencv_face_detector.pbtxt"
faceModel = "Models/opencv_face_detector_uint8.pb"
ageProto = "Models/age_deploy.prototxt"
ageModel = "Models/age_net.caffemodel"
genderProto = "Models/gender_deploy.prototxt"
genderModel = "Models/gender_net.caffemodel"

# -------------Creating the DNN------------#
faceNet = cv2.dnn.readNet(faceModel, faceProto)
ageNet = cv2.dnn.readNet(ageModel, ageProto)
genderNet = cv2.dnn.readNet(genderModel, genderProto)

# ---------Instantiate the Video Capture Object-----------#
video = cv2.VideoCapture(args.image if args.image else 0) # check whether image was passed or not otherwise use the webcam
if not video.isOpened():
print("Error: Could not open video or image.")

# -----------Model Variables---------------#
MODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746)
ageList = ['(0-2)', '(4-6)', '(8-12)', '(15-20)', '(25-32)', '(38-43)', '(48-53)', '(60-100)']
genderList = ['Male', 'Female']
padding = 20

while cv2.waitKey(1) < 0:
hasFrame, frame = video.read()
if not hasFrame:
cv2.waitKey()
break

#----------------Face Detection-----------------#
resultImg,faceBoxes=highlightFace(faceNet,frame)
# ----------------Face Detection-----------------#
resultImg, faceBoxes = highlightFace(faceNet, frame)
if not faceBoxes:
print('No face detected')
break

# for (x1, y1, x2, y2) in faceBoxes:
# # Ensure face box coordinates are within the frame
# if x1 < 0:
# x1 = 0
# if y1 < 0:
# y1 = 0
# if x2 > frame.shape[1]:
# x2 = frame.shape[1]
# if y2 > frame.shape[0]:
# y2 = frame.shape[0]
#
# # Crop out the face from the frame
# face = frame[y1:y2, x1:x2]
#
# # Check if the face region is valid
# if face.size == 0:
# print(f"Error: Invalid face region at ({x1},{y1}) to ({x2},{y2})")
# continue

for faceBox in faceBoxes:
#-------Crop out the face from the image---------#
face=frame[faceBox[1]:faceBox[3],faceBox[0]:faceBox[2]] #img[y1:y2 , x1:x2]
# -------Crop out the face from the image---------#
face = frame[faceBox[1]:faceBox[3], faceBox[0]:faceBox[2]] # img[y1:y2 , x1:x2]

# ------Gender and Age prediction---------#
blob = cv2.dnn.blobFromImage(face, 1.0, (227, 227), MODEL_MEAN_VALUES, swapRB=False)

#------Gender prediction---------#
blob=cv2.dnn.blobFromImage(face, 1.0, (227,227), MODEL_MEAN_VALUES, swapRB=False)
genderNet.setInput(blob)
genderPreds=genderNet.forward()
gender=genderList[genderPreds[0].argmax()]
print(f'Gender: {gender}')
#-------Age Prediction---------#
ageNet.setInput(blob)
agePreds=ageNet.forward()
age=ageList[agePreds[0].argmax()]
print(f'Age: {age[1:-1]} years')

cv2.putText(resultImg, f'{gender}, {age}', (faceBox[0], faceBox[1]-10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0,255,255), 2, cv2.LINE_AA)
genderPreds = genderNet.forward()
agePreds = ageNet.forward()

gender = genderList[genderPreds[0].argmax()]
age = ageList[agePreds[0].argmax()]

print(f'Gender: {gender}, Age: {age[1:-1]} years')

cv2.putText(resultImg, f'{gender}, {age}', (faceBox[0], faceBox[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.8,
(0, 255, 255), 2, cv2.LINE_AA)
cv2.imshow("Detecting age and gender", resultImg)

cv2.imshow("Detecting age and gender", resultImg)
video.release()
cv2.destroyAllWindows()
cv2.destroyAllWindows()