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3.Testing-on-Images.py
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import cv2
import numpy as np
import pickle
import imutils
import os
training = 'training-images'
feature_op = 'features-saved'
detector = 'feature-extract-model'
feature_model = 'feature-extract-model/openface.t7'
least_confidence = 0.65
# importing Necessary Pickle Modules
# -----------------------------------------------------------------------
# Loading the Face Detection Caffe model
print("*** face detector started ... ***")
protoPath = os.path.sep.join([detector, "deploy.prototxt"])
modelPath = os.path.sep.join([detector, "face-area-detect.caffemodel"])
detector = cv2.dnn.readNetFromCaffe(protoPath, modelPath)
print("*** face detector done ... ***")
# Feature Extraction using torch model
print("*** Feature Extraction loading started ... ***")
embedder = cv2.dnn.readNetFromTorch(feature_model)
print("*** Feature Extraction loading done ... ***")
# Recognizer
recognizer = pickle.load(
open('face-recognition-op/recognition-svm-model.pkl', "rb"))
le = pickle.load(open('face-recognition-op/label-encoder.pkl', "rb"))
# -----------------------------------------------------------------------
for i in range(1,28):
img = cv2.imread(f'testing-images/group-pic ({str(i)}).jpg')
# cv2.imshow("Full Group Here ...",img)
image = imutils.resize(img, width=600)
(h, w) = image.shape[:2]
# construct a blob from the image
imageBlob = cv2.dnn.blobFromImage(
cv2.resize(image, (300, 300)), 1.0, (300, 300),
(104.0, 177.0, 123.0), swapRB=False, crop=False)
# Detcting Faces
detector.setInput(imageBlob)
detections = detector.forward()
for i in range(0, detections.shape[2]):
# extract the confidence (i.e., probability) associated with the
# prediction
confidence = detections[0, 0, i, 2]
# filter out weak detections
if confidence > least_confidence:
# compute the (x, y)-coordinates of the bounding box for the
# face
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
# extract the face ROI
face = image[startY:endY, startX:endX]
(fH, fW) = face.shape[:2]
# ensure the face width and height are sufficiently large
if fW < 20 or fH < 20:
continue
# construct a blob for the face ROI, then pass the blob
# through our face embedding model to obtain the 128-d
# quantification of the face
faceBlob = cv2.dnn.blobFromImage(face, 1.0 / 255, (96, 96),
(0, 0, 0), swapRB=True, crop=False)
embedder.setInput(faceBlob)
vec = embedder.forward()
# perform classification to recognize the face
preds = recognizer.predict_proba(vec)[0]
j = np.argmax(preds)
proba = preds[j]
name = le.classes_[j]
# draw the bounding box of the face along with the associated
# probability
text = "{}: {:.2f}%".format(name, proba * 100)
y = startY - 10 if startY - 10 > 10 else startY + 10
cv2.circle(image, ((startX + endX)//2, (startY + endY)//2), (max((- startX + endX)//2, (- startY + endY)//2) + 10),
(0, 255, 0), 2)
cv2.rectangle(image, (startX - 2, y + 5), (startX + 100, y - 15),
(0, 0, 0), -1)
cv2.putText(image, text, (startX, y),
cv2.FONT_HERSHEY_SIMPLEX, 0.45, (255, 255, 255), 2)
# show the output image
cv2.imshow("Drawn - Face Recognised ...", image)
cv2.waitKey(0)