def createTruthMasks(
outputFolderName="Masks_From_VolumeTracing",
moreLinesFolderName="Tracings_Masks_with_more_than_21_lines",
plainLinesFolderName="Blank_Image_Ground_lines_from_Tracings"):
trace, fnames, frames, root = initVars()
for index in fnames:
for frame_index in [0, -1]:
video = os.path.join(root, "Videos", index)
video = loader.loadvideo(video).astype(np.float32)
t = trace[index][frames[index][frame_index]]
x1, y1, x2, y2 = t[:, 0], t[:, 1], t[:, 2], t[:, 3]
x = np.concatenate((x1[1:], np.flip(x2[1:])))
y = np.concatenate((y1[1:], np.flip(y2[1:])))
r, c = skimage.draw.polygon(
np.rint(y).astype(np.int),
np.rint(x).astype(np.int), (video.shape[2], video.shape[3]))
mask = np.zeros((video.shape[2], video.shape[3]), np.float32)
mask[r, c] = 1
if len(t) < 22:
os.makedirs(os.path.join(root, outputFolderName),
exist_ok=True) # creates frames parent directory
rescaled = (255.0 / mask.max() * (mask - mask.min())).astype(
np.uint8)
im = Image.fromarray(rescaled)
im.save(
os.path.join(
root, outputFolderName, index + "_" +
str(frames[index][frame_index]) + '.png'))
else: # more than 21 lines
os.makedirs(os.path.join(root, moreLinesFolderName),
exist_ok=True) # creates frames parent directory
os.makedirs(os.path.join(root, plainLinesFolderName),
exist_ok=True) # creates frames parent directory
rescaled = (255.0 / mask.max() * (mask - mask.min())).astype(
np.uint8)
im = Image.fromarray(rescaled)
im.save(
os.path.join(
root, moreLinesFolderName, index + "_" +
str(frames[index][frame_index]) + '.png'))
# Draw lines on black image
blank_image = np.zeros((126, 126, 3), dtype="uint8")
for coord in range(len(x1)):
cv2.line(blank_image, (int(x1[coord]), int(y1[coord])),
(int(x2[coord]), int(y2[coord])), (255, 255, 255),
1)
cv2.imwrite(
os.path.join(
root, plainLinesFolderName, index + "_" +
str(frames[index][frame_index]) + '.png'), blank_image)
import cv2
from tqdm import tqdm
from algorithms import funcs as funcs
import loader
from PIL import Image
import skimage.draw
import numpy as np
from scipy.interpolate import interp1d
import matplotlib.pyplot as plt
x = "/Users/ishan/Documents/Stanford/EchoData/control_video_frames/122.jpg"
vidPath = "/Users/ishan/Documents/Stanford/EchoData/Control-Videos-Segmented/Control_0027_0001.avi"
volumes, x1, y1, x2, y2, d = funcs.calculateVolumeAngleShift(
x, 20, 10, method="Method of Disks")
video = loader.loadvideo(vidPath).astype(np.float32)
for iteration in x1:
mask = loader.READ_AND_CROP_FRAME(vidPath, 122, False)
overlay = mask.copy() # overlay layer
x = np.concatenate((x1[iteration][1:], np.flip(x2[iteration][1:])))
y = np.concatenate((y1[iteration][1:], np.flip(y2[iteration][1:])))
x0 = np.concatenate((x1[0][1:], np.flip(x2[0][1:])))
y0 = np.concatenate((y1[0][1:], np.flip(y2[0][1:])))
r, c = skimage.draw.polygon(
np.rint(y).astype(np.int),
np.rint(x).astype(np.int), (video.shape[2], video.shape[3]))
r0, c0 = skimage.draw.polygon(
np.rint(y0).astype(np.int),
def get_landmarks(im):
rects = detector(im, 1)
if len(rects) is not 1: # if no face or multiple faces, return None
return None
return np.array([[p.x, p.y] for p in predictor(im, rects[0]).parts()])
def get_landmarks_facepp(img, api):
res = api.detect(image_file=img, return_landmark=1)
lm = np.zeros((83, 2))
if len(res['faces']) == 0:
return None
for i in range(83):
point = res['faces'][0]['landmark'][landmark_name[i]]
lm[i, 0] = point['x']
lm[i, 1] = point['y']
return lm
if __name__ == "__main__":
from loader import loadvideo
filename1 = 'Datasets/Easy/FrankUnderwood.mp4'
print('loading...')
