From f324a12da007a9f39eb718d23e7349fad4e6f870 Mon Sep 17 00:00:00 2001 From: Navan Chauhan Date: Sun, 19 Jan 2020 13:11:50 +0530 Subject: Publish deploy 2020-01-19 13:11 --- feed.rss | 222 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 221 insertions(+), 1 deletion(-) (limited to 'feed.rss') diff --git a/feed.rss b/feed.rss index 0da058c..b55ce6f 100644 --- a/feed.rss +++ b/feed.rss @@ -1,4 +1,224 @@ -Navan ChauhanWelcome to my personal fragment of the internet.https://navanchauhan.github.io/enSat, 18 Jan 2020 19:19:50 +0530Sat, 18 Jan 2020 19:19:50 +0530250https://navanchauhan.github.io/posts/2020-01-14-Converting-between-PIL-NumPyConverting between image and NumPy arrayShort code snippet for converting between PIL image and NumPy arrays.https://navanchauhan.github.io/posts/2020-01-14-Converting-between-PIL-NumPyTue, 14 Jan 2020 00:10:00 +0530Converting between image and NumPy array
import numpy
+Navan ChauhanWelcome to my personal fragment of the internet.https://navanchauhan.github.io/enSun, 19 Jan 2020 13:11:26 +0530Sun, 19 Jan 2020 13:11:26 +0530250https://navanchauhan.github.io/posts/2020-01-16-Image-Classifier-Using-TuricreateCreating a Custom Image Classifier using Turicreate to detect Smoke and FireTutorial on creating a custom Image Classifier using Turicreate and a dataset from Kagglehttps://navanchauhan.github.io/posts/2020-01-16-Image-Classifier-Using-TuricreateThu, 16 Jan 2020 10:36:00 +0530Creating a Custom Image Classifier using Turicreate to detect Smoke and Fire
For setting up Kaggle with Google Colab, please refer to  my previous post
Dataset
Mounting Google Drive
import os
+from google.colab import drive
+drive.mount('/content/drive')
+

+
+
Downloading Dataset from Kaggle
os.environ['KAGGLE_CONFIG_DIR'] = "/content/drive/My Drive/"
+!kaggle datasets download ashutosh69/fire-and-smoke-dataset
+!unzip "fire-and-smoke-dataset.zip"
+

+
+
Pre-Processing
!mkdir default smoke fire
+

+
+

!ls data/data/img_data/train/default/*.jpg
+

+
+

img_1002.jpg   img_20.jpg     img_519.jpg     img_604.jpg       img_80.jpg
+img_1003.jpg   img_21.jpg     img_51.jpg     img_60.jpg       img_8.jpg
+img_1007.jpg   img_22.jpg     img_520.jpg     img_61.jpg       img_900.jpg
+img_100.jpg    img_23.jpg     img_521.jpg    'img_62 (2).jpg'   img_920.jpg
+img_1014.jpg   img_24.jpg    'img_52 (2).jpg'     img_62.jpg       img_921.jpg
+img_1018.jpg   img_29.jpg     img_522.jpg    'img_63 (2).jpg'   img_922.jpg
+img_101.jpg    img_3000.jpg   img_523.jpg     img_63.jpg       img_923.jpg
+img_1027.jpg   img_335.jpg    img_524.jpg     img_66.jpg       img_924.jpg
+img_102.jpg    img_336.jpg    img_52.jpg     img_67.jpg       img_925.jpg
+img_1042.jpg   img_337.jpg    img_530.jpg     img_68.jpg       img_926.jpg
+img_1043.jpg   img_338.jpg    img_531.jpg     img_700.jpg       img_927.jpg
+img_1046.jpg   img_339.jpg   'img_53 (2).jpg'     img_701.jpg       img_928.jpg
+img_1052.jpg   img_340.jpg    img_532.jpg     img_702.jpg       img_929.jpg
+img_107.jpg    img_341.jpg    img_533.jpg     img_703.jpg       img_930.jpg
+img_108.jpg    img_3.jpg      img_537.jpg     img_704.jpg       img_931.jpg
+img_109.jpg    img_400.jpg    img_538.jpg     img_705.jpg       img_932.jpg
+img_10.jpg     img_471.jpg    img_539.jpg     img_706.jpg       img_933.jpg
+img_118.jpg    img_472.jpg    img_53.jpg     img_707.jpg       img_934.jpg
+img_12.jpg     img_473.jpg    img_540.jpg     img_708.jpg       img_935.jpg
+img_14.jpg     img_488.jpg    img_541.jpg     img_709.jpg       img_938.jpg
+img_15.jpg     img_489.jpg   'img_54 (2).jpg'     img_70.jpg       img_958.jpg
+img_16.jpg     img_490.jpg    img_542.jpg     img_710.jpg       img_971.jpg
+img_17.jpg     img_491.jpg    img_543.jpg    'img_71 (2).jpg'   img_972.jpg
+img_18.jpg     img_492.jpg    img_54.jpg     img_71.jpg       img_973.jpg
+img_19.jpg     img_493.jpg   'img_55 (2).jpg'     img_72.jpg       img_974.jpg
+img_1.jpg      img_494.jpg    img_55.jpg     img_73.jpg       img_975.jpg
+img_200.jpg    img_495.jpg    img_56.jpg     img_74.jpg       img_980.jpg
+img_201.jpg    img_496.jpg    img_57.jpg     img_75.jpg       img_988.jpg
+img_202.jpg    img_497.jpg    img_58.jpg     img_76.jpg       img_9.jpg
+img_203.jpg    img_4.jpg      img_59.jpg     img_77.jpg
+img_204.jpg    img_501.jpg    img_601.jpg     img_78.jpg
+img_205.jpg    img_502.jpg    img_602.jpg     img_79.jpg
+img_206.jpg    img_50.jpg     img_603.jpg     img_7.jpg
+

