├── README.md
├── data.py
├── eval.py
├── files
├── README.md
├── data.csv
└── score.csv
├── metrics.py
├── model.py
├── new_data
└── README.md
├── people_segmentation
└── README.md
├── predict.py
├── results
├── football-american-football-referee-referee-football-official-163039_0.png
├── girl-bicycle-garden-people-630770_0.png
├── girl-blonde-wreath-water-160526_0.png
├── girl-cascada-water-waves-160588_0.png
├── girl-cat-kitten-fashion_0.png
├── girl-dance-dress-flowers-163508_0.png
├── girl-forest-nature-stroll-160627_0.png
├── girl-in-leather-jacket-beautiful-hair-model-160974_0.png
├── girl-model-blonde-slav-157666_0.png
├── girl-park-woman-greens-157854_0.png
├── girl-peasant-woman-tradition-water-158007_0.png
├── girl-portrait-face-woman-122426_0.png
├── girl-rustic-grass-meadow-89779_0.png
├── girl-sunset-lake-water-160534_0.png
├── girl-water-wet-summer-122436_0.png
├── girl-wild-water-rock-160440_0.png
├── girl-woman-portrait-young-115015_0.png
├── girl-young-blue-eyes-eyes-39295_0.png
├── girls-children-kids-friends-50581_0.png
├── girls-women-happy-sexy-53364_0.png
├── glamour-style-hat-woman-37649_0.png
├── hammer-throw-athlete-track-and-field-training-37636_0.png
├── hanging-out-trainer-young-79990_fk4fhyJJPf_0.png
├── hiker-traveler-trip-travel-160483_0.png
├── james-stewart-man-person-actor-53487_0.png
├── kickboxer-girl-kickboxing-athletic-girl-160920_0.png
├── love-old-people-the-heart-of-pension-160936_0.png
├── man-selling-razors-black-and-white-older-68655_0.png
├── man-stress-male-face_0.png
├── maternity-baby-belly-pregnant-pregnancy-160776_0.png
├── model-fashion-girl-female-39678_0.png
├── motorbike-racing-motorcycle-race-51388_0.png
├── motorcycle-stunt-jump-motocross-38277_0.png
└── norway-mountain-sky-blue_0.png
├── test_images
├── image
│ ├── wp5815325.jpg
│ ├── wp6244146.jpg
│ ├── wp8058382.jpg
│ ├── wp8725152.jpg
│ ├── wp8725153.jpg
│ ├── wp8725159.jpg
│ ├── wp8725161.jpg
│ ├── wp8725163.jpg
│ ├── wp8725167.jpg
│ ├── wp8725175.jpg
│ ├── wp8725177.jpg
│ ├── wp8725190.jpg
│ ├── wp8725197.jpg
│ ├── wp8725209.jpg
│ ├── wp8725227.jpg
│ ├── wp8725377.jpg
│ └── wp8725382.jpg
└── mask
│ ├── wp5815325.png
│ ├── wp6244146.png
│ ├── wp8058382.png
│ ├── wp8725159.png
│ └── wp8725197.png
└── train.py
/README.md:
--------------------------------------------------------------------------------
1 | # Human Image Segmentation with DeepLabV3+ in TensorFlow
2 | DeepLabV3+ with squeeze and excitation network for human image segmentation in TensorFlow 2.5.0
3 | Watch the YouTube video for better explaination: [https://youtu.be/4LhUpCWBzT8](https://youtu.be/4LhUpCWBzT8)
4 |
5 | ## Dataset
6 | Download the dataset: [Person Segmentation](https://www.kaggle.com/nikhilroxtomar/person-segmentation/download)
7 |
8 | ## Weight file
9 | Download the weight files from here: [model.h5](https://drive.google.com/file/d/17QKxSIBFhyJoDps93-sCVHnVV6UWS1sG/view?usp=sharing)
10 |
11 | ## Quantitative results
12 | These results are on the validation/test set.
13 | | Accuacy | F1 | Jaccard | Recall | Precision |
14 | | :---: | :---: | :---: | :---: | :---: |
15 | | 0.97404 | 0.95379 | 0.92124 | 0.95885 | 0.95683 |
16 |
17 | ## Qualtiative results
18 | These qualitative results are on the validation/test set. The figure consists of a) Input Image b) Ground Truth Mask c) Predicted Mask d) Masked Image
19 | 
20 | :--------------------------------------------------------------------------------------:
21 | 
22 | 
23 | 
24 |
25 | These qualitative results are on random images taken from [https://wallpapercave.com](https://wallpapercave.com). The figure consists of a) Input Image b) Masked Image.
