├── convert_to_onnx.sh ├── .gitmodules ├── Dockerfile ├── .github └── ISSUE_TEMPLATE │ ├── feature_request.md │ └── bug_report.md ├── convert_to_tfjs.py ├── timings.py ├── timings.html ├── predict.py ├── models ├── unet.py └── deeplab.py ├── utils.py ├── matlab ├── README.md ├── example.m └── Meye.m ├── README.md ├── train_dl.py ├── dataloader.py ├── train.py ├── show.py ├── evaluate.py └── LICENSE /convert_to_onnx.sh: -------------------------------------------------------------------------------- 1 | #/bin/bash 2 | 3 | # prerequisite: 4 | # pip install tf2onnx 5 | 6 | RUNDIR="path/to/run/dir" 7 | 8 | python -m tf2onnx.convert --saved-model $RUNDIR/best_savedmodel --output $RUNDIR/best_model.onnx -------------------------------------------------------------------------------- /.gitmodules: -------------------------------------------------------------------------------- 1 | [submodule "expman"] 2 | path = expman 3 | url = https://github.com/fabiocarrara/expman 4 | [submodule "models/deeplab"] 5 | path = models/deeplab 6 | url = https://github.com/david8862/tf-keras-deeplabv3p-model-set 7 | -------------------------------------------------------------------------------- /Dockerfile: -------------------------------------------------------------------------------- 1 | FROM tensorflow/tensorflow:2.4.1-gpu-jupyter 2 | 3 | RUN apt update && apt install -y nvidia-modprobe 4 | RUN pip install adabelief-tf pandas imageio imageio-ffmpeg seaborn sklearn tensorflow_addons tqdm 5 | RUN pip install keras_applications tensorflowjs 6 | RUN pip install tf2onnx 7 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/feature_request.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Feature request 3 | about: Suggest an idea for this project 4 | title: '' 5 | labels: enhancement 6 | assignees: '' 7 | 8 | --- 9 | 10 | **Is your feature request related to a problem? Please describe.** 11 | A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] 12 | 13 | **Describe the solution you'd like** 14 | A clear and concise description of what you want to happen. 15 | 16 | **Describe alternatives you've considered** 17 | A clear and concise description of any alternative solutions or features you've considered. 18 | 19 | **Additional context** 20 | Add any other context or screenshots about the feature request here. 21 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/bug_report.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Bug report 3 | about: Create a report to help us improve 4 | title: '' 5 | labels: bug 6 | assignees: '' 7 | 8 | --- 9 | 10 | **Describe the bug** 11 | A clear and concise description of what the bug is. 12 | 13 | **To Reproduce** 14 | Steps to reproduce the behavior: 15 | 1. Go to '...' 16 | 2. Click on '....' 17 | 3. Scroll down to '....' 18 | 4. See error 19 | 20 | **Expected behavior** 21 | A clear and concise description of what you expected to happen. 22 | 23 | **Screenshots** 24 | If applicable, add screenshots to help explain your problem. 25 | 26 | **Desktop (please complete the following information):** 27 | - OS: [e.g. iOS] 28 | - Browser [e.g. chrome, safari] 29 | - Version [e.g. 22] 30 | 31 | **Smartphone (please complete the following information):** 32 | - Device: [e.g. iPhone6] 33 | - OS: [e.g. iOS8.1] 34 | - Browser [e.g. stock browser, safari] 35 | - Version [e.g. 22] 36 | 37 | **Additional context** 38 | Add any other context about the problem here. 39 | -------------------------------------------------------------------------------- /convert_to_tfjs.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import os 3 | os.sys.path += ['expman'] 4 | import json 5 | from glob import glob 6 | import subprocess 7 | from tqdm import tqdm 8 | import expman 9 | 10 | 11 | def main(args): 12 | 13 | variants = ( 14 | ('' , []), 15 | # ('_qf16', ['--quantize_float16', '*']), 16 | # ('_qu16', ['--quantize_uint16' , '*']), 17 | # ('_qu8' , ['--quantize_uint8' , '*']), 18 | ) 19 | 20 | converted_models = [] 21 | 22 | exps = expman.gather(args.run).filter(args.filter) 23 | for exp_name, exp in tqdm(exps.items()): 24 | # ckpt = exp.path_to('best_model.h5') 25 | # ckpt = ckpt if os.path.exists(ckpt) else exp.path_to('last_model.h5') 26 | ckpt = exp.path_to('best_savedmodel/') 27 | 28 | for suffix, extra_args in variants: 29 | name = exp_name + suffix 30 | out = os.path.join(args.output, name) if args.output else exp.path_to(f'tfjs_graph{suffix}') 31 | 32 | if args.force or not os.path.exists(out): 33 | os.makedirs(out, exist_ok=True) 34 | cmd = ['tensorflowjs_converter', 35 | '--input_format', 'tf_saved_model', 36 | '--output_format', 'tfjs_graph_model'] + extra_args + [ckpt, out] 37 | subprocess.call(cmd) 38 | 39 | converted_models.append(name) 40 | 41 | js_output = 'models = ' + json.dumps(converted_models) 42 | if args.output: 43 | js_filename = os.path.join(args.output, 'models.js') 44 | with open(js_filename, 'w') as f: 45 | f.write(js_output) 46 | else: 47 | print(js_output) 48 | 49 | 50 | if __name__ == '__main__': 51 | parser = argparse.ArgumentParser(description='Convert runs to tfjs') 52 | parser.add_argument('-f', '--filter', default={}, type=expman.exp_filter) 53 | parser.add_argument('run', default='runs/') 54 | parser.add_argument('--output', help='output dir for models, defaults to run dir') 55 | parser.add_argument('--force', action='store_true', default=False) 56 | 57 | args = parser.parse_args() 58 | main(args) 59 | -------------------------------------------------------------------------------- /timings.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import os 3 | import time 4 | 5 | os.sys.path += ['expman'] 6 | import expman 7 | 8 | import numpy as np 9 | import pandas as pd 10 | import tensorflow as tf 11 | from tqdm import trange 12 | 13 | 14 | def main(args): 15 | is_run_dir = expman.is_exp_dir(args.model) 16 | if is_run_dir: 17 | exp = expman.from_dir(args.model) 18 | for model_file in ('best_savedmodel', 'best_model.h5', 'last_model.h5'): 19 | model_path = exp.path_to(model_file) 20 | if os.path.exists(model_path): 21 | break 22 | elif tf.saved_model.contains_saved_model(args.model): 23 | model_path = args.model 24 | else: 25 | print('Cannot find suitable model snapshot in {}'.format(args.model)) 26 | exit(1) 27 | 28 | model = tf.keras.models.load_model(model_path, compile=False, custom_objects={'tf': tf}) 29 | data = np.empty((1, args.rh, args.rw, 1), dtype=np.float32) 30 | 31 | # warm-up 32 | model.predict(data) 33 | 34 | start = time.time() 35 | for _ in trange(args.n): 36 | model.predict(data) 37 | end = time.time() 38 | elapsed = end - start 39 | 40 | throughput = elapsed / args.n 41 | fps = args.n / elapsed 42 | print(f'Total: {elapsed:g}s ({throughput * 1000} ms/img, {fps} fps)') 43 | 44 | timings = pd.Series({'elapsed': elapsed, 'throughput': throughput, 'fps': fps}) 45 | 46 | if is_run_dir and not args.output: 47 | timings_path = exp.path_to('timings.csv') 48 | timings.to_csv(timings_path) 49 | 50 | if args.output: 51 | timings.to_csv(args.output) 52 | 53 | 54 | if __name__ == '__main__': 55 | parser = argparse.ArgumentParser(description='Predict on test video') 56 | parser.add_argument('model', help='path to model or run dir') 57 | parser.add_argument('-n', type=int, default=100, help='number of predictions') 58 | parser.add_argument('-rh', type=int, default=128, help='RoI height (-1 for full height)') 59 | parser.add_argument('-rw', type=int, default=128, help='RoI width (-1 for full width)') 60 | parser.add_argument('-o', '--output', help='CSV output file') 61 | 62 | args = parser.parse_args() 63 | main(args) 64 | -------------------------------------------------------------------------------- /timings.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | Efficiency Tests 5 | 6 | 7 | 75 | 76 | 77 | 78 | 79 | 80 |
modeltiming
81 | 82 | -------------------------------------------------------------------------------- /predict.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | 3 | import imageio 4 | import numpy as np 5 | 6 | from tensorflow.keras.models import load_model 7 | from PIL import Image, ImageOps 8 | from tqdm import tqdm 9 | from utils import draw_predictions, compute_metrics 10 | 11 | 12 | def main(args): 13 | video = imageio.get_reader(args.video) 14 | n_frames = video.count_frames() 15 | fps = video.get_meta_data()['fps'] 16 | frame_w, frame_h = video.get_meta_data()['size'] 17 | 18 | model = load_model(args.model, compile=False) 19 | input_shape = model.input.shape[1:3] 20 | 21 | # default RoI 22 | if None in (args.rl, args.rt, args.rr, args.rb): 23 | side = min(frame_w, frame_h) 24 | args.rl = (frame_w - side) / 2 25 | args.rt = (frame_h - side) / 2 26 | args.rr = (frame_w + side) / 2 27 | args.rb = (frame_h + side) / 2 28 | 29 | crop = (args.rl, args.rt, args.rr, args.rb) 30 | 31 | def preprocess(frame): 32 | frame = Image.fromarray(frame) 33 | eye = frame.crop(crop) 34 | eye = ImageOps.grayscale(eye) 35 | eye = eye.resize(input_shape) 36 | return eye 37 | 38 | def predict(eye): 39 | eye = np.array(eye).astype(np.float32) / 255.0 40 | eye = eye[None, :, :, None] 41 | return model.predict(eye) 42 | 43 | out_video = imageio.get_writer(args.output_video, fps=fps) 44 | 45 | cropped = map(preprocess, video) 46 | frames_and_predictions = map(lambda x: (x, predict(x)), cropped) 47 | 48 | with open(args.output_csv, 'w') as out_csv: 49 | print('frame,pupil-area,pupil-x,pupil-y,eye,blink', file=out_csv) 50 | for idx, (frame, predictions) in enumerate(tqdm(frames_and_predictions, total=n_frames)): 51 | pupil_map, tags = predictions 52 | is_eye, is_blink = tags.squeeze() 53 | (pupil_y, pupil_x), pupil_area = compute_metrics(pupil_map, thr=args.thr, nms=True) 54 | 55 | row = [idx, pupil_area, pupil_x, pupil_y, is_eye, is_blink] 56 | row = ','.join(list(map(str, row))) 57 | print(row, file=out_csv) 58 | 59 | img = draw_predictions(frame, predictions, thr=args.thr) 60 | img = np.array(img) 61 | out_video.append_data(img) 62 | 63 | out_video.close() 64 | 65 | 66 | if __name__ == '__main__': 67 | parser = argparse.ArgumentParser(description='Predict on test video') 68 | parser.add_argument('model', type=str, help='Path to model') 69 | parser.add_argument('video', type=str, default='', help='Video file to process (use \'\' for webcam)') 70 | 71 | parser.add_argument('-t', '--thr', type=float, default=0.5, help='Map Threshold') 72 | parser.add_argument('-rl', type=int, help='RoI X coordinate of top left corner') 73 | parser.add_argument('-rt', type=int, help='RoI Y coordinate of top left corner') 74 | parser.add_argument('-rr', type=int, help='RoI X coordinate of right bottom corner') 75 | parser.add_argument('-rb', type=int, help='RoI Y coordinate of right bottom corner') 76 | 77 | parser.add_argument('-ov', '--output-video', default='predictions.mp4', help='Output video') 78 | parser.add_argument('-oc', '--output-csv', default='pupillometry.csv', help='Output CSV') 79 | 80 | args = parser.parse_args() 81 | main(args) 82 | -------------------------------------------------------------------------------- /models/unet.