def single_video_calculation(file, file_path, pulse_label_data):
start_time = time.time()
w_div = 16
h_div = 8
bpm_values = np.zeros((h_div, w_div), dtype='float64')
video_frames, fps = load_video(file_path)
video_frames = video_frames[22:310]
frame_count, width, height = get_video_dimensions(video_frames)
roi_width = int(width / w_div)
roi_height = int(height / h_div)
width = roi_width * w_div
height = roi_height * h_div
for x in range(0, width, roi_width):
for y in range(0, height, roi_height):
roi_ind_x = int(x / roi_width)
roi_ind_y = int(y / roi_height)
roi_time_series = video_frames[:, y:y + roi_height, x:x + roi_width]
# Spatial Averaging used when ROIs are extracted
time_series = np.mean(roi_time_series, axis=(1, 2))
# Pulse-Signal Extraction
bpm, pruned_fft, fft, heart_rates, raw, H, h, norm_channels, time_series = pos_based_method_improved(time_series, fps)
# bpm, pruned_fft = extract_pos_based_method_improved(time_series, fps)
plot_results(bpm, pruned_fft, fft, heart_rates, raw=raw, overlap_signal=H, pulse_signal=h, norm_channels=norm_channels, time_series=time_series)
bpm_values[roi_ind_y, roi_ind_x] = bpm
print("Fortschritt: %.2f %%" % ((x+1.0) / width*100.0))
def extr_single_video_calculation(in_file, in_file_path, out_dir):
# load video
video_frames, fps = load_video(in_file_path)
video_frames = video_frames[22:358]
frame_count, width, height = get_video_dimensions(video_frames)
# Giant ndarray for pulse-signals for height*width of a Videos
pulse_signal_data = np.zeros([height, width, 44], dtype='float64')
# BPM-map only for visualisation
bpm_map = np.zeros([height, width], dtype='float16')
# For plotting the skin and pulse matrices
last_frame = video_frames[frame_count - 1]
last_frame_clone = last_frame.copy()
# Load all pulse value belonging to a certain video in array
pulse_lower, pulse_upper = get_pulse_vals_from_label_data(
load_reference_data(), in_file)
for x in range(0, width):
for y in range(0, height):
# get pixel sequence
px_time_series = video_frames[:, y, x]
# call POS-Function
bpm, pruned_fft = extract_pos_based_method_improved(
px_time_series, fps)
# write extracted pulse frequencies to ndarray
pulse_signal_data[y, x] = pruned_fft
# fill up bpm-map
bpm_map[y, x] = bpm
print("Completed: %.2f %%" % ((x + 1.0) / width * 100.0))
# compare bpms with reference bpms lower and upper
weak_skin_map = compare_pulse_vals(bpm_map, pulse_lower, pulse_upper)
# check neighbouring BPMs
strong_skin_map = eliminate_weak_skin(weak_skin_map, skin_neighbors=5)
# For plotting the skin and pulse matrices
out_file_path = os.path.join(out_dir, 'no_nan_' + in_file[:-4])
plot_title = in_file + ' BPM: ' + str(pulse_lower) + '-' + str(pulse_upper)
plot_and_save_results(plot_title, last_frame_clone, bpm_map, weak_skin_map,
strong_skin_map, out_file_path)
np.save(out_file_path, pulse_signal_data)
print("--- File Completed after %s seconds ---" %
(time.time() - start_time))
print('Saved to ' + out_file_path)
if __name__ == '__main__':
start_time = time.time()
dir_path = os.path.join('..', 'assets', 'Vid_Original',
'Kuenstliches_Licht')
file = '00130.MTS'
file_path = os.path.join(dir_path, file)
w_div = 16
h_div = 8
bpm_values = np.zeros((h_div, w_div), dtype='float64')
print(file_path)
vid_data, fps = load_video(file_path)
vid_data = vid_data[50:300]
frame_count, width, height = get_video_dimensions(vid_data)
print('Cutted length: ' + str(frame_count))
# w_steps = width/w_div
# h_steps = height/h_div
roi_mean_frames = np.zeros((frame_count, w_div, h_div, 3), dtype='float64')
#
for j, frame in enumerate(vid_data):
