├── README.md ├── dlib_torch_converter.py ├── face.xml ├── negative_images ├── 0_0.jpg ├── 0_1.jpg ├── 0_2.jpg ├── 0_3.jpg ├── 0_4.jpg ├── 0_5.jpg ├── 0_6.jpg ├── 0_7.jpg ├── 0_8.jpg ├── 0_9.jpg ├── 10_0.jpg ├── 10_1.jpg ├── 10_2.jpg ├── 10_3.jpg ├── 10_4.jpg ├── 10_5.jpg ├── 10_6.jpg ├── 10_7.jpg ├── 10_8.jpg ├── 10_9.jpg ├── 11_0.jpg ├── 11_1.jpg ├── 11_2.jpg ├── 11_3.jpg ├── 11_4.jpg ├── 11_5.jpg ├── 11_6.jpg ├── 11_7.jpg ├── 11_8.jpg ├── 11_9.jpg ├── 12_0.jpg ├── 12_1.jpg ├── 12_2.jpg ├── 12_3.jpg ├── 12_4.jpg ├── 12_5.jpg ├── 12_6.jpg ├── 12_7.jpg ├── 12_8.jpg ├── 12_9.jpg ├── 13_0.jpg ├── 13_1.jpg ├── 13_2.jpg ├── 13_3.jpg ├── 13_4.jpg ├── 13_5.jpg ├── 13_6.jpg ├── 13_7.jpg ├── 13_8.jpg ├── 13_9.jpg ├── 14_0.jpg ├── 14_1.jpg ├── 14_2.jpg ├── 14_3.jpg ├── 14_4.jpg ├── 14_5.jpg ├── 14_6.jpg ├── 14_7.jpg ├── 14_8.jpg ├── 14_9.jpg ├── 15_0.jpg ├── 15_1.jpg ├── 15_2.jpg ├── 15_3.jpg ├── 15_4.jpg ├── 15_5.jpg ├── 15_6.jpg ├── 15_7.jpg ├── 15_8.jpg ├── 15_9.jpg ├── 16_0.jpg ├── 16_1.jpg ├── 16_2.jpg ├── 16_3.jpg ├── 16_4.jpg ├── 16_5.jpg ├── 16_6.jpg ├── 16_7.jpg ├── 16_8.jpg ├── 16_9.jpg ├── 17_0.jpg ├── 17_1.jpg ├── 17_2.jpg ├── 17_3.jpg ├── 17_4.jpg ├── 17_5.jpg ├── 17_6.jpg ├── 17_7.jpg ├── 17_8.jpg ├── 17_9.jpg ├── 18_0.jpg ├── 18_1.jpg ├── 18_2.jpg ├── 18_3.jpg ├── 18_4.jpg ├── 18_5.jpg ├── 18_6.jpg ├── 18_7.jpg ├── 18_8.jpg ├── 18_9.jpg ├── 19_0.jpg ├── 19_1.jpg ├── 19_2.jpg ├── 19_3.jpg ├── 19_4.jpg ├── 19_5.jpg ├── 19_6.jpg ├── 19_7.jpg ├── 19_8.jpg ├── 19_9.jpg ├── 1_0.jpg ├── 1_1.jpg ├── 1_2.jpg ├── 1_3.jpg ├── 1_4.jpg ├── 1_5.jpg ├── 1_6.jpg ├── 1_7.jpg ├── 1_8.jpg ├── 1_9.jpg ├── 20_0.jpg ├── 20_1.jpg ├── 20_2.jpg ├── 20_3.jpg ├── 20_4.jpg ├── 20_5.jpg ├── 20_6.jpg ├── 20_7.jpg ├── 20_8.jpg ├── 20_9.jpg ├── 21_0.jpg ├── 21_1.jpg ├── 21_2.jpg ├── 21_3.jpg ├── 21_4.jpg ├── 21_5.jpg ├── 21_6.jpg ├── 21_7.jpg ├── 21_8.jpg ├── 21_9.jpg ├── 22_0.jpg ├── 22_1.jpg ├── 22_2.jpg ├── 22_3.jpg ├── 22_4.jpg ├── 22_5.jpg ├── 22_6.jpg ├── 22_7.jpg ├── 22_8.jpg ├── 22_9.jpg ├── 23_0.jpg ├── 23_1.jpg ├── 23_2.jpg ├── 23_3.jpg ├── 23_4.jpg ├── 23_5.jpg ├── 23_6.jpg ├── 23_7.jpg ├── 23_8.jpg ├── 23_9.jpg ├── 24_0.jpg ├── 24_1.jpg ├── 24_2.jpg ├── 24_3.jpg ├── 24_4.jpg ├── 24_5.jpg ├── 24_6.jpg ├── 24_7.jpg ├── 24_8.jpg ├── 24_9.jpg ├── 25_0.jpg ├── 25_1.jpg ├── 25_2.jpg ├── 25_3.jpg ├── 25_4.jpg ├── 25_5.jpg ├── 25_6.jpg ├── 25_7.jpg ├── 25_8.jpg ├── 25_9.jpg ├── 26_0.jpg ├── 26_1.jpg ├── 26_2.jpg ├── 26_3.jpg ├── 26_4.jpg ├── 26_5.jpg ├── 26_6.jpg ├── 26_7.jpg ├── 26_8.jpg ├── 26_9.jpg ├── 27_0.jpg ├── 27_1.jpg ├── 27_2.jpg ├── 27_3.jpg ├── 27_4.jpg ├── 27_5.jpg ├── 27_6.jpg ├── 27_7.jpg ├── 27_8.jpg ├── 27_9.jpg ├── 28_0.jpg ├── 28_1.jpg ├── 28_2.jpg ├── 