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | from tensorflow.keras import layers as L 3 | from tensorflow.keras.models import Model 4 | 5 | 6 | def build_model(x_shape, y_shape, config): 7 | inp = L.Input(shape=x_shape) 8 | x = inp 9 | 10 | n_stages = config.get('num_stages', 2) 11 | n_conv = config.get('num_conv', 1) 12 | n_filters = config.get('num_filters', 16) 13 | grow_mult = config.get('grow_factor', 1) 14 | up_activation = config.get('up_act', 'relu') 15 | conv_type = config.get('conv_type', 'conv') 16 | use_aspp = config.get('aspp', False) 17 | 18 | if up_activation == 'lrelu': 19 | up_activation = L.LeakyReLU() 20 | else: 21 | up_activation = L.Activation(up_activation) 22 | 23 | use_bn = 'bn-' not in conv_type 24 | 25 | conv = L.SeparableConv2D if 'sep-' in conv_type else L.Conv2D 26 | conv_common = dict(padding='same', use_bias=not use_bn) 27 | 28 | def conv_block(*args, **kwargs): 29 | def layer(x): 30 | if use_bn: 31 | act = kwargs.pop('activation', None) 32 | x = conv(*args, **kwargs)(x) 33 | x = L.BatchNormalization()(x) 34 | return L.Activation(act)(x) if act else x 35 | return conv(*args, **kwargs)(x) 36 | 37 | return layer 38 | 39 | intermediate = [] 40 | 41 | for _ in range(n_conv): 42 | x = conv_block(n_filters, 3, activation='relu', **conv_common)(x) 43 | 44 | # downsample path 45 | for i in range(n_stages): 46 | intermediate.append(x) 47 | n = round(n_filters * (grow_mult ** i)) 48 | x = conv_block(n, 3, 2, activation='relu', **conv_common)(x) 49 | for _ in range(n_conv - 1): 50 | x = conv_block(n, 3, activation='relu', **conv_common)(x) 51 | 52 | middle = L.GlobalAveragePooling2D()(x) 53 | 54 | if use_aspp: 55 | n = round(n / 4) 56 | x1 = conv_block(n, 1, dilation_rate=1, activation='relu', **conv_common)(x) 57 | x2 = conv_block(n, 3, dilation_rate=2, activation='relu', **conv_common)(x) 58 | x3 = conv_block(n, 3, dilation_rate=4, activation='relu', **conv_common)(x) 59 | x4 = conv_block(n, 3, dilation_rate=6, activation='relu', **conv_common)(x) 60 | 61 | # global feature 62 | xg = L.Reshape((1, 1, -1))(middle) 63 | xg = conv_block(n, 1, activation='relu', **conv_common)(xg) 64 | feature_tiling = tf.pad(tf.shape(x)[1:3], tf.constant([[1, 1]]), constant_values=1) 65 | xg = tf.tile(xg, feature_tiling) 66 | 67 | x = tf.concat([x1, x2, x3, x4, xg], axis=-1) 68 | 69 | # upsample path 70 | for i in range(n_stages - 1, -1, -1): 71 | x = L.UpSampling2D(size=2, interpolation='bilinear')(x) 72 | x = L.Concatenate()([x, intermediate.pop()]) 73 | n = round(n_filters * (grow_mult ** i)) 74 | for _ in range(n_conv): 75 | x = conv_block(n, 3, **conv_common)(x) 76 | x = up_activation(x) 77 | 78 | # segmentation mask 79 | out_mask = conv(y_shape[-1], 3, activation='sigmoid', padding='same', name='mask')(x) 80 | # metadata tags (is_eye and is_blink) 81 | out_tags = L.Dense(2, activation='sigmoid', name='tags')(middle) 82 | 83 | return Model(inp, [out_mask, out_tags]) 84 | 85 | 86 | if __name__ == '__main__': 87 | shape = (128, 128, 1) 88 | model = build_model(shape, shape, {'aspp': True}) 89 | model.summary() -------------------------------------------------------------------------------- /models/deeplab.py: -------------------------------------------------------------------------------- 1 | import sys 2 | sys.path += ['models/deeplab'] 3 | 4 | import tensorflow as tf 5 | 6 | from tensorflow.keras import backend as K 7 | from tensorflow.keras import layers as L 8 | from tensorflow.keras.models import Model, Sequential 9 | 10 | from deeplabv3p.models.deeplabv3p_resnet50 import Deeplabv3pResNet50 11 | from deeplabv3p.models.deeplabv3p_mobilenetv3 import Deeplabv3pMobileNetV3Small, Deeplabv3pLiteMobileNetV3Small, Deeplabv3pMobileNetV3Large, Deeplabv3pLiteMobileNetV3Large 12 | from deeplabv3p.models.deeplabv3p_xception import Deeplabv3pXception 13 | from deeplabv3p.models.deeplabv3p_peleenet import Deeplabv3pPeleeNet, Deeplabv3pLitePeleeNet 14 | 15 | AVAILABLE_BACKBONES = { 16 | 'resnet50': Deeplabv3pResNet50, 17 | 'xception': Deeplabv3pXception, 18 | 'mobilenetv3-large': Deeplabv3pMobileNetV3Large, 19 | 'lite-mobilenetv3-large': Deeplabv3pLiteMobileNetV3Large, 20 | 'mobilenetv3-small': Deeplabv3pMobileNetV3Small, 21 | 'lite-mobilenetv3-small': Deeplabv3pLiteMobileNetV3Small, 22 | 'peleenet': Deeplabv3pPeleeNet, 23 | 'lite-peleenet': Deeplabv3pLitePeleeNet, 24 | } 25 | 26 | AVAILABLE_PRETRAINED_WEIGHTS = { 27 | 'resnet50': 'imagenet', 28 | 'xception': None, # 'pascalvoc', # needs fix in upstream 29 | 'mobilenetv3-large': 'imagenet', 30 | 'lite-mobilenetv3-large': 'imagenet', 31 | 'mobilenetv3-small': 'imagenet', 32 | 'lite-mobilenetv3-small': 'imagenet', 33 | 'peleenet': 'imagenet', 34 | 'lite-peleenet': 'imagenet', 35 | } 36 | 37 | def build_model(input_shape, output_shape, config): 38 | 39 | assert input_shape[:2] == output_shape[:2], "Only same input-output HW shapes are supported." 40 | num_classes = output_shape[2] 41 | 42 | # backbone pretends RGB images to use pretrained weights 43 | needs_rgb_conversion = input_shape[2] != 3 44 | backbone_input_shape = (input_shape[:2] + (3,)) if needs_rgb_conversion else input_shape 45 | backbone_name = config.get('backbone', 'resnet50') 46 | weights = config.get('weights', AVAILABLE_PRETRAINED_WEIGHTS[backbone_name]) 47 | backbone_fn = AVAILABLE_BACKBONES[backbone_name] 48 | backbone, backbone_len = backbone_fn(input_shape=backbone_input_shape, num_classes=num_classes, weights=weights, OS=8) 49 | 50 | # segmentation mask 51 | out_mask = backbone.get_layer('pred_resize').output 52 | out_mask = L.Activation('sigmoid', name='mask')(out_mask) 53 | 54 | # metadata tags (is_eye and is_blink) 55 | middle = backbone.get_layer('image_pooling').output 56 | middle = L.Flatten()(middle) 57 | out_tags = L.Dense(2, activation='sigmoid', name='tags')(middle) 58 | 59 | model = Model(inputs=backbone.input, outputs=[out_mask, out_tags]) 60 | 61 | if needs_rgb_conversion: 62 | gray_input = L.Input(shape=input_shape) 63 | rgb_input = L.Lambda(lambda x: K.tile(x, (1, 1, 1, 3)) , name='gray2rgb')(gray_input) # we assume BHWC 64 | out_mask, out_tags = model(rgb_input) 65 | 66 | # rename outputs 67 | out_mask = L.Lambda(lambda x: x, name='mask')(out_mask) 68 | out_tags = L.Lambda(lambda x: x, name='tags')(out_tags) 69 | model = Model(inputs=gray_input, outputs=[out_mask, out_tags]) 70 | 71 | return model 72 | 73 | 74 | if __name__ == "__main__": 75 | shape = (128, 128, 1) 76 | model = build_model(shape, shape, {'weights': None})#, 'backbone': 'lite-mobilenetv3-small'}) 77 | model.summary() 78 | import pdb; pdb.set_trace() 79 | -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import matplotlib.pyplot as plt 3 | 4 | from PIL import Image, ImageDraw 5 | from scipy.ndimage import center_of_mass, label, sum as area 6 | 7 | 8 | def nms_on_area(x, s): # x is a binary image, s is a structuring element 9 | labels, num_labels = label(x, structure=s) # find connected components 10 | if num_labels > 1: 11 | indexes = np.arange(1, num_labels + 1) 12 | areas = area(x, labels, indexes) # compute area for each connected components 13 | 14 | biggest = max(zip(areas, indexes))[1] # get index of largest component 15 | x[labels != biggest] = 0 # discard other components 16 | 17 | return x 18 | 19 | 20 | def compute_metrics(p, thr=None, nms=False): 21 | p = p.squeeze() 22 | 23 | if thr: 24 | p = p > thr 25 | if nms: # perform non-maximum suppression: keep only largest area 26 | s = np.ones((3, 3)) # connectivity structure 27 | p = nms_on_area(p, s) 28 | 29 | center = center_of_mass(p) 30 | area = p.sum() 31 | return center, area 32 | 33 | 34 | def visualizable(x, y, alpha=(.5, .5), thr=0): 35 | xx = np.tile(x, (3,)) # Gray -> RGB: repeat channels 3 times 36 | yy = (y, ) + (np.zeros_like(x),) * (3 - y.shape[-1]) 37 | yy = np.concatenate(yy, axis=-1) # add a zero channels to pad to RGB 38 | mask = yy.max(axis=-1, keepdims=True) > thr # blend only where a prediction is present 39 | # mask = mask[:, :, None] 40 | return np.where(mask, alpha[0] * xx + alpha[1] * yy, xx) 41 | 42 | 43 | def draw_predictions(image, predictions, thr=None): 44 | x = image.convert('RGBA') 45 | 46 | maps, tags = predictions 47 | maps = maps[0] if maps.ndim == 4 else maps 48 | eye, blink = tags.squeeze() 49 | alpha = maps.max(axis=-1, keepdims=True) 50 | alpha = alpha > thr if thr is not None else alpha 51 | 52 | n_pad = 3 - maps.shape[-1] 53 | zero_channels = np.zeros(image.size + (n_pad,)) 54 | y = np.concatenate((maps, zero_channels, alpha), axis=-1) # add pad and masked alpha channel 55 | y = (y * 255).astype(np.uint8) 56 | y = Image.fromarray(y).convert('RGBA') 57 | 58 | preview = Image.alpha_composite(x, y) 59 | draw = ImageDraw.Draw(preview) 60 | draw.text((5, 5), 'E: {: >3.1%} B:{: >3.1%}'.format(eye, blink), fill=(0, 0, 255)) 61 | # draw.text((5, image.height - 5), ''.format(blink), fill=(255, 0, 0)) 62 | 63 | return preview 64 | 65 | 66 | def visualize(x, y, out=None, thr=0, n_cols=4, width=20): 67 | n_rows = len(x) // n_cols 68 | fig, axes = plt.subplots(n_rows, n_cols, figsize=(width, width * n_rows // n_cols)) 69 | y_masks, y_tags = y 70 | 71 | axes = axes.flatten() if isinstance(axes, np.ndarray) else (axes,) 72 | 73 | for xi, yi_mask, yi_tags, ax in zip(x, y_masks, y_tags, axes): 74 | i = visualizable(xi, yi_mask, thr=thr) 75 | ax.imshow(i, cmap=plt.cm.gray) 76 | ax.grid(False) 77 | if len(yi_tags) == 2: 78 | title = 'E: {:.1%} - B: {:.1%}' 79 | elif len(yi_tags) == 4: 80 | title = 'pE: {:.1%} - pB: {:.1%}\ntE: {:.1%} - tB: {:.1%}' 81 | 82 | ax.text(x=0.5, y=-0.02, s=title.format(*yi_tags), transform=ax.transAxes, 83 | ha='center', va='top', 84 | fontsize=width * 4 / 5, fontfamily='monospace') 85 | ax.set_axis_off() 86 | 87 | if out: 88 | plt.savefig(out, bbox_inches='tight') 89 | plt.close() 90 | -------------------------------------------------------------------------------- /matlab/README.md: -------------------------------------------------------------------------------- 1 | # MEYE pupillometry on MATLAB 2 | 3 | > Try MEYE on a standalone [Web-App](https://www.pupillometry.it/) 4 | 5 | > Learn more on the original [MEYE repo](https://github.com/fabiocarrara/meye) 6 | 7 | > Label your own dataset with [pLabeler](https://github.com/LeonardoLupori/pLabeler) 8 | 9 | Starting from MATLAB version 2021b, MEYE is also available for use on MATLAB! 10 | 11 | Here's a brief tutorial on how to use it in you own experiments. 12 | 13 | ## What do you need? 14 | 15 | - [MATLAB 2021b](https://it.mathworks.com/products/matlab.html) or later 16 | - [MATLAB Image Processing Toolbox](https://it.mathworks.com/products/image.html) 17 | - [MATLAB Deep Learning Toolbox](https://it.mathworks.com/products/deep-learning.html) 18 | An additional _support package_ of this toolbox has to be downloaded manually from the Add-On explorer in MATLAB: 19 | - _Deep Learning Toolbox™ Converter for ONNX Model Format_ 20 | ![image](https://user-images.githubusercontent.com/39329654/152327789-dde0af9b-d531-40be-b1a0-5ba17c508a13.png) 21 | - A MEYE model in [ONNX](https://onnx.ai/) format. You can download our latest model [here](https://github.com/fabiocarrara/meye/releases). 