26 | 
27 | :--------------------------------------------------------------------------------------:
28 | 
29 | 
30 | 
31 |
32 | ## Contact
33 | For any query contact below:
34 | - [Facebook](https://www.facebook.com/idiotdeveloper)
35 | - [Instagram](https://instagram/nikhilroxtomar)
36 | - [Telegram](t.me/idiotdeveloper)
37 |
38 |
39 |
--------------------------------------------------------------------------------
/data.py:
--------------------------------------------------------------------------------
1 |
2 | import os
3 | import numpy as np
4 | import cv2
5 | from glob import glob
6 | from tqdm import tqdm
7 | from sklearn.model_selection import train_test_split
8 | from albumentations import HorizontalFlip, GridDistortion, OpticalDistortion, ChannelShuffle, CoarseDropout, CenterCrop, Crop, Rotate
9 |
10 | """ Creating a directory """
11 | def create_dir(path):
12 | if not os.path.exists(path):
13 | os.makedirs(path)
14 |
15 | def load_data(path, split=0.1):
16 | """ Loading the images and masks """
17 | X = sorted(glob(os.path.join(path, "images", "*.jpg")))
18 | Y = sorted(glob(os.path.join(path, "masks", "*.png")))
19 |
20 | """ Spliting the data into training and testing """
21 | split_size = int(len(X) * split)
22 |
23 | train_x, test_x = train_test_split(X, test_size=split_size, random_state=42)
24 | train_y, test_y = train_test_split(Y, test_size=split_size, random_state=42)
25 |
26 | return (train_x, train_y), (test_x, test_y)
27 |
28 | def augment_data(images, masks, save_path, augment=True):
29 | H = 512
30 | W = 512
31 |
32 | for x, y in tqdm(zip(images, masks), total=len(images)):
33 | """ Extract the name """
34 | name = x.split("/")[-1].split(".")[0]
35 |
36 | """ Reading the image and mask """
37 | x = cv2.imread(x, cv2.IMREAD_COLOR)
38 | y = cv2.imread(y, cv2.IMREAD_COLOR)
39 |
40 | """ Augmentation """
41 | if augment == True:
42 | aug = HorizontalFlip(p=1.0)
43 | augmented = aug(image=x, mask=y)
44 | x1 = augmented["image"]
45 | y1 = augmented["mask"]
46 |
47 | x2 = cv2.cvtColor(x, cv2.COLOR_RGB2GRAY)
48 | y2 = y
49 |
50 | aug = ChannelShuffle(p=1)
51 | augmented = aug(image=x, mask=y)
52 | x3 = augmented['image']
53 | y3 = augmented['mask']
54 |
55 | aug = CoarseDropout(p=1, min_holes=3, max_holes=10, max_height=32, max_width=32)
56 | augmented = aug(image=x, mask=y)
57 | x4 = augmented['image']
58 | y4 = augmented['mask']
59 |
60 | aug = Rotate(limit=45, p=1.0)
61 | augmented = aug(image=x, mask=y)
62 | x5 = augmented["image"]
63 | y5 = augmented["mask"]
64 |
65 | X = [x, x1, x2, x3, x4, x5]
66 | Y = [y, y1, y2, y3, y4, y5]
67 |
68 | else:
69 | X = [x]
70 | Y = [y]
71 |
72 | index = 0
73 | for i, m in zip(X, Y):
74 | try:
75 | """ Center Cropping """
76 | aug = CenterCrop(H, W, p=1.0)
77 | augmented = aug(image=i, mask=m)
78 | i = augmented["image"]
79 | m = augmented["mask"]
80 |
81 | except Exception as e:
82 | i = cv2.resize(i, (W, H))
83 | m = cv2.resize(m, (W, H))
84 |
85 | tmp_image_name = f"{name}_{index}.png"
86 | tmp_mask_name = f"{name}_{index}.png"
87 |
88 | image_path = os.path.join(save_path, "image", tmp_image_name)
89 | mask_path = os.path.join(save_path, "mask", tmp_mask_name)
90 |
91 | cv2.imwrite(image_path, i)
92 | cv2.imwrite(mask_path, m)
93 |
94 | index += 1
95 |
96 |
97 | if __name__ == "__main__":
98 | """ Seeding """
99 | np.random.seed(42)
100 |
101 | """ Load the dataset """
102 | data_path = "people_segmentation"
103 | (train_x, train_y), (test_x, test_y) = load_data(data_path)
104 |
105 | print(f"Train:\t {len(train_x)} - {len(train_y)}")
106 | print(f"Test:\t {len(test_x)} - {len(test_y)}")
107 |
108 | """ Create directories to save the augmented data """
109 | create_dir("new_data/train/image/")
110 | create_dir("new_data/train/mask/")
111 | create_dir("new_data/test/image/")
112 | create_dir("new_data/test/mask/")
113 |
114 | """ Data augmentation """
115 | augment_data(train_x, train_y, "new_data/train/", augment=True)
116 | augment_data(test_x, test_y, "new_data/test/", augment=False)
117 |
--------------------------------------------------------------------------------