#
# Spatial Averaging
roi_means_2DArray, frame_devided = devide_frame_into_roi_means(
frame, w_div, h_div)
#
# Create time series array of the roi means
def extr_roi_single_video_calculation(in_file, in_file_path, out_dir):
# determine how to devide frame into rois
w_div = 16
h_div = 8
# load video
video_frames, fps = load_video(in_file_path)
video_frames = video_frames[22:358]
frame_count, width, height = get_video_dimensions(video_frames)
w_steps = int(width / w_div)
h_steps = int(height / h_div)
# Giant ndarray for pulse-signals for height*width of a Videos
pulse_signal_data = np.zeros([h_div, w_div, 44], dtype='float64')
# BPM-map only for visualisation
bpm_map = np.zeros((h_div, w_div), dtype='float64')
# Load all pulse values belonging to a certain video in array
pulse_lower, pulse_upper = get_pulse_vals_from_label_data(
load_reference_data(), in_file)
# For plotting the skin and pulse matrices
plot_title = file + ' BPM: ' + str(pulse_lower) + '-' + str(pulse_upper)
fig = plt.figure(figsize=(20, 15))
fig.suptitle(plot_title, fontsize=20, fontweight='bold')
tick_fontsize = 11
txt_coord_x = 0.05
txt_coord_y = 0.9
txt_fontsize = 21
sub1 = fig.add_subplot(221)
sub2 = fig.add_subplot(222)
sub3 = fig.add_subplot(223)
sub4 = fig.add_subplot(224)
last_frame = video_frames[frame_count - 1]
last_frame_clone = last_frame.copy()
# The odd rest is cutted here
width = w_steps * w_div
height = h_steps * h_div
for x in range(0, width, w_steps):
for y in range(0, height, h_steps):
roi_ind_x = int(x / w_steps)
roi_ind_y = int(y / h_steps)
# get roi volumes
roi_time_series = video_frames[:, y:y + h_steps, x:x + w_steps]
# Spatial Averaging
roi_time_series_avg = np.mean(roi_time_series, axis=(1, 2))
# call POS-Function
bpm, pruned_fft = extract_pos_based_method_improved(
roi_time_series_avg, fps)
# fill up bpm-map
bpm_map[roi_ind_y, roi_ind_x] = bpm
# write extracted pulse frequencies to ndarray
pulse_signal_data[roi_ind_y, roi_ind_x] = pruned_fft
# For plotting the skin and pulse matrices
sub1.text(x + w_steps / 2,
y + h_steps / 2,
round(bpm, 1),
color=(0.0, 0.0, 0.0),
fontsize=7,
va='center',
ha='center')
cv2.rectangle(last_frame_clone, (x, y), (x + w_steps, y + h_steps),
(0, 0, 0), 2)
print("Fortschritt: %.2f %%" % ((x + 1.0) / width * 100.0))
# compare bpms with reference bpms lower and upper
weak_skin_map = compare_pulse_vals(bpm_map, pulse_lower, pulse_upper)
# check neighbouring BPMs
strong_skin_map = eliminate_weak_skin(weak_skin_map, skin_neighbors=3)
# For plotting the skin and pulse matrices
out_file_path = os.path.join(out_dir, 'me_' + in_file[:-4])
bgr_last_frame = cv2.cvtColor(last_frame_clone, cv2.COLOR_RGB2BGR)
sub1.text(txt_coord_x,
txt_coord_y,
'(a)',
color='white',
fontsize=txt_fontsize,
horizontalalignment='center',
transform=sub1.transAxes)
sub1.tick_params(axis='both', which='major', labelsize=tick_fontsize)
sub1.imshow(bgr_last_frame)
sub2.text(txt_coord_x,
txt_coord_y,
'(b)',
color='white',
fontsize=txt_fontsize,
horizontalalignment='center',
transform=sub2.transAxes)
sub2.tick_params(axis='both', which='major', labelsize=tick_fontsize)
sub2.matshow(bpm_map, cmap=plt.cm.gray)
sub3.text(txt_coord_x,
txt_coord_y,
'(c)',
color='white',
fontsize=txt_fontsize,
horizontalalignment='center',
transform=sub3.transAxes)
sub3.tick_params(axis='both', which='major', labelsize=tick_fontsize)
sub3.matshow(weak_skin_map, cmap=plt.cm.gray)
sub4.text(txt_coord_x,
txt_coord_y,
'(d)',
color='white',
fontsize=txt_fontsize,
horizontalalignment='center',
transform=sub4.transAxes)
sub4.tick_params(axis='both', which='major', labelsize=tick_fontsize)
sub4.matshow(strong_skin_map, cmap=plt.cm.gray)