+
+
The image files are not actually JPEG, thus we first need to save them in the correct format for Turicreate
from PIL import Image
+import glob
+
+
+folders = ["default","smoke","fire"]
+for folder in folders:
+  n = 1
+  for file in glob.glob("./data/data/img_data/train/" + folder + "/*.jpg"):
+    im = Image.open(file)
+    rgb_im = im.convert('RGB')
+    rgb_im.save((folder + "/" + str(n) + ".jpg"), quality=100)
+    n +=1 
+  for file in glob.glob("./data/data/img_data/train/" + folder + "/*.jpg"):
+    im = Image.open(file)
+    rgb_im = im.convert('RGB')
+    rgb_im.save((folder + "/" + str(n) + ".jpg"), quality=100)
+    n +=1
+

+
+

!mkdir train
+!mv default ./train
+!mv smoke ./train
+!mv fire ./train
+

+
+
Making the Image Classifier
Making an SFrame
!pip install turicreate
+

+
+

import turicreate as tc
+import os
+
+data = tc.image_analysis.load_images("./train", with_path=True)
+
+data["label"] = data["path"].apply(lambda path: os.path.basename(os.path.dirname(path)))
+
+print(data)
+
+data.save('fire-smoke.sframe')
+

+
+

+-------------------------+------------------------+
+|           path          |         image          |
++-------------------------+------------------------+
+|  ./train/default/1.jpg  | Height: 224 Width: 224 |
+|  ./train/default/10.jpg | Height: 224 Width: 224 |
+| ./train/default/100.jpg | Height: 224 Width: 224 |
+| ./train/default/101.jpg | Height: 224 Width: 224 |
+| ./train/default/102.jpg | Height: 224 Width: 224 |
+| ./train/default/103.jpg | Height: 224 Width: 224 |
+| ./train/default/104.jpg | Height: 224 Width: 224 |
+| ./train/default/105.jpg | Height: 224 Width: 224 |
+| ./train/default/106.jpg | Height: 224 Width: 224 |
+| ./train/default/107.jpg | Height: 224 Width: 224 |
++-------------------------+------------------------+
+[2028 rows x 2 columns]
+Note: Only the head of the SFrame is printed.
+You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.
++-------------------------+------------------------+---------+
+|           path          |         image          |  label  |
++-------------------------+------------------------+---------+
+|  ./train/default/1.jpg  | Height: 224 Width: 224 | default |
+|  ./train/default/10.jpg | Height: 224 Width: 224 | default |
+| ./train/default/100.jpg | Height: 224 Width: 224 | default |
+| ./train/default/101.jpg | Height: 224 Width: 224 | default |
+| ./train/default/102.jpg | Height: 224 Width: 224 | default |
+| ./train/default/103.jpg | Height: 224 Width: 224 | default |
+| ./train/default/104.jpg | Height: 224 Width: 224 | default |
+| ./train/default/105.jpg | Height: 224 Width: 224 | default |
+| ./train/default/106.jpg | Height: 224 Width: 224 | default |
+| ./train/default/107.jpg | Height: 224 Width: 224 | default |
++-------------------------+------------------------+---------+
+[2028 rows x 3 columns]
+Note: Only the head of the SFrame is printed.
+You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.
+