/eval.py:
--------------------------------------------------------------------------------
1 |
2 | import os
3 | os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
4 |
5 | import numpy as np
6 | import cv2
7 | import pandas as pd
8 | from glob import glob
9 | from tqdm import tqdm
10 | import tensorflow as tf
11 | from tensorflow.keras.utils import CustomObjectScope
12 | from sklearn.metrics import accuracy_score, f1_score, jaccard_score, precision_score, recall_score
13 | from metrics import dice_loss, dice_coef, iou
14 | from train import load_data
15 |
16 | """ Global parameters """
17 | H = 512
18 | W = 512
19 |
20 | """ Creating a directory """
21 | def create_dir(path):
22 | if not os.path.exists(path):
23 | os.makedirs(path)
24 |
25 | def save_results(image, mask, y_pred, save_image_path):
26 | ## i - m - yp - yp*i
27 | line = np.ones((H, 10, 3)) * 128
28 |
29 | mask = np.expand_dims(mask, axis=-1) ## (512, 512, 1)
30 | mask = np.concatenate([mask, mask, mask], axis=-1) ## (512, 512, 3)
31 | mask = mask * 255
32 |
33 | y_pred = np.expand_dims(y_pred, axis=-1) ## (512, 512, 1)
34 | y_pred = np.concatenate([y_pred, y_pred, y_pred], axis=-1) ## (512, 512, 3)
35 |
36 | masked_image = image * y_pred
37 | y_pred = y_pred * 255
38 |
39 | cat_images = np.concatenate([image, line, mask, line, y_pred, line, masked_image], axis=1)
40 | cv2.imwrite(save_image_path, cat_images)
41 |
42 | if __name__ == "__main__":
43 | """ Seeding """
44 | np.random.seed(42)
45 | tf.random.set_seed(42)
46 |
47 | """ Directory for storing files """
48 | create_dir("results")
49 |
50 | """ Loading model """
51 | with CustomObjectScope({'iou': iou, 'dice_coef': dice_coef, 'dice_loss': dice_loss}):
52 | model = tf.keras.models.load_model("files/model.h5")
53 |
54 | """ Load the dataset """
55 | dataset_path = "new_data"
56 | valid_path = os.path.join(dataset_path, "test")
57 | test_x, test_y = load_data(valid_path)
58 | print(f"Test: {len(test_x)} - {len(test_y)}")
59 |
60 | """ Evaluation and Prediction """
61 | SCORE = []
62 | for x, y in tqdm(zip(test_x, test_y), total=len(test_x)):
63 | """ Extract the name """
64 | name = x.split("/")[-1].split(".")[0]
65 |
66 | """ Reading the image """
67 | image = cv2.imread(x, cv2.IMREAD_COLOR)
68 | x = image/255.0
69 | x = np.expand_dims(x, axis=0)
70 |
71 | """ Reading the mask """
72 | mask = cv2.imread(y, cv2.IMREAD_GRAYSCALE)
73 |
74 | """ Prediction """
75 | y_pred = model.predict(x)[0]
76 | y_pred = np.squeeze(y_pred, axis=-1)
77 | y_pred = y_pred > 0.5
78 | y_pred = y_pred.astype(np.int32)
79 |
80 | """ Saving the prediction """
81 | save_image_path = f"results/{name}.png"
82 | save_results(image, mask, y_pred, save_image_path)
83 |
84 | """ Flatten the array """
85 | mask = mask.flatten()
86 | y_pred = y_pred.flatten()
87 |
88 | """ Calculating the metrics values """
89 | acc_value = accuracy_score(mask, y_pred)
90 | f1_value = f1_score(mask, y_pred, labels=[0, 1], average="binary")
91 | jac_value = jaccard_score(mask, y_pred, labels=[0, 1], average="binary")
92 | recall_value = recall_score(mask, y_pred, labels=[0, 1], average="binary")
93 | precision_value = precision_score(mask, y_pred, labels=[0, 1], average="binary")
94 | SCORE.append([name, acc_value, f1_value, jac_value, recall_value, precision_value])
95 |
96 | """ Metrics values """
97 | score = [s[1:]for s in SCORE]
98 | score = np.mean(score, axis=0)
99 | print(f"Accuracy: {score[0]:0.5f}")
100 | print(f"F1: {score[1]:0.5f}")
101 | print(f"Jaccard: {score[2]:0.5f}")
102 | print(f"Recall: {score[3]:0.5f}")
103 | print(f"Precision: {score[4]:0.5f}")
104 |
105 | df = pd.DataFrame(SCORE, columns=["Image", "Accuracy", "F1", "Jaccard", "Recall", "Precision"])
106 | df.to_csv("files/score.csv")
107 |
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/files/README.md:
--------------------------------------------------------------------------------
1 | # files
2 | It contains the following:
3 | 1. data.csv
4 | 2. model.h5
5 | 3. score.csv
6 |
7 | Download the weight files from here: [model.h5](https://drive.google.com/file/d/17QKxSIBFhyJoDps93-sCVHnVV6UWS1sG/view?usp=sharing)