22 | ![onnxModel](https://user-images.githubusercontent.com/39329654/152552616-1b800398-5794-4f51-b4ed-2e3339cb2d0d.png) 23 | 24 | 25 | ## Quick start! 26 | 27 | ```matlab 28 | % Create an instance of Meye 29 | meye = Meye('path/to/model.onnx'); 30 | 31 | % Example 1 32 | % Make predictions on a single Image 33 | % 34 | % Load an image for which you want to predict the pupil 35 | img = imread('path/to/img.tif'); 36 | % Make a prediction on a frame 37 | [pupil, isEye, isBlink] = meye.predictImage(img); 38 | 39 | % Example 2 40 | % Make predictions on a video file and preview the results 41 | % 42 | meye.predictMovie_Preview('path/to/video'); 43 | ``` 44 | 45 | ## Examples 46 | 47 | Inside the file [example.m](example.m) you can find 5 extensively commented examples of some use cases for MEYE on MATLAB. 48 | These examples require you to download example data from [here](https://drive.google.com/drive/folders/1BG6O5BEkwXkNKC_1XuB3H9wbx3DeNWwF?usp=sharing). To run the examples succesfully, make sure that the downloaded files are in the same folder as the `example.m` file. 49 | 50 | # Known issues 51 | 52 | ## Small issue with _Upsample_ layers 53 | When [importing](https://it.mathworks.com/help/deeplearning/ref/importonnxnetwork.html) a ONNX network, MATLAB tries to translate all the layers of the network from ONNX Operators to built-in MATLAB layers (see [here](https://it.mathworks.com/help/deeplearning/ref/importonnxnetwork.html#mw_dc6cd14c-e8d0-4370-af81-96626a888d9c)). 54 | This operation is not succesful for all the layers and MATLAB tries to overcome erros by automatically generating custom layers to replace the ones that it wasnt able to translate. These _custom_ layers are stored in a folder as MATLAB `.m` class files. 55 | We found a small bug in the way MATLAB translates `Upsample` layers while importing MEYE network. In particular, the custom generated layers perform the upsample with the `nearest` interpolation method, while it should be used the `linear` method for best results. 56 | For now, we solved this bug by automatically replacing the `nearest` method with the `linear` one in all the custom generated layers. This restores optimal performance with no additional computational costs, but it's a bit hacky. 57 | We hope that in future releases MATLAB's process of translation to its own built-in layers will be smoother and this trick will not be needed anymore. -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # [*mEye*](https://www.pupillometry.it): A Deep Learning Tool for Pupillometry 2 | 3 | > ⭐ MEYE is available on **MATLAB**! Check it out [here](matlab/README.md) 4 | 5 | > Check out [pupillometry.it](https://www.pupillometry.it) for a ready-to-use web-based mEye pupillometry tool! 6 | 7 | This branch provides the Python code to make predictions and train/finetune models. 8 | If you are interested in the code of the pupillometry web app, check out the `gh-pages` branch. 9 | 10 | ## Requirements 11 | You need a Python 3 environment with the following packages installed: 12 | 13 | - tensorflow >= 2.4 14 | - imageio, imageio-ffmpeg 15 | - scipy 16 | - tqdm 17 | 18 | If you want to train models, you also need 19 | 20 | - adabelief_tf >= 0.2.1 21 | - pandas 22 | - sklearn 23 | 24 | We provide a [Dockerfile](./Dockerfile) for building an image with docker. 25 | 26 | ## Make Predictions with Pretrained Models 27 | 28 | You can make predictions with pretrained models on pre-recorded videos or webcam streams. 29 | 30 | 1. Download the [pretrained model](https://github.com/fabiocarrara/meye/releases/download/v0.1.1/meye-2022-01-24.h5). If you want to use the [old model](https://github.com/fabiocarrara/meye/releases/download/v0.1/meye-segmentation_i128_s4_c1_f16_g1_a-relu.hdf5), check out version [`v0.1` of this branch](https://github.com/fabiocarrara/meye/tree/v0.1). See available models in [Releases](https://github.com/fabiocarrara/meye/releases). 31 | 2. Check out the `pupillometry-offline-videos.ipynb` notebook for a complete example of pupillometry data analysis. 32 | 3. In alternative, we provide also the `predict.py` script that implements the basic loop to make predictions on video streams. E.g.: 33 | 34 | - ```bash 35 | # input: webcam (default) 36 | # prediction roi: biggest central square crop (default) 37 | # outputs: predictions.mp4, predictions.csv (default) 38 | predict.py path/to/model 39 | ``` 40 | 41 | - ```bash 42 | # input: video file 43 | # prediction roi: left=80, top=80, right=208, bottom=208 44 | # outputs: video_with_predictions.mp4, pupil_metrics.csv 45 | predict.py path/to/model path/to/video.mp4 -rl 80 -rt 80 -rr 208 -rb 208 -ov video_with_predictions.mp4 -oc pupil_metrics.csv 46 | ``` 47 | - ```bash 48 | # check all parameters with 49 | predict.py -h 50 | ``` 51 | 52 | ## Training Models 53 | 54 | 1. Download our dataset ([NN_human_mouse_eyes.zip](https://doi.org/10.5281/zenodo.4488164), 246.4 MB) or prepare your dataset following our dataset's structure. 55 | > If you need to annotate your dataset, check out [pLabeler](https://github.com/LeonardoLupori/pLabeler), a MATLAB software for labeling pupil images. 56 | 57 | The dataset should be placed in `data/`. 58 | 59 | 2. If you are using a custom dataset, edit `train.py` to perform the train/validation/test split of your data. 60 | 61 | 3. Train with default parameters: 62 | ```bash 63 | python train.py -d data/ 64 | ``` 65 | 66 | - For a list of available parameters, run 67 | ```bash 68 | python train.py -h 69 | ``` 70 | 71 | ## MATLAB support 72 | Starting from MATLAB version 2021b, MEYE is also available for use on MATLAB! 73 | A fully functional class and a tutorial for its use is available [here](matlab/README.md)! 74 | 75 | 76 | ## References 77 | 78 | ### Dataset 79 | [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4488164.svg)](https://doi.org/10.5281/zenodo.4488164) 80 | 81 | If you use our dataset, please cite: 82 | 83 | @dataset{raffaele_mazziotti_2021_4488164, 84 | author = {Raffaele Mazziotti and Fabio Carrara and Aurelia Viglione and Lupori Leonardo and Lo Verde Luca and Benedetto Alessandro and Ricci Giulia and Sagona Giulia and Amato Giuseppe and Pizzorusso Tommaso}, 85 | title = {{Human and Mouse Eyes for Pupil Semantic Segmentation}}, 86 | month = feb, 87 | year = 2021, 88 | publisher = {Zenodo}, 89 | version = {1.0}, 90 | doi = {10.5281/zenodo.4488164}, 91 | url = {https://doi.org/10.5281/zenodo.4488164} 92 | } 93 | -------------------------------------------------------------------------------- /train_dl.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ MEye: Semantic Segmentation """ 3 | 4 | import argparse 5 | import os 6 | 7 | os.sys.path += ['expman', 'models/deeplab'] 8 | import matplotlib 9 | matplotlib.use('Agg') 10 | 11 | import matplotlib.pyplot as plt 12 | 13 | import math 14 | import numpy as np 15 | import pandas as pd 16 | import tensorflow as tf 17 | import tensorflowjs as tfjs 18 | from tensorflow.keras import backend as K 19 | from tensorflow.keras.models import load_model 20 | from tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler, CSVLogger 21 | from sklearn.model_selection import train_test_split 22 | from sklearn.metrics import classification_report, roc_curve, auc, precision_recall_curve, average_precision_score 23 | from adabelief_tf import AdaBeliefOptimizer 24 | from tqdm.keras import TqdmCallback 25 | from tqdm import tqdm 26 | from functools import partial 27 | 28 | from dataloader import get_loader, load_datasets 29 | from deeplabv3p.models.deeplabv3p_mobilenetv3 import hard_swish 30 | from models.deeplab import build_model, AVAILABLE_BACKBONES 31 | from utils import visualize 32 | from expman import Experiment 33 | 34 | import evaluate 35 | 36 | 37 | def main(args): 38 | exp = Experiment(args, ignore=('epochs', 'resume')) 39 | print(exp) 40 | 41 | np.random.seed(args.seed) 42 | tf.random.set_seed(args.seed) 43 | 44 | data = load_datasets(args.data) 45 | 46 | # TRAIN/VAL/TEST SPLIT 47 | if args.split == 'subjects': # by SUBJECTS 48 | val_subjects = (6, 9, 11, 13, 16, 28, 30, 48, 49) 49 | test_subjects = (3, 4, 19, 38, 45, 46, 51, 52) 50 | train_data = data[~data['sub'].isin(val_subjects + test_subjects)] 51 | val_data = data[data['sub'].isin(val_subjects)] 52 | test_data = data[data['sub'].isin(test_subjects)] 53 | 54 | elif args.split == 'random': # 70-20-10 % 55 | train_data, valtest_data = train_test_split(data, test_size=.3, shuffle=True) 56 | val_data, test_data = train_test_split(valtest_data, test_size=.33) 57 | 58 | lengths = map(len, (data, train_data, val_data, test_data)) 59 | print("Total: {} - Train / Val / Test: {} / {} / {}".format(*lengths)) 60 | 61 | x_shape = (args.resolution, args.resolution, 1) 62 | y_shape = (args.resolution, args.resolution, 1) 63 | 64 | train_gen, _ = get_loader(train_data, batch_size=args.batch_size, shuffle=True, augment=True, x_shape=x_shape) 65 | val_gen, val_categories = get_loader(val_data, batch_size=args.batch_size, x_shape=x_shape) 66 | # test_gen, test_categories = get_loader(test_data, batch_size=1, x_shape=x_shape) 67 | 68 | log = exp.path_to('log.csv') 69 | 70 | # weights_only checkpoints 71 | best_weights_path = exp.path_to('best_weights.h5') 72 | best_mask_weights_path = exp.path_to('best_weights_mask.h5') 73 | 74 | # whole model checkpoints 75 | best_ckpt_path = exp.path_to('best_model.h5') 76 | last_ckpt_path = exp.path_to('last_model.h5') 77 | 78 | if args.resume and os.path.exists(last_ckpt_path): 79 | custom_objects={'AdaBeliefOptimizer': AdaBeliefOptimizer, 'iou_coef': evaluate.iou_coef, 'dice_coef': evaluate.dice_coef, 'hard_swish': hard_swish} 80 | model = tf.keras.models.load_model(last_ckpt_path, custom_objects=custom_objects) 81 | optimizer = model.optimizer 82 | initial_epoch = len(pd.read_csv(log)) 83 | else: 84 | config = vars(args) 85 | model = build_model(x_shape, y_shape, config) 86 | optimizer = AdaBeliefOptimizer(learning_rate=args.lr, print_change_log=False) 87 | initial_epoch = 0 88 | 89 | model.compile(optimizer=optimizer, 90 | loss='binary_crossentropy', 91 | metrics={'mask': [evaluate.iou_coef, evaluate.dice_coef], 92 | 'tags': 'binary_accuracy'}) 93 | 94 | model_stopped_file = exp.path_to('early_stopped.txt') 95 | need_training = not os.path.exists(model_stopped_file) and initial_epoch < args.epochs 96 | if need_training: 97 | best_checkpointer = ModelCheckpoint(best_weights_path, monitor='val_loss', save_best_only=True, save_weights_only=True) 98 | best_mask_checkpointer = ModelCheckpoint(best_mask_weights_path, monitor='val_mask_dice_coef', mode='max', save_best_only=True, save_weights_only=True) 