def run(): DC = 0 line1 = [] while DC <= DC_max: update_duty_cycle(DC) wait_for_temperature_to_equalize(DC) #line1 = live_plotter(DC_values,temperature_values,line1) DC += DC_step line1 = live_plotter(DC_values,temperature_values,line1)
return line.astype(np.float) def follow(thefile): thefile.seek(0,2) while True: line = thefile.readline() if not line: time.sleep(0.1) continue yield line if __name__ == '__main__': logfile = open("E:/laure/Documents/HackUTD/TapVRap/tap-standalonewin-sdk-master/TAPWinApp/bin/Debug/data.txt","r") loglines = follow(logfile) axis = 1 size = 100 x = np.linspace(0,1,size+1)[0:-1] y = np.zeros(len(x)) line1 = [] for line in loglines: data = line.split(',') data = np.array(data) newData = cleanData(data) y[-1] = newData[axis] line1 = live_plotter(x,y ,line1) y = np.append(y[1:],0.0)
window_end_datetime = play_time + dt.timedelta( minutes=window_length) #While realtime or replaying, simulate log gap times (and plot zeroes) (i.e. periods of inactivity) while play_time <= log_datetime: play_time = play_time + dt.timedelta(seconds=30) if plot: y_vec_lp[-1] = 0 y_vec_pap[-1] = 0 line_lp, line_lp_avg, line_pap, line_pap_avg = live_plotter( x_vec, y_vec_lp, line_lp, y_vec_lp_avg, line_lp_avg, y_vec_pap, line_pap, y_vec_pap_avg, line_pap_avg, identifier='PHT Activity', pause_time=.2) y_vec_lp = np.append(y_vec_lp[1:], 0.0) y_vec_lp_avg = np.append(y_vec_lp_avg[1:], y_vec_lp_avg[-1]) y_vec_pap = np.append(y_vec_pap[1:], 0.0) y_vec_pap_avg = np.append(y_vec_pap_avg[1:], y_vec_pap_avg[-1]) #Stop replay/file iteration if log_datetime > play_end: playing = False
message = str(message).split(",") accelerometer_readings = [] if timer > 1000: break for index, val in enumerate(message): val = val.strip() if val == "3": accelerometer_readings.append( float(message[index + 1].replace("'", "").strip())) accelerometer_readings.append( float(message[index + 2].replace("'", "").strip())) accelerometer_readings.append( float(message[index + 3].replace("'", "").strip())) y_vec[-1] = accelerometer_readings[2] accelerometer_readings_z.append(accelerometer_readings[2]) line1 = live_plotter(x_vec, y_vec, line1) y_vec = np.append(y_vec[1:], 0.0) except (KeyboardInterrupt, SystemExit): raise except: traceback.print_exc() with open('accelerometer.csv', mode='w') as csv_file: fieldnames = ['accelerometer_z'] writer = csv.DictWriter(csv_file, fieldnames=fieldnames) writer.writeheader() for reading in accelerometer_readings_z: writer.writerow({'accelerometer_z': reading}) print(accelerometer_readings_z)
from pylive import live_plotter import numpy as np size = 100 x_vec = np.linspace(0, 1, size + 1)[0:-1] y_vec = np.random.randn(len(x_vec)) line1 = [] while True: rand_val = np.random.randn(1) y_vec[-1] = rand_val line1 = live_plotter(x_vec, y_vec, line1, "TRIAL") y_vec = np.append(y_vec[1:], 0.0)
from pylive import live_plotter import numpy as np size = 100 x_vec = np.linspace(0, 1, size + 1)[0:-1] y_vec = np.random.randn(len(x_vec)) line1 = [] while True: rand_val = np.random.randn(1) y_vec[-1] = rand_val line1 = live_plotter(x_vec, y_vec, line1, "Dynamic Overall Load") y_vec = np.append(y_vec[1:], 0.0)
def process(input): print("[INFO] loading source image and checkpoint...") source_path = input checkpoint_path = args['checkpoint'] if args['input_video']: video_path = args['input_video'] else: video_path = None source_image = imageio.imread(source_path) source_image = resize(source_image, (256, 256))[..., :3] generator, kp_detector = load_checkpoints( config_path='config/vox-256.yaml', checkpoint_path=checkpoint_path) # Load the cascade face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') if not os.path.exists('output'): os.mkdir('output') relative = True adapt_movement_scale = True