8 |
--------------------------------------------------------------------------------
/files/data.csv:
--------------------------------------------------------------------------------
1 | epoch,dice_coef,iou,loss,lr,precision,recall,val_dice_coef,val_iou,val_loss,val_precision,val_recall
2 | 0,0.9236060380935669,0.8614596724510193,0.0763932317495346,1e-04,0.9160043001174927,0.9380590319633484,0.9534931182861328,0.9122545719146729,0.04655572399497032,0.9438676238059998,0.9629934430122375
3 | 1,0.9488314986228943,0.904120922088623,0.05116594210267067,1e-04,0.9418964982032776,0.9590457677841187,0.9520673155784607,0.9096073508262634,0.047952279448509216,0.9500304460525513,0.9547289609909058
4 | 2,0.9558566212654114,0.9166592359542847,0.04414021968841553,1e-04,0.9500699639320374,0.9645980596542358,0.9521860480308533,0.9101199507713318,0.047862760722637177,0.952200174331665,0.9531099796295166
5 | 3,0.9609637260437012,0.9258140921592712,0.03903239592909813,1e-04,0.9560630321502686,0.9683946967124939,0.9569557309150696,0.9188218712806702,0.04308772832155228,0.948343813419342,0.9654064178466797
6 | 4,0.9648945331573486,0.9330200552940369,0.03510143235325813,1e-04,0.9602518081665039,0.971794605255127,0.9606366157531738,0.9253696799278259,0.03939945995807648,0.9589961171150208,0.9630980491638184
7 | 5,0.9689694046974182,0.9404242634773254,0.031025903299450874,1e-04,0.9648586511611938,0.9749219417572021,0.9609988331794739,0.9259451031684875,0.03903193771839142,0.9564933180809021,0.9650676846504211
8 | 6,0.9708828926086426,0.943962812423706,0.029113758355379105,1e-04,0.9675348997116089,0.9757340550422668,0.96592777967453,0.9348747134208679,0.03410392999649048,0.9649562835693359,0.9667566418647766
9 | 7,0.9747037291526794,0.9510557055473328,0.025292672216892242,1e-04,0.9716162085533142,0.9793248772621155,0.9606902599334717,0.9253950715065002,0.039344802498817444,0.9671708941459656,0.9552639722824097
10 | 8,0.9731919169425964,0.9483922123908997,0.02680276334285736,1e-04,0.9702030420303345,0.977674663066864,0.9639149904251099,0.9315752983093262,0.03612154349684715,0.9654141068458557,0.9632377624511719
11 | 9,0.9760316610336304,0.9536292552947998,0.023963898420333862,1e-04,0.9729712009429932,0.9803910851478577,0.9603624939918518,0.9253203868865967,0.03967543691396713,0.9668949246406555,0.9548359513282776
12 | 10,0.9771482944488525,0.9557695388793945,0.022847160696983337,1e-04,0.9744585752487183,0.9809694886207581,0.9464969038963318,0.9013042449951172,0.05355329066514969,0.9259641766548157,0.9685927629470825
13 | 11,0.9785595536231995,0.9583388566970825,0.021437058225274086,1e-04,0.9761689901351929,0.9821127653121948,0.9622300863265991,0.9282627105712891,0.03780001774430275,0.9521223902702332,0.9730828404426575
14 | 12,0.9790946245193481,0.9593554139137268,0.02090105228126049,1e-04,0.9768486022949219,0.9824462532997131,0.9607579112052917,0.9257270693778992,0.03928377851843834,0.9579097628593445,0.964905321598053
15 | 13,0.9803131818771362,0.9616795778274536,0.01968376711010933,1e-04,0.9783193469047546,0.9834476709365845,0.9665282368659973,0.9360746145248413,0.0335049107670784,0.9687449932098389,0.965411365032196
16 | 14,0.9808645248413086,0.9627286195755005,0.019131800159811974,1e-04,0.9789907932281494,0.9837662577629089,0.9660096764564514,0.9350560307502747,0.034016381949186325,0.9705449938774109,0.963333249092102
17 | 15,0.9819546937942505,0.9647705554962158,0.018042568117380142,1e-04,0.980270504951477,0.9845969080924988,0.966107964515686,0.9353917241096497,0.033921435475349426,0.9647469520568848,0.969424307346344
18 |
--------------------------------------------------------------------------------
/files/score.csv:
--------------------------------------------------------------------------------
1 | ,Image,Accuracy,F1,Jaccard,Recall,Precision
2 | 0,adult-baby-background-bump-41286_0,0.977405548095703,0.982527353556331,0.965654808500768,0.988261824224082,0.976859048434686
3 | 1,adult-caucasian-child-daughter-41316_0,0.983078002929688,0.988819155639571,0.977885569287064,0.982829284912618,0.994882484809754