99 | last_checkpointer = ModelCheckpoint(last_ckpt_path, save_best_only=False, save_weights_only=False) 100 | logger = CSVLogger(log, append=args.resume) 101 | progress = TqdmCallback(verbose=1, initial=initial_epoch, dynamic_ncols=True) 102 | early_stop = tf.keras.callbacks.EarlyStopping(monitor='val_mask_dice_coef', mode='max', patience=100) 103 | 104 | callbacks = [best_checkpointer, best_mask_checkpointer, last_checkpointer, logger, progress, early_stop] 105 | 106 | model.fit(train_gen, 107 | epochs=args.epochs, 108 | callbacks=callbacks, 109 | initial_epoch=initial_epoch, 110 | steps_per_epoch=len(train_gen), 111 | validation_data=val_gen, 112 | validation_steps=len(val_gen), 113 | verbose=False) 114 | 115 | if model.stop_training: 116 | open(model_stopped_file, 'w').close() 117 | 118 | tf.keras.models.save_model(model, best_ckpt_path, include_optimizer=False) 119 | 120 | # evaluation on test set 121 | evaluate.evaluate(exp, force=need_training) 122 | 123 | # save best snapshot in SavedModel format 124 | model.load_weights(best_mask_weights_path) 125 | best_savedmodel_path = exp.path_to('best_savedmodel') 126 | model.save(best_savedmodel_path, save_traces=True) 127 | 128 | # export to tfjs (Layers model) 129 | tfjs_model_dir = exp.path_to('tfjs') 130 | tfjs.converters.save_keras_model(model, tfjs_model_dir) 131 | 132 | 133 | if __name__ == '__main__': 134 | default_data = ['data/NN_human_mouse_eyes'] 135 | 136 | parser = argparse.ArgumentParser(description='Train DeepLab models') 137 | # data params 138 | parser.add_argument('-d', '--data', nargs='+', default=default_data, help='Data directory (may be multiple)') 139 | parser.add_argument('--split', default='random', choices=('random', 'subjects'), help='How to split data') 140 | parser.add_argument('-r', '--resolution', type=int, default=128, help='Input image resolution') 141 | 142 | # model params 143 | parser.add_argument('-a', '--backbone', default='resnet50', choices=AVAILABLE_BACKBONES, help='Backbone architecture') 144 | 145 | # train params 146 | parser.add_argument('--lr', type=float, default=0.001, help='learning rate') 147 | parser.add_argument('-b', '--batch-size', type=int, default=32, help='Batch size') 148 | parser.add_argument('-e', '--epochs', type=int, default=500, help='Number of training epochs') 149 | parser.add_argument('-s', '--seed', type=int, default=23, help='Random seed') 150 | parser.add_argument('--resume', default=False, action='store_true', help='Resume training') 151 | 152 | args = parser.parse_args() 153 | main(args) 154 | -------------------------------------------------------------------------------- /matlab/example.m: -------------------------------------------------------------------------------- 1 | %% Download all the example material 2 | % 3 | % 1 - Download the latest MEYE model in ONNX format 4 | % ------------------------------------------------------------------------- 5 | % Download the .onnx file from the assets here: 6 | % https://github.com/fabiocarrara/meye/releases 7 | 8 | % EXAMPLE data can be found in this folder: 9 | % https://drive.google.com/drive/folders/1BG6O5BEkwXkNKC_1XuB3H9wbx3DeNWwF?usp=sharing 10 | % 11 | % 2 - Download an example image of a simple mouse eye from: 12 | % https://drive.google.com/file/d/1hcWcC1cAmzY4r-SIWDIgUY0-gpbmetUL/view?usp=sharing 13 | % 14 | % 3 - Download an example of a large image here: 15 | % https://drive.google.com/file/d/16QixvUMtojqfrcy4WXlYJ7CP3K8vrz_C/view?usp=sharing 16 | % 17 | % 4 - Download an example pupillometry video here: 18 | % https://drive.google.com/file/d/1TYj80dzIR1ZjpEvfefH_akhbUjwpvJta/view?usp=sharing 19 | 20 | 21 | %% EXAMPLE 1 22 | % ------------------------------------------------------------------------- 23 | % Predict the pupil from a simple image of an eye 24 | 25 | % Clean up the workspace 26 | clearvars, clc 27 | 28 | % Change these values according to the filenames of the MEYE model and the 29 | % simple pupil image 30 | MODEL_NAME = 'meye_20220124.onnx'; 31 | IMAGE_NAME = 'pupilImage_simple.png'; 32 | 33 | 34 | % Initialize a MEYE object 35 | meye = Meye(MODEL_NAME); 36 | 37 | % Load the simple image 38 | img = imread(IMAGE_NAME); 39 | 40 | % Predict a single image 41 | [pupilMask, eyeProb, blinkProb] = meye.predictImage(img); 42 | 43 | % Plot the results of the prediction 44 | subplot(1,3,1) 45 | imshow(img) 46 | title('Original Image') 47 | 48 | subplot(1,3,2) 49 | imagesc(pupilMask) 50 | title(sprintf('Prediction (Eye:%.2f%% - Blink:%.2f%%)',eyeProb*100,blinkProb*100)) 51 | axis off, axis image 52 | 53 | subplot(1,3,3) 54 | imshowpair(img, pupilMask) 55 | title('Merge') 56 | 57 | 58 | %% EXAMPLE 2 59 | % ------------------------------------------------------------------------- 60 | % Binarize the pupil prediction and get the pupil size in pixels 61 | 62 | % Clean up the workspace 63 | clearvars, close all, clc 64 | 65 | % Change these values according to the filenames of the MEYE model and the 66 | % simple pupil image 67 | MODEL_NAME = 'meye_20220124.onnx'; 68 | IMAGE_NAME = 'pupilImage_simple.png'; 69 | 70 | 71 | % Initialize a MEYE object 72 | meye = Meye(MODEL_NAME); 73 | 74 | % Load the simple image 75 | img = imread(IMAGE_NAME); 76 | 77 | % Predict a single image 78 | % You can automatically binarize the prediction by passing the "threshold" 79 | % optional argument. This number can be between 0 and 1. If omitted, the 80 | % function returns a raw probability map instead of a binarized image 81 | pupilBinaryMask = meye.predictImage(img, 'threshold', 0.4); 82 | 83 | imshowpair(img, pupilBinaryMask) 84 | title(sprintf('Pupil Size: %u px', sum(pupilBinaryMask,'all'))) 85 | 86 | 87 | %% EXAMPLE 3 88 | % ------------------------------------------------------------------------- 89 | % Predict the pupil on a large image where the eye is a small portion of 90 | % the image 91 | 92 | % Clean up the workspace 93 | clearvars, close all, clc 94 | 95 | % Change these values according to the filenames of the MEYE model and the 96 | % simple pupil image 97 | MODEL_NAME = 'meye_20220124.onnx'; 98 | IMAGE_NAME = 'pupilImage_large.png'; 99 | 100 | 101 | % Initialize a MEYE object 102 | meye = Meye(MODEL_NAME); 103 | 104 | % Load the simple image 105 | img = imread(IMAGE_NAME); 106 | 107 | % Predict the image 108 | pupilMask = meye.predictImage(img); 109 | 110 | % As you can see from this image, the prediction is not perfect. This is 111 | % because MEYE was trained on images that tightly contained the eye. 112 | subplot(1,2,1) 113 | imshowpair(img, pupilMask) 114 | title('Tomal Image prediction (low-quality)') 115 | 116 | % In order to solve this issue it is possible to restrict the prediction to 117 | % a rectangular Region of Interest (ROI) in the image. This is done simply 118 | % by passing the optional argument "roiPos" to the predictImage function. 119 | % The roiPos is a 4-elements vector containing X,Y, width, height of a 120 | % rectangular shape. Note that X and Y are the coordinates of the top left 121 | % corner of the ROI 122 | 123 | ROI = [90,90,200,200]; 124 | pupilMask = meye.predictImage(img, 'roiPos', ROI); 125 | 126 | % Plot the results with the ROI and see the difference between the 2 methods 127 | subplot(1,2,2) 128 | imshowpair(img, pupilMask) 129 | rectangle('Position',ROI, 'LineStyle','-.','EdgeColor',[1,0,0]) 130 | title('ROI prediction (high quality)') 131 | linkaxes 132 | set(gcf,'Position',[300,600,1000,320]) 133 | 134 | 135 | %% EXAMPLE 4 136 | % ------------------------------------------------------------------------- 137 | % Show a preview of the prediction of an entire pupillometry video. 138 | % 139 | % As you saw you can adjust a few parameters for the prediction. 140 | % If you want to get a quick preview of how your pre-recorded video will be 141 | % processed, you can use the method predictMovie_Preview. 142 | % Here you can play around with different ROI positions and threshold 143 | % values and see what are the results before analyzing the whole video. 144 | 145 | % Clean up the workspace 146 | clearvars, close all, clc 147 | 148 | % Change these values according to the filenames of the MEYE model and the 149 | % simple pupil image 150 | MODEL_NAME = 'meye_20220124.onnx'; 151 | VIDEO_NAME = 'mouse_example.mp4'; 152 | 153 | % Initialize a MEYE object 154 | meye = Meye(MODEL_NAME); 155 | 156 | % Try to play around moving or resizing the ROI to see how the performances change 157 | ROI = [70, 60, 200, 200]; 158 | 159 | % Change the threshold value to binarize the pupil prediction. 160 | % Use [] to see the raw probability map. Use a number in the range [0:1] to binarize it 161 | threshold = 0.4; 162 | 163 | meye.predictMovie_Preview(VIDEO_NAME,"roiPos", ROI,"threshold",threshold); 164 | 165 | 166 | 167 | %% EXAMPLE 5 168 | % Predict the entire video and get the results table 169 | 170 | % Clean up the workspace 171 | clearvars, close all, clc 172 | 173 | % Change these values according to the filenames of the MEYE model and the 174 | % simple pupil image 175 | MODEL_NAME = 'meye_20220124.onnx'; 176 | VIDEO_NAME = 'mouse_example.mp4'; 177 | 178 | % Initialize a MEYE object 179 | meye = Meye(MODEL_NAME); 180 | 181 | % Try to play around moving or resizing the ROI to see how the performances change 182 | ROI = [70, 60, 200, 200]; 183 | 184 | % Change the threshold value to binarize the pupil prediction. 