28_3.jpg ├── 28_4.jpg ├── 28_5.jpg ├── 28_6.jpg ├── 28_7.jpg ├── 28_8.jpg ├── 28_9.jpg ├── 29_0.jpg ├── 29_1.jpg ├── 29_2.jpg ├── 29_3.jpg ├── 29_4.jpg ├── 29_5.jpg ├── 29_6.jpg ├── 29_7.jpg ├── 29_8.jpg ├── 29_9.jpg ├── 2_0.jpg ├── 2_1.jpg ├── 2_2.jpg ├── 2_3.jpg ├── 2_4.jpg ├── 2_5.jpg ├── 2_6.jpg ├── 2_7.jpg ├── 2_8.jpg ├── 2_9.jpg ├── 3_0.jpg ├── 3_1.jpg ├── 3_2.jpg ├── 3_3.jpg ├── 3_4.jpg ├── 3_5.jpg ├── 3_6.jpg ├── 3_7.jpg ├── 3_8.jpg ├── 3_9.jpg ├── 4_0.jpg ├── 4_1.jpg ├── 4_2.jpg ├── 4_3.jpg ├── 4_4.jpg ├── 4_5.jpg ├── 4_6.jpg ├── 4_7.jpg ├── 4_8.jpg ├── 4_9.jpg ├── 5_0.jpg ├── 5_1.jpg ├── 5_2.jpg ├── 5_3.jpg ├── 5_4.jpg ├── 5_5.jpg ├── 5_6.jpg ├── 5_7.jpg ├── 5_8.jpg ├── 5_9.jpg ├── 6_0.jpg ├── 6_1.jpg ├── 6_2.jpg ├── 6_3.jpg ├── 6_4.jpg ├── 6_5.jpg ├── 6_6.jpg ├── 6_7.jpg ├── 6_8.jpg ├── 6_9.jpg ├── 7_0.jpg ├── 7_1.jpg ├── 7_2.jpg ├── 7_3.jpg ├── 7_4.jpg ├── 7_5.jpg ├── 7_6.jpg ├── 7_7.jpg ├── 7_8.jpg ├── 7_9.jpg ├── 8_0.jpg ├── 8_1.jpg ├── 8_2.jpg ├── 8_3.jpg ├── 8_4.jpg ├── 8_5.jpg ├── 8_6.jpg ├── 8_7.jpg ├── 8_8.jpg ├── 8_9.jpg ├── 9_0.jpg ├── 9_1.jpg ├── 9_2.jpg ├── 9_3.jpg ├── 9_4.jpg ├── 9_5.jpg ├── 9_6.jpg ├── 9_7.jpg ├── 9_8.jpg └── 9_9.jpg ├── positive_images ├── 0_0.jpg ├── 0_1.jpg ├── 0_2.jpg ├── 0_3.jpg ├── 0_4.jpg ├── 0_5.jpg ├── 0_6.jpg ├── 0_7.jpg ├── 0_8.jpg ├── 0_9.jpg ├── 10_0.jpg ├── 10_1.jpg ├── 10_2.jpg ├── 10_3.jpg ├── 10_4.jpg ├── 10_5.jpg ├── 10_6.jpg ├── 10_7.jpg ├── 10_8.jpg ├── 10_9.jpg ├── 11_0.jpg ├── 11_1.jpg ├── 11_2.jpg ├── 11_3.jpg ├── 11_4.jpg ├── 11_5.jpg ├── 11_6.jpg ├── 11_7.jpg ├── 11_8.jpg ├── 11_9.jpg ├── 12_0.jpg ├── 12_1.jpg ├── 12_2.jpg ├── 12_3.jpg ├── 12_4.jpg ├── 12_5.jpg ├── 12_6.jpg ├── 12_7.jpg ├── 12_8.jpg ├── 12_9.jpg ├── 13_0.jpg ├── 13_1.jpg ├── 13_2.jpg ├── 13_3.jpg ├── 13_4.jpg ├── 13_5.jpg ├── 13_6.jpg ├── 13_7.jpg ├── 13_8.jpg ├── 13_9.jpg ├── 14_0.jpg ├── 14_1.jpg ├── 14_2.jpg ├── 14_3.jpg ├── 14_4.jpg ├── 14_5.jpg ├── 14_6.jpg ├── 14_7.jpg ├── 14_8.jpg ├── 14_9.jpg ├── 15_0.jpg ├── 15_1.jpg ├── 15_2.jpg ├── 15_3.jpg ├── 15_4.jpg ├── 15_5.jpg ├── 15_6.jpg ├── 15_7.jpg ├── 15_8.jpg ├── 15_9.jpg ├── 16_0.jpg ├── 16_1.jpg ├── 16_2.jpg ├── 16_3.jpg ├── 16_4.jpg ├── 16_5.jpg ├── 16_6.jpg ├── 16_7.jpg ├── 16_8.jpg ├── 16_9.jpg ├── 17_0.jpg ├── 17_1.jpg ├── 17_2.jpg ├── 17_3.jpg ├── 17_4.jpg ├── 17_5.jpg ├── 17_6.jpg ├── 17_7.jpg ├── 17_8.jpg ├── 17_9.jpg ├── 18_0.jpg ├── 18_1.jpg ├── 18_2.jpg ├── 18_3.jpg ├── 18_4.jpg ├── 18_5.jpg ├── 18_6.jpg ├── 18_7.jpg ├── 18_8.jpg ├── 18_9.jpg ├── 19_0.jpg ├── 19_1.jpg ├── 19_2.jpg ├── 19_3.jpg ├── 19_4.jpg ├── 19_5.jpg ├── 19_6.jpg ├── 19_7.jpg ├── 19_8.jpg ├── 19_9.jpg ├── 1_0.jpg ├── 1_1.jpg ├── 1_2.jpg ├── 1_3.jpg ├── 1_4.jpg ├── 1_5.jpg ├── 1_6.jpg ├── 