plt.tight_layout()
# plt.show()
fig.savefig(out_file_path + '.png')
plt.close()
# tp, fp, fn and tn of POS-Algorithm of one video
# is summed up with a global variable for all videos
vid_true_positives, vid_false_positives, vid_false_negatives, vid_true_negatives = compare_with_skin_mask(
file, weak_skin_map, h_div, w_div)
global true_positives
global false_positives
global false_negatives
global true_negatives
true_positives += vid_true_positives
false_positives += vid_false_positives
false_negatives += vid_false_negatives
true_negatives += vid_true_negatives
print("--- File Completed after %s seconds ---" %
(time.time() - start_time))
np.save(out_file_path, pulse_signal_data)
print('Saved to ' + out_file_path)
def skin_detection_algorithm_single_video(_file,
_dir_path,
_dest_folder,
show_figure=False):
file_path = os.path.join(_dir_path, _file)
# define the upper and lower boundaries of the HSV pixel
# intensities to be considered 'skin'
# white skin-tone
lower = np.array([0, 50, 50], dtype="uint8")
upper = np.array([25, 255, 255], dtype="uint8")
# # colored skin-tone 00128
# lower = np.array([0, 80, 30], dtype="uint8")
# upper = np.array([15, 255, 180], dtype="uint8")
video_frames, fps = load_video(file_path)
frame_count, width, height = get_video_dimensions(video_frames)
video_frames = video_frames[22:50]
skin_arr = np.ones([height, width])
max_vals = []
min_vals = []
# keep looping over the frames in the video
for i, frame in enumerate(video_frames):
# To check which HSV values are needed for a certain person
# cv2.rectangle(frame, (850, 530), (860, 550), (0, 255, 0), 2)
# cv2.rectangle(frame, (600, 1000), (860, 1080), (0, 255, 0), 2)
# frame[1000:1080, 600:860]
rect_hsv = cv2.cvtColor(frame[230:250, 750:760], cv2.COLOR_BGR2HSV)
# print(rect_hsv)q
max_in_rect = np.amax(rect_hsv, axis=(0, 1))
min_in_rect = np.amin(rect_hsv, axis=(0, 1))
# print(np.mean(rect_hsv, axis=(0, 1)))
max_vals.append(max_in_rect)
min_vals.append(min_in_rect)
# resize the frame, convert it to the HSV color space,
# and determine the HSV pixel intensities that fall into
# the speicifed upper and lower boundaries
converted = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
skin_mask = cv2.inRange(converted, lower, upper)
# apply a series of erosions and dilations to the mask
# using an elliptical kernel
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11, 11))
skin_mask = cv2.erode(skin_mask, kernel, iterations=2)
skin_mask = cv2.dilate(skin_mask, kernel, iterations=2)
# blur the mask to help remove noise, then apply the
# mask to the frame
skin_mask = cv2.GaussianBlur(skin_mask, (3, 3), 0)
skin = cv2.bitwise_and(frame, frame, mask=skin_mask)
# show the skin in the image along with the mask
cv2.imshow("images", np.hstack([frame, skin]))
# cv2.imshow("images", skin)
# if the 'q' key is pressed, stop the loop
if cv2.waitKey(1) & 0xFF == ord("q"):
break
# Reduce by 1 if pixel is not a skin pixel
mean_skin = np.mean(skin, axis=2)
low_values_indices = mean_skin < 1
skin_arr[low_values_indices] -= 1
# Where values are lower than threshold
final_mask = np.ones([height, width])
low_values_indices = skin_arr < -2
final_mask[low_values_indices] = 0
fig = plt.figure(figsize=(17, 9))
sub1 = fig.add_subplot(111)
sub1.set_title('Norm. Avg.')
sub1.imshow(final_mask, cmap=plt.cm.gray)
file_path_out = os.path.join(_dest_folder, 'Skin_' + _file[:-4])
fig.savefig(file_path_out + '.jpg')
if show_figure:
plt.show()
# Save it as .npy file
# np.save(file_path_out, final_mask)
print('Saved to ' + file_path_out)
# cleanup the camera and close any open windows
cv2.destroyAllWindows()