for fxmin, fymin, fxmax, fymax in faces:
cv2.rectangle(img, (fxmin, fymin), (fxmax, fymax), (255, 255, 0), 2)
# Crop face area from frame
fcrop = frame[fymin:fymax, fxmin:fxmax]
# Search and draw facial landmarks
dots = myCV.get_landmarks(LANDMARK_NET, fcrop, (fxmin, fymin))
for x, y in dots:
cv2.circle(img, (x, y), 3, (255, 0, 255), -1)
# Transfrom the face to improve recognition
fcrop = myCV.face_transform(fcrop)
# Getting facial descriptor
face_features = myCV.get_descriptor(FACE_REID_NET, fcrop)
# Searching the face in face_database
ID, distance = myCV.object_in_database(face_features, face_database,
0.5)
# If our database dont contain the face add it
if not ID:
ID = len(face_database) + 1
face_database[ID] = face_features
cv2.putText(img, f"[id{ID}][{(1 - distance):.2f}]",
(fxmin, fymin - 10), cv2.FONT_HERSHEY_SIMPLEX,
((fxmax - fxmin) / 150), (255, 0, 255), 2)
# Runtime and Afps specs
for fxmin, fymin, fxmax, fymax in face:
fxmin += bxmin
fymin += bymin
fxmax += bxmin
fymax += bymin
cv2.rectangle(img, (fxmin, fymin), (fxmax, fymax), (0, 255, 0), 2)
fchip = frame[fymin:fymax, fxmin:fxmax]
cv2.imshow('Face{}'.format(n), fchip)
cv2.waitKey(0)
dots = myCV.get_landmarks(landmark_net, fchip, (fxmin, fymin))
for x, y in dots:
cv2.circle(img, (x, y), 4, (255, 0, 255), -1)
face_info = myCV.get_descriptor(face_reid_net, fchip)
ID = myCV.object_in_data(face_info, faces_data, 0.5)
if not ID:
ID = len(faces_data) + 1
# Rewriting data!!!
faces_data[ID] = face_info
cv2.putText(img, 'id{}'.format(ID), (bxmin, bymin - 10),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 255), 2)
cv2.imshow('Output', img)
cv2.waitKey(0)
raise Exception('Frame not found')
# Copy the input image for continuing processing
img = frame.copy()
# Detect bodies from the input image
# 15 = person id in mobilessd list
bodies = myCV.detect(BODY_NET, frame, 0.7, 15)
for bxmin, bymin, bxmax, bymax in bodies:
cv2.rectangle(img, (bxmin, bymin), (bxmax, bymax), (255, 255, 0), 2)
# Crop body area from frame
bcrop = frame[bymin:bymax, bxmin:bxmax]
# Getting body descriptor
body_features = myCV.get_descriptor(BODY_REID_NET, bcrop)
# Searching the body in body_database
ID, distance = myCV.object_in_database(body_features, bodies_database,
0.5)
# If our database dont contain the body add it
if not ID:
ID = len(bodies_database) + 1
bodies_database[ID] = body_features
# Display ID confidence of person
cv2.putText(img, f"[id{ID}][{(1 - distance):.2f}]",
(bxmin, bymin - 10), cv2.FONT_HERSHEY_SIMPLEX, 1,
(255, 0, 255), 2)