rgb_file = os.path.join(rgb_path, rgb_file)
thr_file = os.path.join(thr_path, thr_file)
#print(rgb_file)
#print(thr_file)
# load rgb and thermal images
rgb_image = cv2.imread(rgb_file)
thr_image = cv2.imread(thr_file)
# detect faces in the rgb image and return
# corresponding bounding boxes
if args["model"] != "dnn":
rgb_boxes = face_recognition.face_locations(
rgb_image, model=args["model"])
else:
rgb_boxes = face_region_extractor(face_net, rgb_image,
args["confidence"])
# if at least one face is detected
if len(rgb_boxes):
# assume that only one person was detected and extract (x,y)
# coordinates of the corners of the bounding box
(startY, endX, endY, startX) = rgb_boxes[0]
# crop the detected faces
rgb_face = rgb_image[startY:endY, startX:endX]
thr_face = thr_image[startY:endY, startX:endX]
if args["show"]:
# make a copy of the rgb image then replace its RED channel with
# the RED channel of the thermal image
rgb_copy = rgb_image.copy()
# to align with the thermal image
rgb = cv2.warpPerspective(rgb,
H, (W_thr, H_thr),
flags=cv2.INTER_LINEAR,
borderMode=cv2.BORDER_REPLICATE)
# adjust the alignment if there is still
# some misalignment among x and y axises
rgb = rgb[dy:H_thr, dx:W_thr]
thr = thr[0:H_thr - dy, 0:W_thr - dx]
# give the rgb image to the face detector
# as an input and get a bouding box of a face
# as an output
(startX, startY, endX,
endY) = face_region_extractor(face_net, rgb, thr, args["confidence"])
# if the face is not detected
# then skip the current image
if startX is None:
continue
# crop the detected faces
rgb_face = rgb[startY:endY, startX:endX]
thr_face = thr[startY:endY, startX:endX]
if args["show"]:
# make a copy of the rgb image
# then replace its RED channel with
# the RED channel of the thermal image
rgb_copy = rgb.copy()