185 | % Use [] to see the raw probability map. Use a number in the range [0:1] to binarize it 186 | threshold = 0.4; 187 | 188 | % Predict the whole movie and save results in a table 189 | T = meye.predictMovie(VIDEO_NAME, "roiPos", ROI, "threshold", threshold); 190 | 191 | % Show some of the values in the table 192 | disp(head(T)) 193 | 194 | % Plot some of the results 195 | subplot 311 196 | plot(T.frameTime,T.isEye, 'LineWidth', 2) 197 | title('Eye Probability') 198 | ylabel('Probability'), 199 | xlim([T.frameTime(1) T.frameTime(end)]) 200 | 201 | subplot 312 202 | plot(T.frameTime,T.isBlink, 'LineWidth', 2) 203 | title('Blink Probability') 204 | ylabel('Probability') 205 | xlim([T.frameTime(1) T.frameTime(end)]) 206 | 207 | subplot 313 208 | plot(T.frameTime,T.pupilArea, 'LineWidth', 2) 209 | title('Pupil Size') 210 | xlabel('Time (s)'), ylabel('Pupil Area (px)') 211 | xlim([T.frameTime(1) T.frameTime(end)]) 212 | -------------------------------------------------------------------------------- /dataloader.py: -------------------------------------------------------------------------------- 1 | import os 2 | import math 3 | import pandas as pd 4 | import tensorflow as tf 5 | import tensorflow_addons as tfa 6 | 7 | from functools import partial 8 | 9 | # find pupil center 10 | def _get_pupil_position(pmap, datum, x_shape): 11 | total_mass = tf.reduce_sum(pmap) 12 | if total_mass > 0: 13 | shape = tf.shape(pmap) 14 | h, w = shape[0], shape[1] 15 | ii, jj = tf.meshgrid(tf.range(h), tf.range(w), indexing='ij') 16 | y = tf.reduce_sum(tf.cast(ii, 'float32') * pmap) / total_mass 17 | x = tf.reduce_sum(tf.cast(jj, 'float32') * pmap) / total_mass 18 | return tf.stack((y, x)) 19 | 20 | if 'roi_x' in datum and 'roi_y' in datum and 'roi_w' in datum: 21 | roi_x = tf.cast(datum['roi_x'], 'float32') 22 | roi_y = tf.cast(datum['roi_y'], 'float32') 23 | half = tf.cast(datum['roi_w'] / 2, 'float32') 24 | result = tf.stack((roi_y + half, roi_x + half)) 25 | else: # fallback to center of the image 26 | result = tf.cast(tf.stack((x_shape[0] / 2, x_shape[1] / 2)), dtype='float32') 27 | 28 | return result 29 | 30 | 31 | @tf.function 32 | def load_datum(datum, x_shape=(128, 128, 1), augment=False): 33 | 34 | x = tf.io.read_file(datum['filename']) 35 | y = tf.io.read_file(datum['target']) 36 | 37 | # HWC [0,1] float32 38 | x = tf.io.decode_image(x, channels=1, dtype='float32', expand_animations=False) 39 | y = tf.io.decode_image(y, dtype='float32', expand_animations=False) 40 | 41 | shape = tf.cast(tf.shape(x), 'float32') 42 | h, w = shape[0], shape[1] 43 | half_wh = tf.stack((w, h)) / 2 44 | 45 | pupil_map = y[:, :, 0] # R-channel is the pupil map 46 | pupil_area = tf.reduce_sum(pupil_map) 47 | 48 | pupil_pos_yx = _get_pupil_position(pupil_map, datum, x_shape) 49 | 50 | if not augment: 51 | s = tf.minimum(tf.cast(x_shape[0], 'float32'), tf.minimum(h, w)) 52 | pupil_pos_xy = pupil_pos_yx[::-1] 53 | pupil_new_pos_xy = tf.constant([.5, .5]) * s 54 | 55 | crop_xy = pupil_pos_xy - pupil_new_pos_xy # crop origin 56 | # find the feasibility region for the top-left corner of a square crop of size s 57 | crop_min, crop_max = tf.constant((0., 0.)), tf.stack((w - s, h - s)) 58 | crop_xy = tf.clip_by_value(crop_xy, crop_min, crop_max) 59 | 60 | p = tfa.image.translations_to_projective_transforms(-crop_xy) 61 | 62 | else: # data augmentation 63 | # random rotation: pick random angle 64 | theta = tf.random.uniform([], 0, math.pi / 2) 65 | cos_t = tf.math.cos(theta) 66 | sin_t = tf.math.sin(theta) 67 | 68 | # random scale: pick random size of crop around the pupil 69 | # (constrained by the rotation angle and the original image size) 70 | min_s = 15 71 | max_s = tf.math.floor(tf.minimum(w, h) / (sin_t + cos_t)) 72 | s = tf.random.normal([], mean=128, stddev=50) 73 | s = tf.clip_by_value(s, min_s, max_s) 74 | 75 | # find the feasibility region for the top-left corner of a square crop of size s 76 | crop_lt = tf.stack((s * sin_t, 0)) 77 | crop_rb = tf.stack((w - s * cos_t, h - s * (sin_t + cos_t))) 78 | 79 | # pick a new random position (in the crop space) in which to place the pupil center 80 | std = 0.2 if (datum['blink'] == 1) else 0.5 # make sure blinking eyes are shown 81 | pupil_new_pos_yx = tf.random.normal((2,), mean=0.5, stddev=std) * s 82 | 83 | pupil_pos_y, pupil_pos_x = pupil_pos_yx[0], pupil_pos_yx[1] 84 | pupil_new_pos_y, pupil_new_pos_x = pupil_new_pos_yx[0], pupil_new_pos_yx[1] 85 | 86 | # crop origin (works.. but xy seem swapped, to double check) 87 | crop_xy = tf.stack(( 88 | pupil_pos_y + pupil_new_pos_x * sin_t - pupil_new_pos_y * cos_t, 89 | pupil_pos_x - pupil_new_pos_x * cos_t - pupil_new_pos_y * sin_t 90 | )) 91 | 92 | # ensure crop is inside image 93 | crop_xy = tf.clip_by_value(crop_xy, crop_lt, crop_rb) 94 | 95 | # compose transformation 96 | tr1 = tfa.image.translations_to_projective_transforms(half_wh - crop_xy) 97 | rot = tfa.image.angles_to_projective_transforms(theta, h, w) 98 | tr2 = tfa.image.translations_to_projective_transforms(-half_wh) 99 | p = tfa.image.compose_transforms((tr1, rot, tr2)) 100 | 101 | x = tfa.image.transform(x, p, output_shape=(s, s)) 102 | y = tfa.image.transform(y, p, output_shape=(s, s)) 103 | 104 | # compute how much pupil is left in the image 105 | new_pupil_map = y[:, :, 0] 106 | new_pupil_area = tf.reduce_sum(new_pupil_map) 107 | eye = (new_pupil_area / pupil_area) if pupil_area > 0 else 0. 108 | 109 | datum_eye = tf.cast(datum['eye'], 'float32') 110 | datum_blink = tf.cast(datum['blink'], 'float32') 111 | if datum_eye == 0: # set noblink if there is no eye 112 | datum_blink = 0. 113 | 114 | if (datum_eye == 1) & (datum_blink == 0): # update eye percentage due to crop (if no blink) 115 | datum_eye = eye 116 | 117 | if tf.math.reduce_any(tf.shape(x)[:2] != x_shape[:2]): 118 | x = tf.image.resize(x, x_shape[:2]) 119 | y = tf.image.resize(y, x_shape[:2]) 120 | 121 | if augment: 122 | # random flip 123 | if tf.random.uniform([]) < 0.5: 124 | x = tf.image.flip_left_right(x) 125 | y = tf.image.flip_left_right(y) 126 | 127 | if tf.random.uniform([]) < 0.5: 128 | x = tf.image.flip_up_down(x) 129 | y = tf.image.flip_up_down(y) 130 | 131 | # random brightness, contrast 132 | contrast_factor = tf.random.normal([], mean=1.0, stddev=0.4) 133 | 134 | x = tf.image.random_brightness(x, 0.2) 135 | x = tf.image.adjust_contrast(x, contrast_factor) 136 | x = tf.clip_by_value(x, 0, 1) 137 | 138 | y = y[:, :, :1] 139 | y2 = tf.stack((datum_eye, datum_blink)) 140 | 141 | return x, y, y2 142 | 143 | 144 | def get_loader(dataframe, batch_size=8, shuffle=False, **kwargs): 145 | categories = dataframe.exp.values 146 | 147 | dataset = tf.data.Dataset.from_tensor_slices(dict(dataframe)) 148 | 149 | if shuffle: 150 | dataset = dataset.shuffle(1000) 151 | 152 | dataset = dataset.map(partial(load_datum, **kwargs), num_parallel_calls=tf.data.AUTOTUNE, deterministic=not shuffle) 153 | dataset = dataset.batch(batch_size) 154 | 155 | # pack targets for keras 156 | def _pack_targets(*ins): 157 | inputs = ins[0] 158 | targets = {'mask': ins[1], 'tags': ins[2]} 159 | return [inputs, targets] 160 | 161 | dataset = dataset.map(_pack_targets, num_parallel_calls=tf.data.AUTOTUNE, deterministic=not shuffle) 162 | dataset = dataset.prefetch(tf.data.AUTOTUNE) 163 | return dataset, categories 164 | 165 | 166 | def load_datasets(dataset_dirs): 167 | 168 | def _load_and_prepare_annotations(dataset_dir): 169 | data = os.path.join(dataset_dir, 'annotation', 'annotations.csv') 170 | data = pd.read_csv(data) 171 | data['target'] = dataset_dir + '/annotation/png/' + data.filename.str.replace(r'jpe?g', 'png') 172 | data['filename'] = dataset_dir + '/fullFrames/' + data.filename 173 | return data 174 | 175 | dataset = pd.concat([_load_and_prepare_annotations(d) for d in dataset_dirs]) 176 | dataset['sub'] = dataset['sub'].astype(str) 177 | return dataset 178 | 179 | 180 | if __name__ == '__main__': 181 | dataset = load_datasets(['NN_human_mouse_eyes']) 182 | loader, categories = get_loader(dataset, batch_size=1, shiffle=False) 183 | 184 | for x, y in loader: 185 | print(x, y) 186 | break 187 | -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ MEye: Semantic Segmentation """ 3 | 4 | import argparse 5 | import os 6 | 7 | os.sys.path += ['expman'] 8 | import matplotlib 9 | matplotlib.use('Agg') 10 | 11 | import matplotlib.pyplot as plt 12 | 13 | import math 14 | import numpy as np 15 | import pandas as pd 16 | import tensorflow as tf 17 | import tensorflowjs as tfjs 18 | from tensorflow.keras import backend as K 19 | from tensorflow.keras.models import load_model 20 | from tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler, CSVLogger 21 | from sklearn.model_selection import train_test_split 22 | from sklearn.metrics import classification_report, roc_curve, auc, precision_recall_curve, average_precision_score 23 | from adabelief_tf import AdaBeliefOptimizer 24 | from tqdm.keras import TqdmCallback 25 | from tqdm import tqdm 26 | from functools import partial 27 | 28 | from dataloader import get_loader, load_datasets 29 | from models.unet import build_model 30 | from utils import visualize 31 | from expman import Experiment 32 | 33 | import evaluate 34 | 35 | 36 | def main(args): 37 | exp = Experiment(args, ignore=('epochs', 'resume')) 38 | print(exp) 39 | 40 | np.random.seed(args.seed) 41 | tf.random.set_seed(args.seed) 42 | 43 | data = load_datasets(args.data) 44 | 45 | # TRAIN/VAL/TEST SPLIT 46 | if args.split == 'subjects': # by SUBJECTS 47 | val_subjects = (6, 9, 11, 13, 16, 28, 30, 48, 49) 48 | test_subjects = (3, 4, 19, 38, 45, 46, 51, 52) 49 | train_data = data[~data['sub'].isin(val_subjects + test_subjects)] 50 | val_data = data[data['sub'].isin(val_subjects)] 51 | test_data = data[data['sub'].isin(test_subjects)] 52 | 53 | elif args.split == 'random': # 70-20-10 % 54 | train_data, valtest_data = train_test_split(data, test_size=.3, shuffle=True) 55 | val_data, test_data = train_test_split(valtest_data, test_size=.33) 56 | 57 | lengths = map(len, (data, train_data, val_data, test_data)) 58 | print("Total: {} - Train / Val / Test: {} / {} / {}".format(*lengths)) 59 | 60 | x_shape = (args.resolution, args.resolution, 1) 61 | y_shape = (args.resolution, args.resolution, 1) 62 | 63 | train_gen, _ = get_loader(train_data, batch_size=args.batch_size, shuffle=True, augment=True, x_shape=x_shape) 64 | val_gen, val_categories = get_loader(val_data, batch_size=args.batch_size, x_shape=x_shape) 65 | # test_gen, test_categories = get_loader(test_data, batch_size=1, x_shape=x_shape) 66 | 67 | log = exp.path_to('log.csv') 68 | 69 | # weights_only checkpoints 70 | best_weights_path = exp.path_to('best_weights.h5') 71 | best_mask_weights_path = exp.path_to('best_weights_mask.h5') 72 | 73 | # whole model checkpoints 74 | best_ckpt_path = exp.path_to('best_model.h5') 75 | last_ckpt_path = exp.path_to('last_model.h5') 76 | 77 | if args.resume and os.path.exists(last_ckpt_path): 78 | custom_objects={'AdaBeliefOptimizer': AdaBeliefOptimizer, 'iou_coef': evaluate.iou_coef, 'dice_coef': evaluate.dice_coef} 79 | model = tf.keras.models.load_model(last_ckpt_path, custom_objects=custom_objects) 80 | optimizer = model.optimizer 81 | initial_epoch = len(pd.read_csv(log)) 82 | else: 83 | config = vars(args) 84 | model = build_model(x_shape, y_shape, config) 85 | optimizer = AdaBeliefOptimizer(learning_rate=args.lr, print_change_log=False) 86 | initial_epoch = 0 87 | 88 | model.compile(optimizer=optimizer, 89 | loss='binary_crossentropy', 90 | metrics={'mask': [evaluate.iou_coef, evaluate.dice_coef], 91 | 'tags': 'binary_accuracy'}) 92 | 93 | model_stopped_file = exp.path_to('early_stopped.txt') 94 | need_training = not os.path.exists(model_stopped_file) and initial_epoch < args.epochs 95 | if need_training: 96 | best_checkpointer = ModelCheckpoint(best_weights_path, monitor='val_loss', save_best_only=True, save_weights_only=True) 97 | best_mask_checkpointer = ModelCheckpoint(best_mask_weights_path, monitor='val_mask_dice_coef', mode='max', save_best_only=True, save_weights_only=True) 98 | last_checkpointer = ModelCheckpoint(last_ckpt_path, save_best_only=False, save_weights_only=False) 99 | logger = CSVLogger(log, append=args.resume) 100 | progress = TqdmCallback(verbose=1, initial=initial_epoch, dynamic_ncols=True) 101 | early_stop = tf.keras.callbacks.EarlyStopping(monitor='val_mask_dice_coef', mode='max', patience=100) 102 | 103 | callbacks = [best_checkpointer, best_mask_checkpointer, last_checkpointer, logger, progress, early_stop] 104 | 105 | model.fit(train_gen, 106 | epochs=args.epochs, 107 | callbacks=callbacks, 108 | initial_epoch=initial_epoch, 109 | steps_per_epoch=len(train_gen), 110 | validation_data=val_gen, 111 | validation_steps=len(val_gen), 112 | verbose=False) 113 | 114 | if model.stop_training: 115 | open(model_stopped_file, 'w').close() 116 | 117 | tf.keras.models.save_model(model, best_ckpt_path, include_optimizer=False) 118 | 119 | # evaluation on test set 120 | evaluate.evaluate(exp, force=need_training) 121 | 122 | # save best snapshot in SavedModel format 123 | model.load_weights(best_mask_weights_path) 124 | best_savedmodel_path = exp.path_to('best_savedmodel') 125 | model.save(best_savedmodel_path, save_traces=True) 126 | 127 | # export to tfjs (Layers model) 128 | tfjs_model_dir = exp.path_to('tfjs') 129 | tfjs.converters.save_keras_model(model, tfjs_model_dir) 130 | 131 | 132 | if __name__ == '__main__': 133 | default_data = ['data/NN_human_mouse_eyes'] 134 | 135 | parser = argparse.ArgumentParser(description='') 136 | # data params 137 | parser.add_argument('-d', '--data', nargs='+', default=default_data, help='Data directory (may be multiple)') 138 | parser.add_argument('--split', default='random', choices=('random', 'subjects'), help='How to split data') 139 | parser.add_argument('-r', '--resolution', type=int, default=128, help='Input image resolution') 140 | 141 | # model params 142 | parser.add_argument('--num-stages', type=int, default=5, help='number of down-up sample stages') 143 | parser.add_argument('--num-conv', type=int, default=1, help='number of convolutions per stage') 144 | parser.add_argument('--num-filters', type=int, default=16, help='number of conv filter at first stage') 145 | parser.add_argument('--grow-factor', type=float, default=1.5, 146 | help='# filters at stage i = num-filters * grow-factor ** i') 147 | parser.add_argument('--up-activation', default='relu', choices=('relu', 'lrelu'), 148 | help='activation in upsample stages') 149 | parser.add_argument('--conv-type', default='conv', choices=('conv', 'bn-conv', 'sep-conv', 'sep-bn-conv'), 150 | help='convolution type') 151 | parser.add_argument('--use-aspp', default=False, action='store_true', help='Use Atrous Spatial Pyramid Pooling') 152 | 153 | # train params 154 | parser.add_argument('--lr', type=float, default=0.001, help='learning rate') 155 | parser.add_argument('-b', '--batch-size', type=int, default=32, help='Batch size') 156 | parser.add_argument('-e', '--epochs', type=int, default=1500, help='Number of training epochs') 157 | parser.add_argument('-s', '--seed', type=int, default=23, help='Random seed') 158 | parser.add_argument('--resume', default=False, action='store_true', help='Resume training') 159 | 160 | args = parser.parse_args() 161 | main(args) 162 | -------------------------------------------------------------------------------- /show.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import math 3 | import os 4 | os.sys.path += ['expman'] 5 | 6 | import matplotlib 7 | matplotlib.use('Agg') 8 | import matplotlib.pyplot as plt 9 | import matplotlib.ticker as ticker 10 | from matplotlib.image import imread 11 | from matplotlib.backends.backend_pdf import PdfPages 12 | 13 | import numpy as np 14 | import pandas as pd 15 | import seaborn as sns 16 | 17 | from glob import glob 18 | 19 | import expman 20 | 21 | 22 | def ee(args): 23 | sns.set_theme(context='notebook', style='whitegrid') 24 | 25 | exps = expman.gather(args.run).filter(args.filter) 26 | mask_metrics = exps.collect('test_pred/mask_metrics.csv').groupby('exp_id')[['dice', 'iou']].max() 27 | flops_nparams = exps.collect('flops_nparams.csv') 28 | data = pd.merge(mask_metrics, flops_nparams, on='exp_id') 29 | data['dice'] *= 100 30 | 31 | named_data = data.rename({ 32 | 'nparams': '# Params', 33 | 'dice': 'mean Dice Coeff. (%)', 34 | 'conv_type': '$t$ (Conv. Type)', 35 | 'grow_factor': r'$\gamma$', 36 | 'num_filters': '$k$ (# Filters)', 37 | 'flops': 'FLOPs', 38 | 'num_stages': '$s$ (# Stages)', 39 | }, axis=1).replace({ 40 | 'bn-conv': 'conv-bn', 41 | 'sep-bn-conv': 'sep-conv-bn' 42 | }) 43 | 44 | g = sns.relplot(data=named_data, 45 | x='FLOPs', y='mean Dice Coeff. (%)', 46 | hue='$t$ (Conv. Type)', 47 | hue_order=['conv', 'conv-bn', 'sep-conv', 'sep-conv-bn'], 48 | col='$s$ (# Stages)', style='$k$ (# Filters)', markers=True, markersize=9, 49 | kind='line', dashes=True, facet_kws=dict(despine=False, legend_out=False), legend=True, 50 | height=3.8, aspect=1.3, markeredgecolor='white') 51 | 52 | b_formatter = ticker.FuncFormatter(lambda x, pos: '{:.2f}'.format(x / 10 ** 9) + 'B') 53 | 54 | h, l = g.axes.flatten()[0].get_legend_handles_labels() 55 | for hi in h: 56 | hi.set_markeredgecolor('white') 57 | g.axes.flatten()[0].legend_.remove() 58 | g.fig.legend(h, l, ncol=2, bbox_to_anchor=(0.53 ,0.53), 59 | fancybox=False, columnspacing=0, framealpha=1, handlelength=1.2) 60 | 61 | for ax in g.axes.flatten(): 62 | ax.yaxis.set_minor_locator(ticker.AutoMinorLocator()) 63 | ax.set_ylim(bottom=40, top=90) 64 | ax.set_xscale('symlog') 65 | ax.set_xlim(left=0.04 * 10 ** 9, right=2 * 10 ** 9) 66 | 67 | ax.xaxis.set_minor_locator(ticker.SymmetricalLogLocator(base=10, linthresh=2, subs=[1.5, 2,3,4,5,6,8])) 68 | ax.xaxis.set_minor_formatter(b_formatter) 69 | ax.grid(which='minor', linestyle='--', color='#eeeeee') 70 | 71 | ax.xaxis.set_major_formatter(b_formatter) 72 | ax.tick_params(axis="x", which="both", rotation=90) 73 | 74 | plt.savefig(args.output, bbox_inches='tight') 75 | 76 | 77 | def bd(args): 78 | exps = expman.gather(args.run).filter(args.filter) 79 | blink_metrics = exps.collect('test_pred/all_blink_roc_metrics.csv') 80 | blink_metrics = blink_metrics.iloc[3::4].rename({'0': 'auc'}, axis=1) 81 | aucs = blink_metrics.auc.values 82 | print(f'{aucs.mean()} +- {aucs.std()}') 83 | 84 | 85 | def dice_fps(args): 86 | exps = expman.gather(args.run).filter(args.filter) 87 | 88 | mask_metrics = exps.collect('test_pred/mask_metrics.csv') 89 | mask_metrics = mask_metrics.groupby('exp_name').dice.max() 90 | 91 | time_metrics = exps.collect('timings.csv') 92 | time_metrics = time_metrics.rename({'Unnamed: 0': 'metrics', '0':'value'}, axis=1) 93 | time_metrics = time_metrics.pivot_table(index='exp_name', columns='metrics', values='value') 94 | 95 | flops_nparams = exps.collect('flops_nparams.csv') 96 | flops_nparams = flops_nparams.set_index('exp_name')[['flops','nparams']] 97 | 98 | table = pd.concat((time_metrics, mask_metrics, flops_nparams), axis=1)[['dice', 'fps', 'throughput', 'flops', 'nparams']] 99 | table['dice'] = table.dice.map('{:.1%}'.format) 100 | table['fps'] = table.fps.map('{:.1f}'.format) 101 | table['throughput'] = (table.throughput*1000).map('{:.1f}ms'.format) 102 | table['flops'] = (table.flops / 10**9).map('{:.1f}G'.format) 103 | table['nparams'] = (table.nparams / 10**6).map('{:.2f}M'.format) 104 | print(table) 105 | 106 | 107 | def metrics(args): 108 | exps = expman.gather(args.run).filter(args.filter) 109 | mask_metrics = exps.collect('test_pred/mask_metrics.csv') 110 | sns.lineplot(data=mask_metrics, x='thr', y='dice', hue='conv_type', size='grow_factor', style='num_filters') 111 | plt.savefig(args.output) 112 | 113 | 114 | def log(args): 115 | exps = expman.gather(args.run).filter(args.filter) 116 | with PdfPages(args.output) as pdf: 117 | for exp_name, exp in sorted(exps.items()): 118 | print(exp_name) 119 | log = pd.read_csv(exp.path_to('log.csv'), index_col='epoch') 120 | train_cols = [c for c in log.columns if 'val' not in c] 121 | val_cols = [c for c in log.columns if 'val' in c] 122 | 123 | test_images = glob(os.path.join(exp.path_to('test_pred'), '*_samples.png')) 124 | 125 | fig = plt.figure(figsize=(14, 10)) 126 | fig_shape = (2, 2) if test_images else (2, 1) 127 | ax1 = plt.subplot2grid(fig_shape, (0, 0)) 128 | ax2 = plt.subplot2grid(fig_shape, (1, 0)) 129 | 130 | log[train_cols].plot(ax=ax1) 131 | log[val_cols].plot(ax=ax2) 132 | ax1.legend(loc='center right', bbox_to_anchor=(-0.05, 0.5)) 133 | ax2.legend(loc='center right', bbox_to_anchor=(-0.05, 0.5)) 134 | ax2.set_ylim((0, 1)) 135 | 136 | if test_images: 137 | test_images = sorted(test_images) 138 | test_images = list(map(imread, test_images)) 139 | max_w = max(i.shape[1] for i in test_images) 140 | pads = [((0,0), (0, max_w - i.shape[1]), (0, 0)) for i in test_images] 141 | test_images = np.concatenate([np.pad(i, pad) for i, pad in zip(test_images, pads)], axis=0) 142 | 143 | ax3 = plt.subplot2grid(fig_shape, (0, 1), rowspan=2) 144 | ax3.imshow(test_images) 145 | ax3.set_axis_off() 146 | 147 | log_plot_file = exp.path_to('log_plot.pdf') 148 | plt.suptitle(exp_name) 149 | plt.savefig(log_plot_file, bbox_inches='tight') 150 | pdf.savefig(fig, bbox_inches='tight') 151 | plt.close() 152 | 153 | 154 | if __name__ == '__main__': 155 | parser = argparse.ArgumentParser(description='Show stuff') 156 | parser.add_argument('-f', '--filter', default={}, type=expman.exp_filter) 157 | subparsers = parser.add_subparsers() 158 | 159 | parser_log = subparsers.add_parser('log') 160 | parser_log.add_argument('run', default='runs/') 161 | parser_log.add_argument('-o', '--output', default='log_summary.pdf') 162 | parser_log.set_defaults(func=log) 163 | 164 | parser_metrics = subparsers.add_parser('metrics') 165 | parser_metrics.add_argument('run', default='runs/') 166 | parser_metrics.add_argument('-o', '--output', default='mask_metrics_summary.pdf') 167 | parser_metrics.set_defaults(func=metrics) 168 | 169 | parser_ee = subparsers.add_parser('ee') 170 | parser_ee.add_argument('run', default='runs/') 171 | parser_ee.add_argument('-o', '--output', default='ee_summary.pdf') 172 | parser_ee.set_defaults(func=ee) 173 | 174 | parser_bd = subparsers.add_parser('bd') 175 | parser_bd.add_argument('run', default='runs/') 176 | parser_bd.set_defaults(func=bd) 177 | 178 | parser_dice_fps = subparsers.add_parser('dice-fps') 179 | parser_dice_fps.add_argument('run', default='runs/') 180 | parser_dice_fps.set_defaults(func=dice_fps) 181 | 182 | args = parser.parse_args() 183 | args.func(args) 184 | -------------------------------------------------------------------------------- /evaluate.