4 | 2,aircraft-women-fashion-pilot-48797_0,0.986316680908203,0.99148480582651,0.983113404294383,0.990964913113214,0.992005244331704
5 | 3,american-football-football-football-player-quarterback-159819_0,0.976207733154297,0.96303189440055,0.928699628465276,0.975152446343688,0.951208945612084
6 | 4,american-football-referees-american-football-football-referees-decision-making-163454_0,0.975193023681641,0.979694813325298,0.960197817398383,0.976465974941025,0.982945075876242
7 | 5,baby-boy-child-cuddle-40975_0,0.995906829833984,0.997581911889647,0.995175489869878,0.99833560364814,0.996829357269284
8 | 6,baby-child-cute-doll-41307_0,0.897792816162109,0.903377269694729,0.823781273841783,0.994379123365539,0.82763519585558
9 | 7,ball-bauble-christmas-decoration-41516_0,0.9921875,0.99428698951127,0.988638885190609,0.994581080100677,0.993993072791464
10 | 8,ballerina-in-the-city-girl-beautiful-cute-161000_0,0.987728118896484,0.934476648267715,0.877011889742708,0.942210539286154,0.926868686868687
11 | 9,baseball-player-running-sport-163209_0,0.996009826660156,0.993337324993312,0.986762844849405,0.992793481028775,0.993881765110766
12 | 10,bass-guitar-musician-instrument-guitarist-159260_0,0.935325622558594,0.924907208093049,0.860304538413368,0.964063452198482,0.888807545627894
13 | 11,beautiful-cute-dress-eyes-41537_0,0.992671966552734,0.994982958774599,0.99001600765041,0.992083621951283,0.997899291731277
14 | 12,beautiful-face-female-girl-41533_0,0.994579315185547,0.995722726739128,0.991481887771923,0.99595955946553,0.995486006620524
15 | 13,beautiful-woman-picnic-spring-vintage-val_0,0.983081817626953,0.984099554357296,0.968696842863092,0.997267838976893,0.971274495233647
16 | 14,beverage-diet-drink-female-41556_0,0.892543792724609,0.883790774714417,0.791778776499808,0.796411815876934,0.992706344646068
17 | 15,black-and-white-silhouette-girl-playful-463193_0,0.995861053466797,0.987088098439861,0.974505380891959,0.987240829346092,0.986935414782733
18 | 16,black-white-bw-nature-dress-157915_0,0.991313934326172,0.990844427646049,0.98185498330531,0.993052533165691,0.988646120022146
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540 | 538,pexels-photo-91626_0,0.995594024658203,0.975385205549517,0.951953076251092,0.959175119456786,0.992152612182961
541 | 539,pexels-photo-91950_0,0.9881591796875,0.987564800051279,0.975435069919831,0.982432507831244,0.992750996737948
542 | 540,pexels-photo-93140_0,0.99737548828125,0.995925377554042,0.99188382545506,0.994182540527119,0.997674335821161
543 | 541,photographer-ocean-woman-with-camera-center-591670_0,0.954715728759766,0.920628765135762,0.852930609413135,0.963271816540975,0.881601188341956
544 | 542,plank-fitness-muscular-exercising-163437_0,0.992130279541016,0.985760727769687,0.971921276132404,0.974986687784165,0.99677554438861
545 | 543,portrait-child-hands-57449_0,0.994853973388672,0.996949847718491,0.993918245713693,0.994577378374477,0.999333662724549
546 | 544,portrait-of-a-girl-girl-hair-makeup-159072_0,0.9952392578125,0.990810150071428,0.981787668734039,0.982863403944485,0.998886447321535
547 | 545,portrait-studio-female-underwear-104840_0,0.978668212890625,0.970477678759978,0.942648506727929,0.943868224855717,0.998631000239032
548 | 546,portrait-sunset-beauty-young-women_0,0.980236053466797,0.98234771024487,0.965307816957052,0.990368431538234,0.97445586048398
549 | 547,pylon-flight-girl-model-163497_0,0.969898223876953,0.96993389342935,0.941622957247379,0.97088459866208,0.968985048265782
550 | 548,review-viewing-platform-view-sky-160407_0,0.990264892578125,0.986704871060172,0.973758624589978,0.988208162456042,0.985206146419617
551 | 549,smile-color-laugh-black-157907_0,0.989326477050781,0.994188944456444,0.988445035453671,0.989933121848931,0.998481517481321
552 | 550,soccer-football-soccer-player-goal-keeper-159636_0,0.977615356445312,0.972694022280338,0.946839640161981,0.950931688321141,0.995475759596152
553 | 551,softball-player-female-youth-163375_0,0.989154815673828,0.992873990189517,0.98584882181362,0.987746613736559,0.998054876666079