1_7.jpg ├── 1_8.jpg ├── 1_9.jpg ├── 20_0.jpg ├── 20_1.jpg ├── 20_2.jpg ├── 20_3.jpg ├── 20_4.jpg ├── 20_5.jpg ├── 20_6.jpg ├── 20_7.jpg ├── 20_8.jpg ├── 20_9.jpg ├── 21_0.jpg ├── 21_1.jpg ├── 21_2.jpg ├── 21_3.jpg ├── 21_4.jpg ├── 21_5.jpg ├── 21_6.jpg ├── 21_7.jpg ├── 21_8.jpg ├── 21_9.jpg ├── 22_0.jpg ├── 22_1.jpg ├── 22_2.jpg ├── 22_3.jpg ├── 22_4.jpg ├── 22_5.jpg ├── 22_6.jpg ├── 22_7.jpg ├── 22_8.jpg ├── 22_9.jpg ├── 23_0.jpg ├── 23_1.jpg ├── 23_2.jpg ├── 23_3.jpg ├── 23_4.jpg ├── 23_5.jpg ├── 23_6.jpg ├── 23_7.jpg ├── 23_8.jpg ├── 23_9.jpg ├── 24_0.jpg ├── 24_1.jpg ├── 24_2.jpg ├── 24_3.jpg ├── 24_4.jpg ├── 24_5.jpg ├── 24_6.jpg ├── 24_7.jpg ├── 24_8.jpg ├── 24_9.jpg ├── 25_0.jpg ├── 25_1.jpg ├── 25_2.jpg ├── 25_3.jpg ├── 25_4.jpg ├── 25_5.jpg ├── 25_6.jpg ├── 25_7.jpg ├── 25_8.jpg ├── 25_9.jpg ├── 26_0.jpg ├── 26_1.jpg ├── 26_2.jpg ├── 26_3.jpg ├── 26_4.jpg ├── 26_5.jpg ├── 26_6.jpg ├── 26_7.jpg ├── 26_8.jpg ├── 26_9.jpg ├── 27_0.jpg ├── 27_1.jpg ├── 27_2.jpg ├── 27_3.jpg ├── 27_4.jpg ├── 27_5.jpg ├── 27_6.jpg ├── 27_7.jpg ├── 27_8.jpg ├── 27_9.jpg ├── 28_0.jpg ├── 28_1.jpg ├── 28_2.jpg ├── 28_3.jpg ├── 28_4.jpg ├── 28_5.jpg ├── 28_6.jpg ├── 28_7.jpg ├── 28_8.jpg ├── 28_9.jpg ├── 29_0.jpg ├── 29_1.jpg ├── 29_2.jpg ├── 29_3.jpg ├── 29_4.jpg ├── 29_5.jpg ├── 29_6.jpg ├── 29_7.jpg ├── 29_8.jpg ├── 29_9.jpg ├── 2_0.jpg ├── 2_1.jpg ├── 2_2.jpg ├── 2_3.jpg ├── 2_4.jpg ├── 2_5.jpg ├── 2_6.jpg ├── 2_7.jpg ├── 2_8.jpg ├── 2_9.jpg ├── 3_0.jpg ├── 3_1.jpg ├── 3_2.jpg ├── 3_3.jpg ├── 3_4.jpg ├── 3_5.jpg ├── 3_6.jpg ├── 3_7.jpg ├── 3_8.jpg ├── 3_9.jpg ├── 4_0.jpg ├── 4_1.jpg ├── 4_2.jpg ├── 4_3.jpg ├── 4_4.jpg ├── 4_5.jpg ├── 4_6.jpg ├── 4_7.jpg ├── 4_8.jpg ├── 4_9.jpg ├── 5_0.jpg ├── 5_1.jpg ├── 5_2.jpg ├── 5_3.jpg ├── 5_4.jpg ├── 5_5.jpg ├── 5_6.jpg ├── 5_7.jpg ├── 5_8.jpg ├── 5_9.jpg ├── 6_0.jpg ├── 6_1.jpg ├── 6_2.jpg ├── 6_3.jpg ├── 6_4.jpg ├── 6_5.jpg ├── 6_6.jpg ├── 6_7.jpg ├── 6_8.jpg ├── 6_9.jpg ├── 7_0.jpg ├── 7_1.jpg ├── 7_2.jpg ├── 7_3.jpg ├── 7_4.jpg ├── 7_5.jpg ├── 7_6.jpg ├── 7_7.jpg ├── 7_8.jpg ├── 7_9.jpg ├── 8_0.jpg ├── 8_1.jpg ├── 8_2.jpg ├── 8_3.jpg ├── 8_4.jpg ├── 8_5.jpg ├── 8_6.jpg ├── 8_7.jpg ├── 8_8.jpg ├── 8_9.jpg ├── 9_0.jpg ├── 9_1.jpg ├── 9_2.jpg ├── 9_3.jpg ├── 9_4.jpg ├── 9_5.jpg ├── 9_6.jpg ├── 9_7.jpg ├── 9_8.jpg ├── 9_9.jpg ├── prev.jpg └── result.jpg ├── visualize_faces.py └── webcam_example.py /README.md: -------------------------------------------------------------------------------- 1 | ## Dlib's face detector ported to PyTorch 2 | 3 | 4 | ![](https://raw.githubusercontent.com/jacobgil/dlib_facedetector_pytorch/master/positive_images/13_4.jpg)![](https://raw.githubusercontent.com/jacobgil/dlib_facedetector_pytorch/master/positive_images/18_3.jpg)![](https://raw.githubusercontent.com/jacobgil/dlib_facedetector_pytorch/master/positive_images/18_6.jpg)![](https://raw.githubusercontent.com/jacobgil/dlib_facedetector_pytorch/master/positive_images/1_5.jpg)![](https://raw.githubusercontent.com/jacobgil/dlib_facedetector_pytorch/master/negative_images/0_4.jpg) 5 | 6 | This repository contains code to: 7 | 8 | - Convert a Dlib model in XML format, to a PyTorch sequential object. 9 | This happens in dlib_torch_converter.py, in get_model. 10 | - Run multi scale face detection in a webcam. 11 | ``` python webcam_example.py face.xml``` 12 | - Hallucinate faces using activation maximization. 13 | ``` python webcam_example.py face.xml``` 14 | 15 | -------------------------------------------------------------------------------- /dlib_torch_converter.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import torch 3 | import torch.nn as nn 4 | import xml.etree.ElementTree as ET 5 | 6 | def get_model(path): 7 | layers = [] 8 | in_channels = 3 9 | 10 | tree = ET.parse(path) 11 | elems = [elem for elem in tree.iter()] 12 | elems = elems[::-1] 13 | 14 | for elem in elems: 15 | meta = elem.attrib 16 | if elem.tag == 'con' or elem.tag == 'affine_con': 17 | for key in meta: 18 | meta[key] = int(meta[key]) 19 | data = ''.join([x for x in elem.text.strip() \ 20 | if x != '\n' and x != '\r']) 21 | data = [float(x) for x in data.split(' ') if x != ''] 22 | data = np.float32(data) 23 | 24 | if elem.tag == 'con': 25 | layer = torch.nn.Conv2d(in_channels=in_channels, 26 | out_channels=meta['num_filters'], 27 | kernel_size = (meta['nr'], meta['nc']), 28 | stride = (meta['stride_y'], meta['stride_x']), 29 | padding = (meta['padding_y'], meta['padding_x']), 30 | bias = True) 31 | 32 | 33 | size = meta['num_filters']*meta['nr']*meta['nc']*in_channels 34 | 35 | filter_data = data[ : size] 36 | bias_data = data[size : ] 37 | 38 | filter_data = np.reshape(filter_data, (meta['num_filters'], \ 39 | in_channels, meta['nr'], meta['nc'])) 40 | 41 | layer.weight.data = torch.from_numpy(filter_data) 42 | layer.bias.data = torch.from_numpy(bias_data) 43 | 44 | layers.append(layer) 45 | in_channels = meta['num_filters'] 46 | 47 | # The dilation can be increased to have a larger receptive field, 48 | # for example for use in webcams when the person is 49 | # close to the camera. 