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align_crop_session_two.py
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align_crop_session_two.py
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# import the necessary packages
from speakingfacespy.imtools import face_region_extractor
from speakingfacespy.imtools import make_dir
from imutils import paths
import face_recognition
import numpy as np
import cv2
import argparse
import os
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--dataset", required=True,
help="path to the dataset")
ap.add_argument("-i", "--sub_info", nargs='+', type=int,
help="subID(1,...,142) trialID (1,2) posID(1,...,9)")
ap.add_argument("-y", "--dy", nargs='+', type=int,
help="a list of shifts in y axis for each position")
ap.add_argument("-x", "--dx", nargs='+', type=int,
help="a list of shifts in x axis for each position")
ap.add_argument("-m", "--model", type=str, default="dnn",
help="a model for face detection: hog/dnn/cnn/none")
ap.add_argument("-c", "--confidence", type=float, default=0.8,
help="a minimum probability for dnn to filter out weak face detections")
ap.add_argument("-u", "--upper_bound", nargs='+', type=int,
help="a list of upper bounds for landmarks to crop the lip for each position")
ap.add_argument("-r", "--resize", nargs='+', type=int, default = (128,64),
help="resize the ROI (width, height)")
ap.add_argument("-s", "--show", type=int, default=0,
help="visualize (1) or not (0) a preliminary result of alignment")
args = vars(ap.parse_args())
# initialize the subject, trial and
# position IDs
sub_id = args["sub_info"][0]
trial_id = args["sub_info"][1]
pos_id = args["sub_info"][2]
# initialize a list of the upper bounds for
# cropping the lip ROI
upper_bound = args["upper_bound"][pos_id - 1]
# initialize the bbox coordinates of the RoI
# for manual extraction
initBB = None
# load the serialiazed dnn face detector from disk
# in case if it was selected
if args["model"] == "dnn":
print("[INFO] loading dnn face detector...")
face_net = cv2.dnn.readNetFromCaffe("models/deploy.prototxt.txt",
"models/res10_300x300_ssd_iter_140000.caffemodel")
# initialize a path to our dataset
path_to_dataset = args["dataset"]
# construct a path to our visual images
rgb_image_path = os.path.join(path_to_dataset, "sub_{}/trial_{}/rgb_image_cmd".format(sub_id, trial_id))
# construct a path to our thermal images
thr_image_path = os.path.join(path_to_dataset, "sub_{}/trial_{}/thr_image_cmd".format(sub_id, trial_id))
# create a directory to save the aligned visual images
rgb_image_aligned_path = os.path.join(path_to_dataset, "sub_{}/trial_{}/rgb_image_cmd_aligned".format(sub_id, trial_id))
make_dir(rgb_image_aligned_path)
# create directories for extracted lip region
lip_rgb_path = os.path.join(path_to_dataset, "sub_{}/trial_{}/rgb_roi_cmd".format(sub_id, trial_id))
lip_thr_path = os.path.join(path_to_dataset, "sub_{}/trial_{}/thr_roi_cmd".format(sub_id, trial_id))
make_dir(lip_rgb_path)
make_dir(lip_thr_path)
# initialize lists of shifts
dy = args["dy"][pos_id - 1]
dx = args["dx"][pos_id - 1]
# construct arrays of matched features
# for the given position
ptsA = np.array([[399 + dx, 345 + dy], [423 + dx, 293 + dy], [293 + dx, 316 + dy], [269 + dx, 368 + dy]])
ptsB = np.array([[249, 237], [267, 196], [169, 214], [151, 254]])
# estimate a homography matrix
# for the given position
(H, status) = cv2.findHomography(ptsA, ptsB, cv2.RANSAC, 2.0)
# grab the path to the visual images
rgb_image_filepaths = list(paths.list_images(rgb_image_path))
# loop over the visible images
for rgb_image_filepath in rgb_image_filepaths:
# extract the current image info
sub, trial, session, pos, cmd, frame = rgb_image_filepath.split(os.path.sep)[-1].split("_")[-7:-1]