554 | 552,soldier-military-uniform-american_0,0.981147766113281,0.978910984040283,0.9586930901614,0.985555937446297,0.97235503560529
555 | 553,sunglasses-white-dress-fashion-model-157887_0,0.975791931152344,0.982149286646564,0.964924692552162,0.981442545536317,0.982857046345088
556 | 554,tea-summer-pretty-young-woman-vintage-37832_0,0.893501281738281,0.813285001538235,0.685324616771867,0.72357491372129,0.928388200085507
557 | 555,tourist-girl-backpack-vacation-163259_0,0.990325927734375,0.994140372280449,0.98834901499559,0.988976030451532,0.999358932669349
558 | 556,tulips-yellow-blonde-pretty-young-woman-37606_0,0.98577880859375,0.988636294358993,0.977527954429006,0.980340831464342,0.997073344687444
559 | 557,vintage-woman-on-bed-retro-bedroom-37738_0,0.933490753173828,0.873355657410165,0.775183102950278,0.975149637463706,0.790805051302289
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561 | 559,wedding-dresses-character-fashion-individuality-157997_0,0.900753021240234,0.918941317589145,0.850038330518586,0.957039209832959,0.883760509615212
562 | 560,wildernessculture-rei1440project-welltravelled-awesomeearth_0,0.997577667236328,0.94656231591349,0.89854609362518,0.917455138662316,0.977576916391448
563 | 561,woman-poses-e-learning-female-159750_0,0.988040924072266,0.991298023943619,0.982746190127629,0.996834718725402,0.985822494092706
564 | 562,woman-pregnant-pier-belly-54634_0,0.997200012207031,0.997425862020593,0.994864942388011,0.997376892810302,0.997474836039701
565 | 563,women-modeling-style-skin_0,0.981113433837891,0.93627974620008,0.880193587416818,0.888340741464368,0.989687916632656
566 | 564,women-sexy-outdoors-girl-48843_0,0.992816925048828,0.992527748125985,0.985166337117243,0.990982281530318,0.994078042653991
567 | 565,young-girl-ballerina-dance-591679_0,0.994487762451172,0.988787236849252,0.977823137603978,0.981332306507509,0.996356300628655
568 | 566,young-woman-pretty-shooting-157717_0,0.9683837890625,0.973640521337565,0.948634997366056,0.963697720247052,0.98379062787215
569 |
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/metrics.py:
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1 |
2 | import numpy as np
3 | import tensorflow as tf
4 | from tensorflow.keras import backend as K
5 |
6 | def iou(y_true, y_pred):
7 | def f(y_true, y_pred):
8 | intersection = (y_true * y_pred).sum()
9 | union = y_true.sum() + y_pred.sum() - intersection
10 | x = (intersection + 1e-15) / (union + 1e-15)
11 | x = x.astype(np.float32)
12 | return x
13 | return tf.numpy_function(f, [y_true, y_pred], tf.float32)
14 |
15 | smooth = 1e-15
16 | def dice_coef(y_true, y_pred):
17 | y_true = tf.keras.layers.Flatten()(y_true)
18 | y_pred = tf.keras.layers.Flatten()(y_pred)
19 | intersection = tf.reduce_sum(y_true * y_pred)
20 | return (2. * intersection + smooth) / (tf.reduce_sum(y_true) + tf.reduce_sum(y_pred) + smooth)
21 |
22 | def dice_loss(y_true, y_pred):
23 | return 1.0 - dice_coef(y_true, y_pred)
24 |
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/model.py:
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1 |
2 | import os
3 | os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
4 |
5 | from tensorflow.keras.layers import Conv2D, BatchNormalization, Activation, MaxPool2D, Conv2DTranspose, Concatenate, Input
6 | from tensorflow.keras.layers import AveragePooling2D, GlobalAveragePooling2D, UpSampling2D, Reshape, Dense
7 | from tensorflow.keras.models import Model
8 | from tensorflow.keras.applications import ResNet50
9 | import tensorflow as tf
10 |
11 | def SqueezeAndExcite(inputs, ratio=8):
12 | init = inputs
13 | filters = init.shape[-1]
14 | se_shape = (1, 1, filters)
15 |
16 | se = GlobalAveragePooling2D()(init)
17 | se = Reshape(se_shape)(se)
18 | se = Dense(filters // ratio, activation='relu', kernel_initializer='he_normal', use_bias=False)(se)
19 | se = Dense(filters, activation='sigmoid', kernel_initializer='he_normal', use_bias=False)(se)
20 | x = init * se
21 | return x
22 |
23 | def ASPP(inputs):
24 | """ Image Pooling """
25 | shape = inputs.shape