+
+
Making the Model
import turicreate as tc
+
+# Load the data
+data =  tc.SFrame('fire-smoke.sframe')
+
+# Make a train-test split
+train_data, test_data = data.random_split(0.8)
+
+# Create the model
+model = tc.image_classifier.create(train_data, target='label')
+
+# Save predictions to an SArray
+predictions = model.predict(test_data)
+
+# Evaluate the model and print the results
+metrics = model.evaluate(test_data)
+print(metrics['accuracy'])
+
+# Save the model for later use in Turi Create
+model.save('fire-smoke.model')
+
+# Export for use in Core ML
+model.export_coreml('fire-smoke.mlmodel')
+

+
+

Performing feature extraction on resized images...
+Completed   64/1633
+Completed  128/1633
+Completed  192/1633
+Completed  256/1633
+Completed  320/1633
+Completed  384/1633
+Completed  448/1633
+Completed  512/1633
+Completed  576/1633
+Completed  640/1633
+Completed  704/1633
+Completed  768/1633
+Completed  832/1633
+Completed  896/1633
+Completed  960/1633
+Completed 1024/1633
+Completed 1088/1633
+Completed 1152/1633
+Completed 1216/1633
+Completed 1280/1633
+Completed 1344/1633
+Completed 1408/1633
+Completed 1472/1633
+Completed 1536/1633
+Completed 1600/1633
+Completed 1633/1633
+PROGRESS: Creating a validation set from 5 percent of training data. This may take a while.
+          You can set ``validation_set=None`` to disable validation tracking.
+
+Logistic regression:
+--------------------------------------------------------
+Number of examples          : 1551
+Number of classes           : 3
+Number of feature columns   : 1
+Number of unpacked features : 2048
+Number of coefficients      : 4098
+Starting L-BFGS
+--------------------------------------------------------
++-----------+----------+-----------+--------------+-------------------+---------------------+
+| Iteration | Passes   | Step size | Elapsed Time | Training Accuracy | Validation Accuracy |
++-----------+----------+-----------+--------------+-------------------+---------------------+
+| 0         | 6        | 0.018611  | 0.891830     | 0.553836          | 0.560976            |
+| 1         | 10       | 0.390832  | 1.622383     | 0.744681          | 0.792683            |
+| 2         | 11       | 0.488541  | 1.943987     | 0.733075          | 0.804878            |
+| 3         | 14       | 2.442703  | 2.512545     | 0.727917          | 0.841463            |
+| 4         | 15       | 2.442703  | 2.826964     | 0.861380          | 0.853659            |
+| 9         | 28       | 2.340435  | 5.492035     | 0.941328          | 0.975610            |
++-----------+----------+-----------+--------------+-------------------+---------------------+
+Performing feature extraction on resized images...
+Completed  64/395
+Completed 128/395
+Completed 192/395
+Completed 256/395
+Completed 320/395
+Completed 384/395
+Completed 395/395
+0.9316455696202531
+

+
+
We just got an accuracy of 94% on Training Data and 97% on Validation Data!
]]>https://navanchauhan.github.io/posts/2020-01-15-Setting-up-Kaggle-to-use-with-ColabSetting up Kaggle to use with Google ColabTutorial on setting up kaggle, to use with Google Colabhttps://navanchauhan.github.io/posts/2020-01-15-Setting-up-Kaggle-to-use-with-ColabWed, 15 Jan 2020 23:36:00 +0530Setting up Kaggle to use with Google Colab
In order to be able to access Kaggle Datasets, you will need to have an account on Kaggle (which is Free)
Grabbing Our Tokens
Go to Kaggle
Click on your User Profile and Click on My Account
Scroll Down untill you see Create New API Token
This will download your token as a JSON file
Copy the File to the root folder of your Google Drive
Setting up Colab
Mounting Google Drive
import os
+from google.colab import drive
+drive.mount('/content/drive')
+

+
+
After this click on the URL in the output section, login and then paste the Auth Code
Configuring Kaggle
os.environ['KAGGLE_CONFIG_DIR'] = "/content/drive/My Drive/"
+

+
+
Voila! You can now download kaggel datasets
]]>https://navanchauhan.github.io/posts/2020-01-14-Converting-between-PIL-NumPyConverting between image and NumPy arrayShort code snippet for converting between PIL image and NumPy arrays.https://navanchauhan.github.io/posts/2020-01-14-Converting-between-PIL-NumPyTue, 14 Jan 2020 00:10:00 +0530Converting between image and NumPy array
import numpy
 import PIL
 
 # Convert PIL Image to NumPy array
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