# process only the files for the given position if "show" mode is enabled.
if int(pos) != pos_id and args["show"]:
continue
print("[INFO] processing image {}".format(rgb_image_filepath.split(os.path.sep)[-1]))
# construct a filenames of the corresponding thermal images
thr_file = "{}_{}_2_{}_{}_{}_1.png".format(sub, trial, pos, cmd, frame)
thr_image_filepath = os.path.join(thr_image_path, thr_file)
# load rgb and corresponding thermal image
rgb_image = cv2.imread(rgb_image_filepath)
thr_image = cv2.imread(thr_image_filepath)
# grab height and width of the thermal image
(h_thr, w_thr) = thr_image.shape[:2]
# align rgb image with the thermal one
rgb_image_aligned = cv2.warpPerspective(rgb_image, H, (w_thr, h_thr),
flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT)
# make a copy of the aligned rgb image
rgb_image_aligned_copy = rgb_image_aligned.copy()
# if we want automatically extract RoI
if args["model"] != "none":
# detect faces in the rgb image and return
# corresponding bounding boxes
if args["model"] != "dnn":
# apply dlib's face detector
rgb_boxes = face_recognition.face_locations(rgb_image_aligned, model=args["model"])
else:
# apply OpenCV's face detector
rgb_boxes = face_region_extractor(face_net, rgb_image_aligned, 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 bbox
rgb_box = rgb_boxes[0]
(face_startY, face_endX, face_endY, face_startX) = rgb_box
# check if the was detected properly
if face_startX < 0 or face_startY < 0:
print("[INFO] INAPPROPRIATE COORDINATES FOR THE BOUNDING BOX!!!")
break
# extract facial landmarks
rgb_landmark = face_recognition.face_landmarks(rgb_image_aligned, rgb_boxes)[0]
# extract coordinates for the chin ROI
chin_roi = rgb_landmark['chin']
(lip_startX, lip_startY, lip_endX, lip_endY) = (chin_roi[2][0], chin_roi[upper_bound][1], chin_roi[14][0],chin_roi[6][1])
# slightly change coordinates of the lip ROI
# for some positions
if pos > 6:
lip_startX = lip_startX - 20
lip_endX = lip_endX - 20
elif pos > 3:
lip_startX = lip_startX + 20
lip_endX = lip_endX + 20
# draw landmarks if visualization is enabled
if args["show"]:
# draw landmarks
for (x_l, y_l) in chin_roi:
cv2.circle(rgb_image_aligned_copy, (x_l, y_l), 2, (0, 255, 0), -1)
cv2.circle(thr_image, (x_l, y_l), 2, (0, 255, 0), -1)
else:
# select the RoI manually
if initBB is None:
initBB = cv2.selectROI("Frames", rgb_image_aligned, fromCenter=False,
showCrosshair=True)
(x, y, w, h) = initBB
(lip_startX, lip_startY, lip_endX, lip_endY) = (x, y, x + w, y + h)
# crop the detected faces
rgb_lip = rgb_image_aligned[lip_startY:lip_endY, lip_startX:lip_endX]
thr_lip = thr_image[lip_startY:lip_endY, lip_startX:lip_endX]
# resize the lips
rgb_lip = cv2.resize(rgb_lip, args["resize"], interpolation = cv2.INTER_AREA)
thr_lip = cv2.resize(thr_lip, args["resize"], interpolation = cv2.INTER_AREA)
# if visualization is enabled
if args["show"]:
# draw the ROI
cv2.rectangle(rgb_image_aligned_copy, (lip_startX, lip_startY), (lip_endX, lip_endY), (0, 0, 255), 2)
cv2.rectangle(thr_image, (lip_startX, lip_startY), (lip_endX, lip_endY), (0, 0, 255), 2)
# show the frames
cv2.imshow("Frames", np.hstack([rgb_image_aligned_copy, thr_image]))
cv2.imshow("Lips", np.hstack([rgb_lip, thr_lip]))
key = cv2.waitKey(0) & 0xFF
# if the 'q' key is pressed, stop the loop
if key == ord("q"):
break
else:
# construct filenames to save the image and the ROIs
rgb_aligned_filename = "{}_{}_{}_{}_{}_{}_3.png".format(sub, trial, session, pos, cmd, frame)
rgb_lip_filename = "{}_{}_{}_{}_{}_{}_4.png".format(sub, trial, session, pos, cmd, frame)
thr_lip_filename = "{}_{}_{}_{}_{}_{}_5.png".format(sub, trial, session, pos, cmd, frame)
# construct paths to save images
rgb_aligned_path = os.path.join(rgb_image_aligned_path, rgb_aligned_filename)
rgb_lip_path = os.path.join(lip_rgb_path, rgb_lip_filename)
thr_lip_path = os.path.join(lip_thr_path, thr_lip_filename)
# save the images
cv2.imwrite(rgb_aligned_path, rgb_image_aligned)
cv2.imwrite(rgb_lip_path, rgb_lip)
cv2.imwrite(thr_lip_path, thr_lip)