50 | layer.dilation = 1 51 | 52 | 53 | elif elem.tag == 'affine_con': 54 | layer = torch.nn.BatchNorm2d(num_features=in_channels) 55 | 56 | layer.running_var = \ 57 | torch.from_numpy(np.float32(np.ones(in_channels))) 58 | layer.running_mean = \ 59 | torch.from_numpy(np.float32(np.zeros(in_channels))) 60 | 61 | layer.weight.data = torch.from_numpy(data[ : in_channels]) 62 | layer.bias.data = torch.from_numpy(data[in_channels : ]) 63 | 64 | layers.append(layer) 65 | 66 | elif elem.tag == 'relu': 67 | layers.append(nn.ReLU()) 68 | 69 | model = nn.Sequential(*layers) 70 | return model -------------------------------------------------------------------------------- /negative_images/0_0.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jacobgil/dlib_facedetector_pytorch/2ed74afdf98ea692b18b8c5621d66ba6939ffe08/negative_images/0_0.jpg -------------------------------------------------------------------------------- /negative_images/0_1.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jacobgil/dlib_facedetector_pytorch/2ed74afdf98ea692b18b8c5621d66ba6939ffe08/negative_images/0_1.jpg -------------------------------------------------------------------------------- /negative_images/0_2.jpg: 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-------------------------------------------------------------------------------- 1 | import sys 2 | import numpy as np 3 | import torch 4 | import torch.nn as nn 5 | from torch.autograd import Variable 6 | import xml.etree.ElementTree as ET 7 | import cv2 8 | import scipy.misc 9 | import random 10 | from skimage.transform import rotate 11 | from dlib_torch_converter import get_model 12 | 13 | def rotate_img(img, angle): 14 | for index in range(3): 15 | channel = img[0, index, :, :] 16 | channel = rotate(channel, angle, resize=False, preserve_range=True) 17 | img[0, index, :, :] = channel 18 | return np.float32(img) 19 | 20 | 21 | def tensor_to_np_image(input): 22 | img = input.cpu().data.numpy()[0, :] 23 | img = np.transpose(img, (1, 2, 0)) 24 | img = img - np.min(img) 25 | img = img / np.max(img) 26 | img = img[:, :, ::-1] 27 | return img 28 | 29 | def activation_maximization(model, filter_index): 30 | model.eval() 31 | N = 128 32 | input = np.float32(np.ones((1, 3, N, N))) / 255 33 | input = Variable(torch.from_numpy(input), requires_grad=True) 34 | 35 | # forward pass: 36 | iterations = 900 37 | lr = 1.0 38 | 39 | for i in xrange(iterations): 40 | 41 | img = input.data.numpy() 42 | # Random rotation: 43 | angle = np.float32(np.random.uniform(-30, 30)) * 1 44 | 45 | use_flip = random.random() > 0.5 46 | if use_flip: 47 | img[:, :, :, :] = img[:, :, :, ::-1] 48 | 49 | img = rotate_img(img, angle) 50 | 51 | 52 | input.data = torch.from_numpy(img) 53 | 54 | input_cuda = input 55 | model.zero_grad() 56 | 57 | # The activation is before softmax 58 | out = model(input_cuda) 59 | #out = torch.nn.Sigmoid()(out) 60 | size = out.size(2) 61 | # Take the middle pixel in the image. 