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ MEye: Semantic Segmentation """ 3 | 4 | import argparse 5 | import os 6 | os.sys.path += ['expman', 'models/deeplab'] 7 | import expman 8 | 9 | import matplotlib 10 | matplotlib.use('Agg') 11 | import matplotlib.pyplot as plt 12 | 13 | import numpy as np 14 | import pandas as pd 15 | import tensorflow as tf 16 | from tensorflow.keras import backend as K 17 | from tensorflow.keras.models import load_model 18 | from sklearn.model_selection import train_test_split 19 | from sklearn.metrics import classification_report, roc_curve, auc, precision_recall_curve, average_precision_score 20 | from adabelief_tf import AdaBeliefOptimizer 21 | from glob import glob 22 | from tqdm import tqdm 23 | from PIL import Image 24 | 25 | from deeplabv3p.models.deeplabv3p_mobilenetv3 import hard_swish 26 | from dataloader import get_loader, load_datasets 27 | from utils import visualize, visualizable 28 | 29 | 30 | def iou_coef(y_true, y_pred, smooth=0.001, thr=None): 31 | y_pred = K.cast(y_pred > thr, 'float32') if thr is not None else y_pred 32 | intersection = K.sum(K.abs(y_true * y_pred), axis=[1, 2, 3]) 33 | union = K.sum(y_true, [1, 2, 3]) + K.sum(y_pred, [1, 2, 3]) - intersection 34 | iou = K.mean((intersection + smooth) / (union + smooth), axis=0) 35 | return iou 36 | 37 | 38 | def dice_coef(y_true, y_pred, smooth=0.001, thr=None): 39 | y_pred = K.cast(y_pred > thr, 'float32') if thr is not None else y_pred 40 | intersection = K.sum(y_true * y_pred, axis=[1, 2, 3]) 41 | union = K.sum(y_true, axis=[1, 2, 3]) + K.sum(y_pred, axis=[1, 2, 3]) 42 | dice = K.mean((2. * intersection + smooth) / (union + smooth), axis=0) 43 | return dice 44 | 45 | 46 | def _filter_by_closeness(a, eps=10e-3): 47 | keep = [] 48 | prev = np.array([-1, -1]) 49 | for row in a.drop('thr', axis=1).values: 50 | if (np.abs(prev - row) > eps).any(): 51 | keep.append(True) 52 | prev = row 53 | else: 54 | keep.append(False) 55 | return a[keep] 56 | 57 | 58 | def _weighted_roc_pr(y_true, y_scores, label, outdir, simplify=False): 59 | npos = y_true.sum() 60 | nneg = len(y_true) - npos 61 | pos_weight = nneg / npos 62 | print(label, 'Tot:', len(y_true), 'P:', npos, 'N:', nneg, 'N/P:', pos_weight) 63 | sample_weight = np.where(y_true, pos_weight, 1) 64 | 65 | fpr, tpr, thr = roc_curve(y_true, y_scores, sample_weight=sample_weight) 66 | auc_score = auc(fpr, tpr) 67 | print(label, 'AuROC:', auc_score) 68 | 69 | roc_metrics = pd.Series({'npos': npos, 'nneg': nneg, 'nneg_over_npos': pos_weight, 'roc_auc': auc_score}) 70 | roc_metrics_file = os.path.join(outdir, '{}_roc_metrics.csv'.format(label)) 71 | roc_metrics.to_csv(roc_metrics_file, index=False) 72 | 73 | roc = pd.DataFrame({'fpr': fpr, 'tpr': tpr, 'thr': thr}) 74 | if simplify: 75 | full_roc_file = os.path.join(outdir, '{}_roc_curve_full.csv.gz'.format(label)) 76 | roc.to_csv(full_roc_file, index=False) 77 | roc = _filter_by_closeness(roc) 78 | 79 | roc_file = os.path.join(outdir, '{}_roc_curve.csv'.format(label)) 80 | roc.to_csv(roc_file, index=False) 81 | 82 | roc.plot(x='fpr', y='tpr', xlim=(0, 1), ylim=(0, 1)) 83 | roc_plot_file = os.path.join(outdir, '{}_roc.pdf'.format(label)) 84 | plt.savefig(roc_plot_file) 85 | plt.close() 86 | 87 | precision, recall, thr = precision_recall_curve(y_true, y_scores, sample_weight=sample_weight) 88 | f1_score = 2 * precision * recall / (precision + recall) 89 | pr_auc = auc(recall, precision) 90 | 91 | pr_metrics = pd.Series({'npos': npos, 'nneg': nneg, 'nneg_over_npos': pos_weight, 'pr_auc': pr_auc}) 92 | pr_metrics_file = os.path.join(outdir, '{}_pr_metrics.csv'.format(label)) 93 | pr_metrics.to_csv(pr_metrics_file, index=False) 94 | 95 | thr = np.append(thr, [thr[-1]]) 96 | pr = pd.DataFrame({'precision': precision, 'recall': recall, 'f1_score': f1_score, 'thr': thr}) 97 | if simplify: 98 | full_pr_file = os.path.join(outdir, '{}_pr_curve_full.csv.gz'.format(label)) 99 | pr.to_csv(full_pr_file, index=False) 100 | pr = _filter_by_closeness(pr) 101 | 102 | pr_file = os.path.join(outdir, '{}_pr_curve.csv'.format(label)) 103 | pr.to_csv(pr_file, index=False) 104 | 105 | pr.plot(x='recall', y='precision', xlim=(0, 1), ylim=(0, 1)) 106 | pr_plot_file = os.path.join(outdir, '{}_pr.pdf'.format(label)) 107 | plt.savefig(pr_plot_file) 108 | plt.close() 109 | 110 | print(label, 'AuPR:', pr_auc, 'AvgP:', average_precision_score(y_true, y_scores, sample_weight=sample_weight)) 111 | 112 | 113 | # https://github.com/tensorflow/tensorflow/issues/32809#issuecomment-768977280 114 | from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2_as_graph 115 | def get_flops(model): 116 | concrete = tf.function(lambda inputs: model(inputs)) 117 | concrete_func = concrete.get_concrete_function( 118 | [tf.TensorSpec([1, *inputs.shape[1:]]) for inputs in model.inputs]) 119 | frozen_func, graph_def = convert_variables_to_constants_v2_as_graph(concrete_func) 120 | with tf.Graph().as_default() as graph: 121 | tf.graph_util.import_graph_def(graph_def, name='') 122 | run_meta = tf.compat.v1.RunMetadata() 123 | opts = tf.compat.v1.profiler.ProfileOptionBuilder.float_operation() 124 | flops = tf.compat.v1.profiler.profile(graph=graph, run_meta=run_meta, cmd="op", options=opts) 125 | 126 | tf.compat.v1.reset_default_graph() 127 | return flops.total_float_ops 128 | 129 | 130 | def evaluate(exp, force=False): 131 | 132 | ckpt_path = exp.path_to('best_model.h5') 133 | 134 | custom_objects = {'AdaBeliefOptimizer': AdaBeliefOptimizer, 'iou_coef': iou_coef, 'dice_coef': dice_coef, 'hard_swish': hard_swish} 135 | model = tf.keras.models.load_model(ckpt_path, custom_objects=custom_objects) 136 | 137 | # get flops 138 | flop_params_path = exp.path_to('flops_nparams.csv') 139 | if force or not os.path.exists(flop_params_path): 140 | model.compile() 141 | tf.keras.models.save_model(model, 'tmp_model', overwrite=True, include_optimizer=False) 142 | stripped_model = tf.keras.models.load_model('tmp_model') 143 | flops = get_flops(stripped_model) 144 | nparams = stripped_model.count_params() 145 | del stripped_model 146 | print('FLOPS:', flops) 147 | print('#PARAMS:', nparams) 148 | pd.DataFrame({'flops': flops, 'nparams': nparams}, index=[0]).to_csv(flop_params_path) 149 | 150 | model.compile(loss='binary_crossentropy', metrics={'mask': [iou_coef, dice_coef], 'tags': 'binary_accuracy'}) 151 | 152 | params = exp.params 153 | np.random.seed(params.seed) 154 | tf.random.set_seed(params.seed) 155 | 156 | data = load_datasets(params.data) 157 | 158 | # TRAIN/VAL/TEST SPLIT 159 | if params.split == 'subjects': # by SUBJECTS 160 | # val_subjects = (6, 9, 11, 13, 16, 28, 30, 48, 49) 161 | test_subjects = (3, 4, 19, 38, 45, 46, 51, 52) 162 | # train_data = data[~data['sub'].isin(val_subjects + test_subjects)] 163 | # val_data = data[data['sub'].isin(val_subjects)] 164 | test_data = data[data['sub'].isin(test_subjects)] 165 | 166 | elif params.split == 'random': # 70-20-10 % 167 | _, valtest_data = train_test_split(data, test_size=.3, shuffle=True) 168 | _, test_data = train_test_split(valtest_data, test_size=.33) 169 | 170 | x_shape = (params.resolution, params.resolution, 1) 171 | test_gen, test_categories = get_loader(test_data, batch_size=1, x_shape=x_shape) 172 | 173 | prediction_dir = exp.path_to('test_pred') 174 | os.makedirs(prediction_dir, exist_ok=True) 175 | 176 | loss_per_sample = None 177 | 178 | def _get_test_predictions(test_gen, model): 179 | x_masks = [] 180 | y_masks, y_tags = [], [] 181 | pred_masks, pred_tags = [], [] 182 | loss_per_sample = [] 183 | 184 | for x, y in tqdm(test_gen, desc='TEST'): 185 | sample_loss = model.test_on_batch(x, reset_metrics=True) 186 | loss_per_sample.append(sample_loss) 187 | 188 | p_mask, p_tags = model.predict_on_batch(x) 189 | pred_masks.append(p_mask) 190 | pred_tags.append(p_tags) 191 | y_masks.append(y['mask'].numpy()) 192 | y_tags.append(y['tags'].numpy()) 193 | x_masks.append(x.numpy()) 194 | 195 | loss_per_sample = np.array(loss_per_sample) 196 | pred_masks = np.concatenate(pred_masks) 197 | pred_tags = np.concatenate(pred_tags) 198 | y_masks = np.concatenate(y_masks) 199 | y_tags = np.concatenate(y_tags) 200 | x_masks = np.concatenate(x_masks) 201 | 202 | return loss_per_sample, x_masks, y_masks, y_tags, pred_masks, pred_tags 203 | 204 | 205 | mask_metrics_path = exp.path_to('test_pred/mask_metrics.csv') 206 | if force or not os.path.exists(mask_metrics_path): 207 | if loss_per_sample is None: 208 | loss_per_sample, x_masks, y_masks, y_tags, pred_masks, pred_tags = _get_test_predictions(test_gen, model) 209 | 210 | thrs = np.linspace(0, 1, 101) 211 | ious = [iou_coef(y_masks, pred_masks, thr=thr).numpy() for thr in thrs] 212 | dices = [dice_coef(y_masks, pred_masks, thr=thr).numpy() for thr in thrs] 213 | 214 | best_thr = max(zip(dices, thrs))[1] 215 | 216 | mask_metrics = pd.DataFrame({'iou': ious, 'dice': dices, 'thr': thrs}) 217 | print(mask_metrics.max(axis=0)) 218 | mask_metrics.to_csv(mask_metrics_path) 219 | else: 220 | mask_metrics = pd.read_csv(mask_metrics_path, index_col=0) 221 | best_thr = mask_metrics.loc[mask_metrics.dice.idxmax(), 'thr'] 222 | 223 | if force: 224 | if loss_per_sample is None: 225 | loss_per_sample, x_masks, y_masks, y_tags, pred_masks, pred_tags = _get_test_predictions(test_gen, model) 226 | # _weighted_roc_pr(y_masks.ravel(), pred_masks.ravel(), 'all_pupil', prediction_dir, simplify=True) 227 | _weighted_roc_pr(y_tags[:, 0], pred_tags[:, 0], 'all_eye', prediction_dir) 228 | _weighted_roc_pr(y_tags[:, 1], pred_tags[:, 1], 'all_blink', prediction_dir) 229 | 230 | filenames = ('top_samples.png', 'bottom_samples.png', 'random_samples.png') 231 | if force or any(not os.path.exists(os.path.join(prediction_dir, f)) for f in filenames): 232 | if loss_per_sample is None: 233 | loss_per_sample, x_masks, y_masks, y_tags, pred_masks, pred_tags = _get_test_predictions(test_gen, model) 234 | 235 | k = 5 236 | best_selector = [] 237 | worst_selector = [] 238 | random_selector = [] 239 | 240 | idx = np.arange(len(test_data)) 241 | for cat in np.unique(test_categories): 242 | cat_outdir = os.path.join(prediction_dir, cat) 243 | os.makedirs(cat_outdir, exist_ok=True) 244 | 245 | selector = test_categories == cat 246 | # _weighted_roc_pr(y_masks[selector].ravel(), pred_masks[selector].ravel(), '{}_pupil'.format(cat), cat_outdir, simplify=True) 247 | _weighted_roc_pr(y_tags[selector, 0], pred_tags[selector, 0], '{}_eye'.format(cat), cat_outdir) 248 | _weighted_roc_pr(y_tags[selector, 1], pred_tags[selector, 1], '{}_blink'.format(cat), cat_outdir) 249 | 250 | cat_losses = loss_per_sample[selector, 1] 251 | rank = cat_losses.argsort() 252 | topk, bottomk = rank[:k], rank[-k:] 253 | 254 | best_selector += idx[selector][topk].tolist() 255 | worst_selector += idx[selector][bottomk].tolist() 256 | random_selector += np.random.choice(idx[selector], k, replace=False).tolist() 257 | 258 | # topk-bottomk images 259 | selectors = (best_selector, worst_selector, random_selector) 260 | for selector, outfile in zip(selectors, filenames): 261 | combined_m = np.concatenate((pred_masks[selector], y_masks[selector]), axis=-1)[:, :, :, ::-1] 262 | combined_t = np.concatenate((pred_tags[selector], y_tags[selector]), axis=-1) 263 | combined_y = (combined_m, combined_t) 264 | out = os.path.join(prediction_dir, outfile) 265 | visualize(x_masks[selector], combined_y, out=out, thr=best_thr, n_cols=k, width=10) 266 | 267 | for i, (xi, yi_mask) in enumerate(zip(x_masks[selector], combined_m)): 268 | img = visualizable(xi, yi_mask, thr=best_thr) 269 | img = (img * 255).astype(np.uint8) 270 | out = os.path.join(prediction_dir, outfile[:-4]) 271 | os.makedirs(out, exist_ok=True) 272 | out = os.path.join(out, f'{i}.png') 273 | Image.fromarray(img).save(out) 274 | 275 | 276 | def main(args): 277 | for exp in expman.gather(args.run).filter(args.filter): 278 | print(exp) 279 | evaluate(exp, force=args.force) 280 | 281 | 282 | if __name__ == '__main__': 283 | parser = argparse.ArgumentParser(description='Evaluate Run') 284 | # data params 285 | parser.add_argument('run', help='Run(s) directory') 286 | parser.add_argument('-f', '--filter', default={}, type=expman.exp_filter) 287 | parser.add_argument('--force', default=False, action='store_true', help='Force metrics recomputation') 288 | 289 | args = parser.parse_args() 290 | main(args) 291 | -------------------------------------------------------------------------------- /matlab/Meye.m: -------------------------------------------------------------------------------- 1 | classdef Meye 2 | 3 | properties (Access=private) 4 | model 5 | end 6 | 7 | 8 | methods 9 | 10 | % CONSTRUCTOR 11 | %------------------------------------------------------------------ 12 | function self = Meye(modelPath) 13 | % Class constructor 14 | arguments 15 | modelPath char {mustBeText} 16 | end 17 | 18 | % Change the current directory to the directory where the 19 | % original class is, so that the package with the custom layers 20 | % is created there 21 | classPath = getClassPath(self); 22 | oldFolder = cd(classPath); 23 | % Import the model saved as ONNX 24 | self.model = importONNXNetwork(modelPath, ... 