26 | y1 = AveragePooling2D(pool_size=(shape[1], shape[2]))(inputs)
27 | y1 = Conv2D(256, 1, padding="same", use_bias=False)(y1)
28 | y1 = BatchNormalization()(y1)
29 | y1 = Activation("relu")(y1)
30 | y1 = UpSampling2D((shape[1], shape[2]), interpolation="bilinear")(y1)
31 |
32 | """ 1x1 conv """
33 | y2 = Conv2D(256, 1, padding="same", use_bias=False)(inputs)
34 | y2 = BatchNormalization()(y2)
35 | y2 = Activation("relu")(y2)
36 |
37 | """ 3x3 conv rate=6 """
38 | y3 = Conv2D(256, 3, padding="same", use_bias=False, dilation_rate=6)(inputs)
39 | y3 = BatchNormalization()(y3)
40 | y3 = Activation("relu")(y3)
41 |
42 | """ 3x3 conv rate=12 """
43 | y4 = Conv2D(256, 3, padding="same", use_bias=False, dilation_rate=12)(inputs)
44 | y4 = BatchNormalization()(y4)
45 | y4 = Activation("relu")(y4)
46 |
47 | """ 3x3 conv rate=18 """
48 | y5 = Conv2D(256, 3, padding="same", use_bias=False, dilation_rate=18)(inputs)
49 | y5 = BatchNormalization()(y5)
50 | y5 = Activation("relu")(y5)
51 |
52 | y = Concatenate()([y1, y2, y3, y4, y5])
53 | y = Conv2D(256, 1, padding="same", use_bias=False)(y)
54 | y = BatchNormalization()(y)
55 | y = Activation("relu")(y)
56 |
57 | return y
58 |
59 | def deeplabv3_plus(shape):
60 | """ Input """
61 | inputs = Input(shape)
62 |
63 | """ Encoder """
64 | encoder = ResNet50(weights="imagenet", include_top=False, input_tensor=inputs)
65 |
66 | image_features = encoder.get_layer("conv4_block6_out").output
67 | x_a = ASPP(image_features)
68 | x_a = UpSampling2D((4, 4), interpolation="bilinear")(x_a)
69 |
70 | x_b = encoder.get_layer("conv2_block2_out").output
71 | x_b = Conv2D(filters=48, kernel_size=1, padding='same', use_bias=False)(x_b)
72 | x_b = BatchNormalization()(x_b)
73 | x_b = Activation('relu')(x_b)
74 |
75 | x = Concatenate()([x_a, x_b])
76 | x = SqueezeAndExcite(x)
77 |
78 | x = Conv2D(filters=256, kernel_size=3, padding='same', use_bias=False)(x)
79 | x = BatchNormalization()(x)
80 | x = Activation('relu')(x)
81 |
82 | x = Conv2D(filters=256, kernel_size=3, padding='same', use_bias=False)(x)
83 | x = BatchNormalization()(x)
84 | x = Activation('relu')(x)
85 | x = SqueezeAndExcite(x)
86 |
87 | x = UpSampling2D((4, 4), interpolation="bilinear")(x)
88 | x = Conv2D(1, 1)(x)
89 | x = Activation("sigmoid")(x)
90 |
91 | model = Model(inputs, x)
92 | return model
93 |
94 | if __name__ == "__main__":
95 | model = deeplabv3_plus((512, 512, 3))
96 |
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/new_data/README.md:
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1 | # new_data
2 | This folder contains the augmenated data.
3 |
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/people_segmentation/README.md:
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1 | # people_segmentation
2 | The folder contains the dataset. Download the dataset: [Person Segmentation](https://www.kaggle.com/nikhilroxtomar/person-segmentation/download)
3 |
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/predict.py:
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1 |
2 | import os
3 | os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
4 |
5 | import numpy as np
6 | import cv2
7 | import pandas as pd
8 | from glob import glob
9 | from tqdm import tqdm
10 | import tensorflow as tf
11 | from tensorflow.keras.utils import CustomObjectScope
12 | from metrics import dice_loss, dice_coef, iou
13 | from train import create_dir
14 |
15 | """ Global parameters """
16 | H = 512
17 | W = 512
18 |
19 | if __name__ == "__main__":
20 | """ Seeding """
21 | np.random.seed(42)
22 | tf.random.set_seed(42)
23 |
24 | """ Directory for storing files """
25 | create_dir("test_images/mask")
26 |
27 | """ Loading model """
28 | with CustomObjectScope({'iou': iou, 'dice_coef': dice_coef, 'dice_loss': dice_loss}):
29 | model = tf.keras.models.load_model("files/model.h5")
30 |
31 | """ Load the dataset """
32 | data_x = glob("test_images/image/*")
33 |
34 | for path in tqdm(data_x, total=len(data_x)):
35 | """ Extracting name """
36 | name = path.split("/")[-1].split(".")[0]
37 |
38 | """ Reading the image """
39 | image = cv2.imread(path, cv2.IMREAD_COLOR)