62 | # For classification this will be just out[0, category] 63 | loss = out[0, filter_index, size//2, size//2] 64 | #loss = out[0, filter_index, :, :].mean() 65 | loss.backward() 66 | 67 | # Normalize the gradient to a unit vector 68 | grad_cpu = input.grad 69 | grad_cpu = grad_cpu / torch.norm(grad_cpu, 2) 70 | data = grad_cpu.data.numpy() 71 | 72 | input = input + lr * grad_cpu 73 | input.volatile=False 74 | input.requires_grad=True 75 | 76 | data = input.data.numpy() 77 | 78 | img = input.data.numpy() 79 | img = rotate_img(img, -angle) 80 | if use_flip: 81 | img[:, :, :, :] = img[:, :, :, ::-1] 82 | 83 | # Weight decay regularization for the visualization 84 | input.data = torch.from_numpy(img) 85 | 86 | print(i) 87 | img = tensor_to_np_image(input) 88 | cv2.imshow("img", img) 89 | cv2.waitKey(10) 90 | 91 | 92 | img = tensor_to_np_image(input) 93 | img = np.uint8(255 * img) 94 | return img 95 | 96 | if __name__ == '__main__': 97 | model = get_model(sys.argv[1]) 98 | 99 | # Skip the last layer (and the BN layer before it) 100 | model = nn.Sequential(*[model._modules[i] \ 101 | for i in model._modules.keys()[:-2]]) 102 | 103 | for filter_index in range(0, 30): 104 | for index in range(10): 105 | img = activation_maximization(model, filter_index) 106 | output_path = "_".join([str(filter_index), str(index)]) + ".jpg" 107 | cv2.imwrite(output_path, img) -------------------------------------------------------------------------------- /webcam_example.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import time 3 | import torch 4 | import torch.nn as nn 5 | import cv2 6 | from torch.autograd import Variable 7 | import numpy as np 8 | from skimage.feature import peak_local_max 9 | from skimage.transform import resize as sk_resize 10 | 11 | from dlib_torch_converter import get_model 12 | 13 | def recepive_field(model): 14 | start_in = 0 15 | j_in = 1 16 | r_in = 0 17 | for module in model._modules: 18 | m = model._modules[module] 19 | if type(m) is nn.Conv2d: 20 | if type(m.dilation) is tuple: 21 | dilation = m.dilation[0] 22 | else: 23 | dilation = m.dilation 24 | kernel_size = m.kernel_size[0] + (m.kernel_size[0]-1)*(dilation-1) 25 | j_out = j_in * m.stride[0] 26 | r_out = r_in + (kernel_size - 1) * m.stride[0] 27 | start_out = start_in + ((kernel_size-1)/2 - m.padding[0])*j_in 28 | 29 | start_in, j_in, r_in = start_out, j_out, r_out 30 | 31 | return r_out, start_out 32 | 33 | def intersects(rect1, rect2): 34 | x1, y1, w1, h1, _ = rect1 35 | x2, y2, w2, h2, _ = rect2 36 | x_intersection = False 37 | y_intersection = False 38 | if x1 <= x2 + w2 and x1 + w1 >= x2: 39 | x_intersection = True 40 | if x2 <= x1 + w1 and x2 + w2 >= x1: 41 | x_intersection = True 42 | if y1 <= y2 + h2 and y1 + h1 >= y2: 43 | y_intersection = True 44 | if y2 <= y1 + h1 and y2 + h2 >= y1: 45 | y_intersection = True 46 | 47 | if x_intersection and y_intersection: 48 | return True 49 | else: 50 | return False 51 | 52 | 53 | def detect_img(model, img): 54 | model.eval() 55 | orig_shape = img.shape[0 : 2] 56 | 57 | r=122.781998 58 | g=117.000999 59 | b=104.297997 60 | 61 | img[: , :, 0] -= b 62 | img[: , :, 1] -= g 63 | img[: , :, 2] -= r 64 | 65 | img = img[:, :, ::-1].copy() / 256.0 66 | 67 | img = img.transpose((2, 0, 1)) 68 | img = np.float32(img) 69 | img = torch.from_numpy(img) 70 | 71 | img = img.unsqueeze(0) 72 | 73 | output = model(Variable(img, volatile=True)) 74 | 75 | output = output.data.numpy() 76 | 77 | output = output[0, 0, :, :] 78 | return output 79 | 80 | def detect_multi_scale(model, img, scales, recepive_field): 81 | result = [] 82 | for scale in scales: 83 | scaled_img = cv2.resize(img, (int(img.shape[1]/scale), int(img.shape[0]/scale))) 84 | output = detect_img(model, np.float32(scaled_img)) 85 | output = sk_resize(output, output_shape=img.shape[:2], preserve_range=True) 86 | output = np.float32(output) 87 | #cv2.imshow("Scale " + str(scale), output) 88 | #cv2.waitKey(10) 89 | coordinates = peak_local_max(output, min_distance=30, threshold_abs=-1) 90 | for y, x in coordinates: 91 | detection = map(int, [x, y, recepive_field*scale, recepive_field*scale, output[y, x]]) 92 | result.append(detection) 93 | 94 | # Non maximal surpression: 95 | result = sorted(result, key = lambda r: r[-1], reverse=True) 96 | after_nms = [] 97 | for detection in result: 98 | keep_detection = True 99 | for other_detection in after_nms: 100 | if intersects(detection, other_detection): 101 | keep_detection = False 102 | break 103 | if keep_detection: 104 | after_nms.append(detection) 105 | 106 | return after_nms 107 | 108 | def detect_webcam(model): 109 | recepive_field_size, offset = recepive_field(model) 110 | print "Receptive field", recepive_field_size, "Center offset", offset 111 | scales = [2.7, 3.5] 112 | cap = cv2.VideoCapture(0) 113 | 114 | while True: 115 | coordinates = [] 116 | ret, img = cap.read() 117 | t0 = time.time() 118 | coordinates = detect_multi_scale(model, img, scales, recepive_field_size) 119 | t1 = time.time() 120 | print("Inference time for frame", t1 - t0) 121 | 122 | 123 | for x, y, width, height, score in coordinates: 124 | x = x - offset//2 125 | y = y - offset//2 126 | cv2.rectangle(img, (x-width//2, y-height//2), (x+width//2, y+height//2), (0, 255, 0)) 127 | cv2.imshow("result", img) 128 | cv2.waitKey(10) 129 | 130 | if __name__ == '__main__': 131 | model = get_model(sys.argv[1]) 132 | detect_webcam(model) --------------------------------------------------------------------------------