video = loadvideo(filename1)
print('done')
img = video[0]
lm = get_landmarks_facepp(img)
for groups in lm:
cv2.circle(img, (int(groups[0]), int(groups[1])), 3, (255, 0, 0), 2)
cv2.imshow('img', img)
cv2.waitKey(0)
startYs_flat = startYs.flatten()
newXs = np.full(startXs_flat.shape, -1, dtype=float)
newYs = np.full(startYs_flat.shape, -1, dtype=float)
for i in range(np.size(startXs)):
if startXs_flat[i] != -1:
newXs[i], newYs[i] = estimateFeatureTranslation(
startXs_flat[i], startYs_flat[i], Ix, Iy, img1, img2)
newXs = np.reshape(newXs, startXs.shape)
newYs = np.reshape(newYs, startYs.shape)
return newXs, newYs
if __name__ == "__main__":
from loader import loadvideo, loadlandmarks
filename = 'Datasets/Easy/FrankUnderwood.mp4'
video = loadvideo(filename)
result = loadvideo(filename)
landmarks = loadlandmarks(filename)
startXs = landmarks[0][:, [0]]
startYs = landmarks[0][:, [1]]
for i in range(1, len(video)):
newXs, newYs = estimateAllTranslation(startXs, startYs, video[i - 1],
video[i])
newlandmarks = np.hstack((newXs, newYs)).astype(int)
startXs, startYs = newXs, newYs
# draw true landmarks
for groups in landmarks[i]:
cv2.circle(result[i], (groups[0], groups[1]), 3, (255, 0, 0), 2)
# draw tracker landmarks
landmarks1_trans = landmarks1_trans[0:2, :].T
dist = calcSimilarity(landmarks1_trans, landmarks2[i])
if dist < mindist:
faceind[i] = j
mindist = dist
return faceind.astype(int)
if __name__ == "__main__":
from loader import loadlandmarks_facepp, loadvideo
import time
easy1 = 'Datasets/Easy/FrankUnderwood.mp4'
easy2 = 'Datasets/Easy/MrRobot.mp4'
lm1 = loadlandmarks_facepp(easy2)
lm2 = loadlandmarks_facepp(easy1)
video2 = loadvideo(easy1)
print(len(lm1))
t0 = time.time()
ind = findMinDistFace(lm1, lm2)
ind_s = findMinDistFace_static(lm1, lm2)
t1 = time.time()
print(t1 - t0)
T = cv2.estimateRigidTransform(lm1[ind[0]], lm2[0], False)
T_full = np.vstack((T, np.array([0, 0, 1])))
landmarks1_full = np.vstack(
(lm1[ind[0]].T, np.ones((1, lm1[ind[0]].shape[0]))))
landmarks1_trans = np.dot(T_full, landmarks1_full)
landmarks1_trans = landmarks1_trans[0:2, :].T
for groups in landmarks1_trans.astype(int):
import matplotlib.pyplot as plt
import time
if __name__ == "__main__":
easy1 = 'Datasets/Easy/FrankUnderwood.mp4'
easy2 = 'Datasets/Easy/MrRobot.mp4'
easy3 = 'Datasets/Easy/JonSnow.mp4'
medium1 = 'Datasets/Medium/LucianoRosso1.mp4'
medium2 = 'Datasets/Medium/LucianoRosso2.mp4'
medium3 = 'Datasets/Medium/LucianoRosso3.mp4'
hard1 = 'Datasets/Hard/Joker.mp4'
hard2 = 'Datasets/Hard/LeonardoDiCaprio.mp4'
xi = 'Datasets/xidada.mp4'
source_video_path = easy2
target_video_path = easy1
source_video = loadvideo(source_video_path)
target_video = loadvideo(target_video_path)
target_video_with_landmark = vislandmarks(target_video_path,
use_facepp=True)
source_landmarks = smooth_landmark_traj(
loadlandmarks_facepp(source_video_path))
target_landmarks = smooth_landmark_traj(
loadlandmarks_facepp(target_video_path))
# Face Matching
# source_ind = findMinDistFace(source_landmarks, target_landmarks)
source_ind = findMinDistFace_static(source_landmarks, target_landmarks)
N_FRAMES = len(target_video)
output = target_video.copy()
easy1 = 'Datasets/Easy/FrankUnderwood.mp4'
easy2 = 'Datasets/Easy/MrRobot.mp4'
easy3 = 'Datasets/Easy/JonSnow.mp4'
medium1 = 'Datasets/Medium/LucianoRosso1.mp4'
medium2 = 'Datasets/Medium/LucianoRosso2.mp4'
medium3 = 'Datasets/Medium/LucianoRosso3.mp4'
hard1 = 'Datasets/Hard/Joker.mp4'
hard2 = 'Datasets/Hard/LeonardoDiCaprio.mp4'
xidada = 'Datasets/xidada.mp4'
SOURCE_VIDEO_PATH = easy1
TARGET_VIDEO_PATH = easy2
if __name__ == "__main__":
print("Loading videos...")
source_video = loadvideo(SOURCE_VIDEO_PATH)
target_video = loadvideo(TARGET_VIDEO_PATH)
print("Loading landmarks...")
source_landmarks = smooth_landmark_traj(
loadlandmarks_facepp(SOURCE_VIDEO_PATH))
target_landmarks = smooth_landmark_traj(
loadlandmarks_facepp(TARGET_VIDEO_PATH))
# Face Matching
print("Matching faces...")
source_ind = findMinDistFace_static(source_landmarks, target_landmarks)
# For each frame in target video
N_FRAMES = len(target_video)
output = target_video.copy()
for i in range(N_FRAMES):