40 | h, w, _ = image.shape
41 | x = cv2.resize(image, (W, H))
42 | x = x/255.0
43 | x = x.astype(np.float32)
44 | x = np.expand_dims(x, axis=0)
45 |
46 | """ Prediction """
47 | y = model.predict(x)[0]
48 | y = cv2.resize(y, (w, h))
49 | y = np.expand_dims(y, axis=-1)
50 |
51 | """ Save the image """
52 | masked_image = image * y
53 | line = np.ones((h, 10, 3)) * 128
54 | cat_images = np.concatenate([image, line, masked_image], axis=1)
55 | cv2.imwrite(f"test_images/mask/{name}.png", cat_images)
56 |
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/train.py:
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1 |
2 | import os
3 | os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
4 |
5 | import numpy as np
6 | import cv2
7 | from glob import glob
8 | from sklearn.utils import shuffle
9 | import tensorflow as tf
10 | from tensorflow.keras.callbacks import ModelCheckpoint, CSVLogger, ReduceLROnPlateau, EarlyStopping, TensorBoard
11 | from tensorflow.keras.optimizers import Adam
12 | from tensorflow.keras.metrics import Recall, Precision
13 | from model import deeplabv3_plus
14 | from metrics import dice_loss, dice_coef, iou
15 |
16 | """ Global parameters """
17 | H = 512
18 | W = 512
19 |
20 | """ Creating a directory """
21 | def create_dir(path):
22 | if not os.path.exists(path):
23 | os.makedirs(path)
24 |
25 | def shuffling(x, y):
26 | x, y = shuffle(x, y, random_state=42)
27 | return x, y
28 |
29 | def load_data(path):
30 | x = sorted(glob(os.path.join(path, "image", "*png")))
31 | y = sorted(glob(os.path.join(path, "mask", "*png")))
32 | return x, y
33 |
34 | def read_image(path):
35 | path = path.decode()
36 | x = cv2.imread(path, cv2.IMREAD_COLOR)
37 | x = x/255.0
38 | x = x.astype(np.float32)
39 | return x
40 |
41 | def read_mask(path):
42 | path = path.decode()
43 | x = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
44 | x = x.astype(np.float32)
45 | x = np.expand_dims(x, axis=-1)
46 | return x
47 |
48 | def tf_parse(x, y):
49 | def _parse(x, y):
50 | x = read_image(x)
51 | y = read_mask(y)
52 | return x, y
53 |
54 | x, y = tf.numpy_function(_parse, [x, y], [tf.float32, tf.float32])
55 | x.set_shape([H, W, 3])
56 | y.set_shape([H, W, 1])
57 | return x, y
58 |
59 | def tf_dataset(X, Y, batch=2):
60 | dataset = tf.data.Dataset.from_tensor_slices((X, Y))
61 | dataset = dataset.map(tf_parse)
62 | dataset = dataset.batch(batch)
63 | dataset = dataset.prefetch(10)
64 | return dataset
65 |
66 |
67 | if __name__ == "__main__":
68 | """ Seeding """
69 | np.random.seed(42)
70 | tf.random.set_seed(42)
71 |
72 | """ Directory for storing files """
73 | create_dir("files")
74 |
75 | """ Hyperparameters """
76 | batch_size = 2
77 | lr = 1e-4
78 | num_epochs = 20
79 | model_path = os.path.join("files", "model.h5")
80 | csv_path = os.path.join("files", "data.csv")
81 |
82 | """ Dataset """
83 | dataset_path = "new_data"
84 | train_path = os.path.join(dataset_path, "train")
85 | valid_path = os.path.join(dataset_path, "test")
86 |
87 | train_x, train_y = load_data(train_path)
88 | train_x, train_y = shuffling(train_x, train_y)
89 | valid_x, valid_y = load_data(valid_path)
90 |
91 | print(f"Train: {len(train_x)} - {len(train_y)}")
92 | print(f"Valid: {len(valid_x)} - {len(valid_y)}")
93 |
94 | train_dataset = tf_dataset(train_x, train_y, batch=batch_size)
95 | valid_dataset = tf_dataset(valid_x, valid_y, batch=batch_size)
96 |
97 | """ Model """
98 | model = deeplabv3_plus((H, W, 3))
99 | model.compile(loss=dice_loss, optimizer=Adam(lr), metrics=[dice_coef, iou, Recall(), Precision()])
100 |
101 | callbacks = [
102 | ModelCheckpoint(model_path, verbose=1, save_best_only=True),
103 | ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=5, min_lr=1e-7, verbose=1),
104 | CSVLogger(csv_path),
105 | TensorBoard(),
106 | EarlyStopping(monitor='val_loss', patience=20, restore_best_weights=False),
107 | ]
108 |
109 | model.fit(
110 | train_dataset,
111 | epochs=num_epochs,
112 | validation_data=valid_dataset,
113 | callbacks=callbacks
114 | )
115 |
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