25 | 'GenerateCustomLayers',true, ... 26 | 'PackageName','customLayers_meye',... 27 | 'InputDataFormats', 'BSSC',... 28 | 'OutputDataFormats',{'BSSC','BC'}); 29 | 30 | % Manually change the "nearest" option to "linear" inside of 31 | % the automatically generated custom layers. This is necessary 32 | % due to the fact that MATLAB still does not support the proper 33 | % translation between ONNX layers and DLtoolbox layers 34 | self.nearest2Linear([classPath filesep '+customLayers_meye']) 35 | 36 | % Go back to the old current folder 37 | cd(oldFolder) 38 | end 39 | 40 | 41 | % PREDICTION OF SINGLE IMAGES 42 | %------------------------------------------------------------------ 43 | function [pupilMask, eyeProb, blinkProb] = predictImage(self, inputImage, options) 44 | % Predicts pupil location on a single image 45 | arguments 46 | self 47 | inputImage 48 | options.roiPos = [] 49 | options.threshold = [] 50 | end 51 | 52 | roiPos = options.roiPos; 53 | 54 | % Convert the image to grayscale if RGB 55 | if size(inputImage,3) > 1 56 | inputImage = im2gray(inputImage); 57 | end 58 | 59 | % Crop the frame to the desired ROI 60 | if ~isempty(roiPos) 61 | crop = inputImage(roiPos(2):roiPos(2)+roiPos(4)-1,... 62 | roiPos(1):roiPos(1)+roiPos(3)-1); 63 | else 64 | crop = inputImage; 65 | end 66 | 67 | % Preprocessing 68 | img = double(imresize(crop,[128 128])); 69 | img = img / max(img,[],'all'); 70 | 71 | % Do the prediction 72 | [rawMask, info] = predict(self.model, img); 73 | eyeProb = info(1); 74 | blinkProb = info(2); 75 | 76 | % Reinsert the cropped prediction in the frame 77 | if ~isempty(roiPos) 78 | pupilMask = zeros(size(inputImage)); 79 | pupilMask(roiPos(2):roiPos(2)+roiPos(4)-1,... 80 | roiPos(1):roiPos(1)+roiPos(3)-1) = imresize(rawMask, [roiPos(4), roiPos(3)],"bilinear"); 81 | else 82 | pupilMask = imresize(rawMask,size(inputImage),"bilinear"); 83 | end 84 | 85 | % Apply a threshold to the image if requested 86 | if ~isempty(options.threshold) 87 | pupilMask = pupilMask > options.threshold; 88 | end 89 | 90 | end 91 | 92 | 93 | % PREDICT A MOVIE AND GET A TABLE WITH THE RESULTS 94 | %------------------------------------------------------------------ 95 | function tab = predictMovie(self, moviePath, options) 96 | % Predict an entire video file and returns a results Table 97 | % 98 | % tab = predictMovie(moviePath, name-value) 99 | % 100 | % INPUT(S) 101 | % - moviePath: (char/string) Full path of a video file. 102 | % - name-value pairs 103 | % - roiPos: [x,y,width,height] 4-elements vector defining a 104 | % rectangle containing the eye. Works best if width and 105 | % height are similar. If empty, a prediction will be done on 106 | % a full frame(Default: []). 107 | % - threshold: [0-1] The pupil prediction is binarized based 108 | % on a threshold value to measure pupil size. (Default:0.4) 109 | % 110 | % OUTPUT(S) 111 | % - tab: a MATLAB table containing data of the analyzed video 112 | 113 | arguments 114 | self 115 | moviePath char {mustBeText} 116 | options.roiPos double = [] 117 | options.threshold = 0.4; 118 | end 119 | 120 | % Initialize a video reader 121 | v = VideoReader(moviePath); 122 | totFrames = v.NumFrames; 123 | 124 | % Initialize Variables 125 | frameN = zeros(totFrames,1,'double'); 126 | frameTime = zeros(totFrames,1,'double'); 127 | binaryMask = cell(totFrames,1); 128 | pupilArea = zeros(totFrames,1,'double'); 129 | isEye = zeros(totFrames,1,'double'); 130 | isBlink = zeros(totFrames,1,'double'); 131 | 132 | tic 133 | for i = 1:totFrames 134 | % Progress report 135 | if toc>10 136 | fprintf('%.1f%% - Processing frame (%u/%u)\n', (i/totFrames)*100 , i, totFrames) 137 | tic 138 | end 139 | 140 | % Read a frame and make its prediction 141 | frame = read(v, i, 'native'); 142 | [pupilMask, eyeProb, blinkProb] = self.predictImage(frame, roiPos=options.roiPos,... 143 | threshold=options.threshold); 144 | 145 | % Save results for this frame 146 | frameN(i) = i; 147 | frameTime(i) = v.CurrentTime; 148 | binaryMask{i} = pupilMask > options.threshold; 149 | pupilArea(i) = sum(binaryMask{i},"all"); 150 | isEye(i) = eyeProb; 151 | isBlink(i) = blinkProb; 152 | end 153 | % Save all the results in a final table 154 | tab = table(frameN,frameTime,binaryMask,pupilArea,isEye,isBlink); 155 | end 156 | 157 | 158 | 159 | % PREVIEW OF A PREDICTED MOVIE 160 | %------------------------------------------------------------------ 161 | function predictMovie_Preview(self, moviePath, options) 162 | % Displays a live-preview of prediction for a video file 163 | 164 | arguments 165 | self 166 | moviePath char {mustBeText} 167 | options.roiPos double = [] 168 | options.threshold double = [] 169 | end 170 | roiPos = options.roiPos; 171 | 172 | 173 | % Initialize a video reader 174 | v = VideoReader(moviePath); 175 | % Initialize images to show 176 | blankImg = zeros(v.Height, v.Width, 'uint8'); 177 | cyanColor = cat(3, blankImg, blankImg+255, blankImg+255); 178 | pupilTransparency = blankImg; 179 | 180 | % Create a figure for the preview 181 | figHandle = figure(... 182 | 'Name','MEYE video preview',... 183 | 'NumberTitle','off',... 184 | 'ToolBar','none',... 185 | 'MenuBar','none', ... 186 | 'Color',[.1, .1, .1]); 187 | 188 | ax = axes('Parent',figHandle,... 189 | 'Units','normalized',... 190 | 'Position',[0 0 1 .94]); 191 | 192 | imHandle = imshow(blankImg,'Parent',ax); 193 | hold on 194 | cyanHandle = imshow(cyanColor,'Parent',ax); 195 | cyanHandle.AlphaData = pupilTransparency; 196 | rect = rectangle('LineWidth',1.5, 'LineStyle','-.','EdgeColor',[1,0,0],... 197 | 'Parent',ax,'Position',[0,0,0,0]); 198 | hold off 199 | title(ax,'MEYE Video Preview', 'Color',[1,1,1]) 200 | 201 | % Movie-Showing loop 202 | while exist("figHandle","var") && ishandle(figHandle) && hasFrame(v) 203 | try 204 | tic 205 | frame = readFrame(v); 206 | 207 | % Actually do the prediction 208 | [pupilMask, eyeProb, blinkProb] = self.predictImage(frame, roiPos=roiPos,... 209 | threshold=options.threshold); 210 | 211 | % Update graphic elements 212 | imHandle.CData = frame; 213 | cyanHandle.AlphaData = imresize(pupilMask, [v.Height, v.Width]); 214 | if ~isempty(roiPos) 215 | rect.Position = roiPos; 216 | end 217 | titStr = sprintf('Eye: %.2f%% - Blink:%.2f%% - FPS:%.1f',... 218 | eyeProb*100, blinkProb*100, 1/toc); 219 | ax.Title.String = titStr; 220 | drawnow 221 | catch ME 222 | warning(ME.message) 223 | close(figHandle) 224 | end 225 | end 226 | disp('Stop preview.') 227 | end 228 | 229 | 230 | end 231 | 232 | 233 | %------------------------------------------------------------------ 234 | %------------------------------------------------------------------ 235 | % INTERNAL FUNCTIONS 236 | %------------------------------------------------------------------ 237 | %------------------------------------------------------------------ 238 | methods(Access=private) 239 | %------------------------------------------------------------------ 240 | function path = getClassPath(~) 241 | % Returns the full path of where the class file is 242 | 243 | fullPath = mfilename('fullpath'); 244 | [path,~,~] = fileparts(fullPath); 245 | end 246 | 247 | %------------------------------------------------------------------ 248 | function [fplist,fnlist] = listfiles(~, folderpath, token) 249 | listing = dir(folderpath); 250 | index = 0; 251 | fplist = {}; 252 | fnlist = {}; 253 | for i = 1:size(listing,1) 254 | s = listing(i).name; 255 | if contains(s,token) 256 | index = index+1; 257 | fplist{index} = [folderpath filesep s]; 258 | fnlist{index} = s; 259 | end 260 | end 261 | end 262 | 263 | % nearest2Linear 264 | %------------------------------------------------------------------ 265 | function nearest2Linear(self, inputPath) 266 | fP = self.listfiles(inputPath, 'Shape_To_Upsample'); 267 | 268 | foundFileToChange = false; 269 | beforePatter = '"half_pixel", "nearest",'; 270 | afterPattern = '"half_pixel", "linear",'; 271 | for i = 1:length(fP) 272 | 273 | % Get the content of the file 274 | fID = fopen(fP{i}, 'r'); 275 | f = fread(fID,'*char')'; 276 | fclose(fID); 277 | 278 | % Send a verbose warning the first time we are manually 279 | % correcting the upsampling layers bug 280 | if ~foundFileToChange && contains(f,beforePatter) 281 | foundFileToChange = true; 282 | msg = ['This is a message from MEYE developers.\n' ... 283 | 'In the current release of the Deep Learning Toolbox ' ... 284 | 'MATLAB does not translate well all the layers in the ' ... 285 | 'ONNX network to native MATLAB layers. In particular the ' ... 286 | 'automatically generated custom layers that have to do ' ... 287 | 'with UPSAMPLING are generated with the ''nearest'' instead of ' ... 288 | 'the ''linear'' mode.\nWe automatically correct for this bug when you ' ... 289 | 'instantiate a Meye object (henche this warning).\nEverything should work fine, ' ... 290 | 'and we hope that in future MATLAB releases this hack wont be ' ... 291 | 'needed anymore.\n' ... 292 | 'If you find bugs or performance issues, please let us know ' ... 293 | 'with an issue ' ... 294 | 'HERE.']; 295 | warning(sprintf(msg)) 296 | end 297 | 298 | % Replace the 'nearest' option with 'linear' 299 | newF = strrep(f, beforePatter, afterPattern); 300 | 301 | % Save the file back in its original location 302 | fID = fopen(fP{i}, 'w'); 303 | fprintf(fID,'%s',newF); 304 | fclose(fID); 305 | end 306 | end 307 | end 308 | end 309 | 310 | 311 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------