if args['cpu']: cpu = True else: cpu = False if video_path: cap = cv2.VideoCapture(video_path) print("[INFO] Loading video from the given path") else: cap = cv2.VideoCapture(0) print("[INFO] Initializing front camera...") # get vcap property width = cap.get(cv2.CAP_PROP_FRAME_WIDTH) # float `width` height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT) # float `height` fps = cap.get(cv2.CAP_PROP_FPS) frame_count = cap.get(cv2.CAP_PROP_FRAME_COUNT) print('resolution : {} x {}'.format(width, height)) print('frame rate : {} \nframe count : {}'.format(fps, frame_count)) fourcc = cv2.VideoWriter_fourcc(*'MJPG') out1 = cv2.VideoWriter('output/test.avi', fourcc, 12, (256 * 3, 256), True) cv2_source = cv2.cvtColor(source_image.astype('float32'), cv2.COLOR_BGR2RGB) cv2_source2 = (source_image * 255).astype(np.uint8) if args['vc']: camera = pyfakewebcam.FakeWebcam('/dev/video7', 640, 360) camera._settings.fmt.pix.width = 640 camera._settings.fmt.pix.height = 360 img = np.zeros((360, 640, 3), dtype=np.uint8) yoff = round((360 - 256) / 2) xoff = round((640 - 256) / 2) img_im = img.copy() img_cv2_source = img.copy() img_im[:, :, 2] = 255 img_cv2_source[:, :, 2] = 255 with torch.no_grad(): predictions = [] source = torch.tensor(source_image[np.newaxis].astype( np.float32)).permute(0, 3, 1, 2) if not cpu: source = source.cuda() kp_source = kp_detector(source) count = 0 fps = [] if args['csv']: line1 = [] size = 10 x_vec = np.linspace(0, 1, size + 1)[0:-1] y_vec = np.random.randn(len(x_vec)) while (True): start = time.time() ret, frame = cap.read() gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Detect the faces faces = face_cascade.detectMultiScale(gray, 1.1, 4) frame = cv2.flip(frame, 1) if ret == True: if not video_path: x = 143 y = 87 w = 322 h = 322 frame = frame[y:y + h, x:x + w] frame1 = resize(frame, (256, 256))[..., :3] if count == 0: source_image1 = frame1 source1 = torch.tensor(source_image1[np.newaxis].astype( np.float32)).permute(0, 3, 1, 2) kp_driving_initial = kp_detector(source1) frame_test = torch.tensor(frame1[np.newaxis].astype( np.float32)).permute(0, 3, 1, 2) driving_frame = frame_test if not cpu: driving_frame = driving_frame.cuda() kp_driving = kp_detector(driving_frame) kp_norm = normalize_kp( kp_source=kp_source, kp_driving=kp_driving, kp_driving_initial=kp_driving_initial, use_relative_movement=relative, use_relative_jacobian=relative, adapt_movement_scale=adapt_movement_scale) out = generator(source, kp_source=kp_source, kp_driving=kp_norm) predictions.append( np.transpose(out['prediction'].data.cpu().numpy(), [0, 2, 3, 1])[0]) im = np.transpose(out['prediction'].data.cpu().numpy(), [0, 2, 3, 1])[0] #im = cv2.cvtColor(im,cv2.COLOR_RGB2BGR) #cv2_source = cv2.cvtColor(cv2_source,cv2.COLOR_RGB2BGR) im = (np.array(im) * 255).astype(np.uint8) #cv2_source = (np.array(cv2_source)*255).astype(np.uint8) img_im[yoff:yoff + 256, xoff:xoff + 256] = im img_cv2_source[yoff:yoff + 256, xoff:xoff + 256] = cv2_source2 #print(faces) #print(type(im)) if args['debug']: #print("[DEBUG] FPS : ",1.0 / (time.time()-start)) fps.append(1.0 / (time.time() - start)) if args['cpu']: print("[DEBUG] Avg. of FPS using CPU : ", mean(fps)) else: print("[DEBUG] Avg. of FPS using GPU : ", mean(fps)) if args['csv']: y_vec[-1] = mean(fps) line1 = live_plotter(x_vec, y_vec, line1) y_vec = np.append(y_vec[1:], 0.0) if args['vc']: if np.array(faces).any(): #joinedFrame = np.concatenate((cv2_source,im,frame1),axis=1) camera.schedule_frame(img_im) else: #joinedFrame = np.concatenate((cv2_source,cv2_source,frame1),axis=1) camera.schedule_frame(img_cv2_source) #cv2.imshow('Test',joinedFrame) #out1.write(img_as_ubyte(np.array(im))) count += 1 else: break cap.release() out1.release() cv2.destroyAllWindows()