import sys, os, argparse
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import numpy as np
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import cv2
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import matplotlib.pyplot as plt
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import torch
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import torch.nn as nn
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from torch.autograd import Variable
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from torch.utils.data import DataLoader
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from torchvision import transforms
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import torch.backends.cudnn as cudnn
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import torchvision
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import torch.nn.functional as F
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from PIL import Image
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import datasets, hopenet, utils
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from skimage import io
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import dlib
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def parse_args():
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"""Parse input arguments."""
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parser = argparse.ArgumentParser(description='Head pose estimation using the Hopenet network.')
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parser.add_argument('--gpu', dest='gpu_id', help='GPU device id to use [0]',
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default=0, type=int)
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parser.add_argument('--snapshot', dest='snapshot', help='Path of model snapshot.',
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default='', type=str)
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parser.add_argument('--face_model', dest='face_model', help='Path of DLIB face detection model.',
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default='', type=str)
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parser.add_argument('--video', dest='video_path', help='Path of video')
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parser.add_argument('--output_string', dest='output_string', help='String appended to output file')
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parser.add_argument('--n_frames', dest='n_frames', help='Number of frames', type=int)
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parser.add_argument('--fps', dest='fps', help='Frames per second of source video', type=float, default=30.)
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args = parser.parse_args()
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return args
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if __name__ == '__main__':
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args = parse_args()
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cudnn.enabled = True
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batch_size = 1
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gpu = args.gpu_id
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snapshot_path = args.snapshot
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out_dir = 'output/video'
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video_path = args.video_path
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if not os.path.exists(out_dir):
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os.makedirs(out_dir)
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if not os.path.exists(args.video_path):
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sys.exit('Video does not exist')
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# ResNet50 structure
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model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66)
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# Dlib face detection model
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cnn_face_detector = dlib.cnn_face_detection_model_v1(args.face_model)
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print 'Loading snapshot.'
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# Load snapshot
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saved_state_dict = torch.load(snapshot_path)
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model.load_state_dict(saved_state_dict)
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print 'Loading data.'
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transformations = transforms.Compose([transforms.Scale(224),
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transforms.CenterCrop(224), transforms.ToTensor(),
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transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
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model.cuda(gpu)
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print 'Ready to test network.'
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# Test the Model
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model.eval() # Change model to 'eval' mode (BN uses moving mean/var).
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total = 0
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idx_tensor = [idx for idx in xrange(66)]
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idx_tensor = torch.FloatTensor(idx_tensor).cuda(gpu)
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video = cv2.VideoCapture(video_path)
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# New cv2
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width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH)) # float
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height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT)) # float
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# Define the codec and create VideoWriter object
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fourcc = cv2.VideoWriter_fourcc(*'MJPG')
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out = cv2.VideoWriter('output/video/output-%s.avi' % args.output_string, fourcc, args.fps, (width, height))
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# # Old cv2
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# width = int(video.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH)) # float
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# height = int(video.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT)) # float
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#
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# # Define the codec and create VideoWriter object
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# fourcc = cv2.cv.CV_FOURCC(*'MJPG')
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# out = cv2.VideoWriter('output/video/output-%s.avi' % args.output_string, fourcc, 30.0, (width, height))
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txt_out = open('output/video/output-%s.txt' % args.output_string, 'w')
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frame_num = 1
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while frame_num <= args.n_frames:
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print frame_num
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ret,frame = video.read()
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if ret == False:
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break
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cv2_frame = cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)
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# Dlib detect
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dets = cnn_face_detector(cv2_frame, 1)
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for idx, det in enumerate(dets):
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# Get x_min, y_min, x_max, y_max, conf
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x_min = det.rect.left()
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y_min = det.rect.top()
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x_max = det.rect.right()
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y_max = det.rect.bottom()
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conf = det.confidence
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if conf > 1.0:
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bbox_width = abs(x_max - x_min)
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bbox_height = abs(y_max - y_min)
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x_min -= 2 * bbox_width / 4
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x_max += 2 * bbox_width / 4
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y_min -= 3 * bbox_height / 4
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y_max += bbox_height / 4
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x_min = max(x_min, 0); y_min = max(y_min, 0)
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x_max = min(frame.shape[1], x_max); y_max = min(frame.shape[0], y_max)
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# Crop image
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img = cv2_frame[y_min:y_max,x_min:x_max]
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img = Image.fromarray(img)
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# Transform
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img = transformations(img)
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img_shape = img.size()
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img = img.view(1, img_shape[0], img_shape[1], img_shape[2])
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img = Variable(img).cuda(gpu)
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yaw, pitch, roll = model(img)
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yaw_predicted = F.softmax(yaw)
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pitch_predicted = F.softmax(pitch)
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roll_predicted = F.softmax(roll)
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# Get continuous predictions in degrees.
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yaw_predicted = torch.sum(yaw_predicted.data[0] * idx_tensor) * 3 - 99
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pitch_predicted = torch.sum(pitch_predicted.data[0] * idx_tensor) * 3 - 99
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roll_predicted = torch.sum(roll_predicted.data[0] * idx_tensor) * 3 - 99
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# Print new frame with cube and axis
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txt_out.write(str(frame_num) + ' %f %f %f\n' % (yaw_predicted, pitch_predicted, roll_predicted))
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# utils.plot_pose_cube(frame, yaw_predicted, pitch_predicted, roll_predicted, (x_min + x_max) / 2, (y_min + y_max) / 2, size = bbox_width)
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utils.draw_axis(frame, yaw_predicted, pitch_predicted, roll_predicted, tdx = (x_min + x_max) / 2, tdy= (y_min + y_max) / 2, size = bbox_height/2)
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# Plot expanded bounding box
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# cv2.rectangle(frame, (x_min, y_min), (x_max, y_max), (0,255,0), 1)
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out.write(frame)
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frame_num += 1
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out.release()
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video.release()
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