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Analyse screen #315

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abb4601
updated the requirements.txt
deepthi1107 Mar 4, 2023
ba73896
added rotation of images as data augmentation process
deepthi1107 Mar 5, 2023
f1e6cf9
Update README.md
deepthi1107 Mar 6, 2023
81e6ccf
Train of yolov5 model
deepthi1107 Mar 18, 2023
34b19dd
added android code pf survey_screen with xml
deepthi1107 Jul 31, 2023
564ea46
added android code of analyse_screen with xml
deepthi1107 Jul 31, 2023
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203 changes: 138 additions & 65 deletions DataCollection/ImageAugmentation.ipynb
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169 changes: 169 additions & 0 deletions Object Detection/objectDetection_train.ipynb
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"metadata": {
"id": "wbvMlHd_QwMG",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "fd0147bf-9e26-41de-eb98-fd0c58c361cd"
},
"source": [
"!git clone https://github.com/ultralytics/yolov5 \n",
"%cd yolov5\n",
"%pip install -qr requirements.txt \n",
"\n",
"import torch\n",
"import utils\n",
"display = utils.notebook_init() "
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"YOLOv5 🚀 v7.0-120-g3e55763 Python-3.9.16 torch-1.13.1+cu116 CPU\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Setup complete ✅ (2 CPUs, 12.7 GB RAM, 25.5/107.7 GB disk)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# traing the YOLOV5 model\n",
"\n",
"!python train.py --img 640 --batch 16 --epochs 3 --data /content/yolov5/data/data.yaml --weights yolov5s.pt "
],
"metadata": {
"id": "JIvXqot4f8d1",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "86e52852-2746-4466-ea66-e1f3e46c2f45"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=/content/yolov5/data/data.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=3, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n",
"\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
"YOLOv5 🚀 v7.0-120-g3e55763 Python-3.9.16 torch-1.13.1+cu116 CPU\n",
"\n",
"\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
"\u001b[34m\u001b[1mClearML: \u001b[0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML\n",
"\u001b[34m\u001b[1mComet: \u001b[0mrun 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet\n",
"\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
"2023-03-15 11:20:33.924337: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n",
"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
"2023-03-15 11:20:35.565625: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.9/dist-packages/cv2/../../lib64:/usr/local/lib/python3.9/dist-packages/cv2/../../lib64:/usr/local/nvidia/lib:/usr/local/nvidia/lib64\n",
"2023-03-15 11:20:35.565860: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.9/dist-packages/cv2/../../lib64:/usr/local/lib/python3.9/dist-packages/cv2/../../lib64:/usr/local/nvidia/lib:/usr/local/nvidia/lib64\n",
"2023-03-15 11:20:35.565890: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n",
"Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt to yolov5s.pt...\n",
"100% 14.1M/14.1M [00:00<00:00, 80.7MB/s]\n",
"\n",
"Overriding model.yaml nc=80 with nc=52\n",
"\n",
" from n params module arguments \n",
" 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] \n",
" 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n",
" 2 -1 1 18816 models.common.C3 [64, 64, 1] \n",
" 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n",
" 4 -1 2 115712 models.common.C3 [128, 128, 2] \n",
" 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n",
" 6 -1 3 625152 models.common.C3 [256, 256, 3] \n",
" 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n",
" 8 -1 1 1182720 models.common.C3 [512, 512, 1] \n",
" 9 -1 1 656896 models.common.SPPF [512, 512, 5] \n",
" 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n",
" 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
" 12 [-1, 6] 1 0 models.common.Concat [1] \n",
" 13 -1 1 361984 models.common.C3 [512, 256, 1, False] \n",
" 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n",
" 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
" 16 [-1, 4] 1 0 models.common.Concat [1] \n",
" 17 -1 1 90880 models.common.C3 [256, 128, 1, False] \n",
" 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] \n",
" 19 [-1, 14] 1 0 models.common.Concat [1] \n",
" 20 -1 1 296448 models.common.C3 [256, 256, 1, False] \n",
" 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] \n",
" 22 [-1, 10] 1 0 models.common.Concat [1] \n",
" 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] \n",
" 24 [17, 20, 23] 1 153729 models.yolo.Detect [52, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
"Model summary: 214 layers, 7159873 parameters, 7159873 gradients, 16.4 GFLOPs\n",
"\n",
"Transferred 343/349 items from yolov5s.pt\n",
"\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
"\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/drive/MyDrive/data_imageClassification/object_detection/train/labels.cache... 3619 images, 117 backgrounds, 0 corrupt: 100% 3736/3736 [00:00<?, ?it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/drive/MyDrive/data_imageClassification/object_detection/valid/images/labels.cache... 4000 images, 33 backgrounds, 0 corrupt: 100% 4033/4033 [00:00<?, ?it/s]\n",
"\n",
"\u001b[34m\u001b[1mAutoAnchor: \u001b[0m5.99 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
"Plotting labels to runs/train/exp/labels.jpg... \n",
"Image sizes 640 train, 640 val\n",
"Using 2 dataloader workers\n",
"Logging results to \u001b[1mruns/train/exp\u001b[0m\n",
"Starting training for 3 epochs...\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 0/2 0G 0.08917 0.04332 0.09752 43 640: 100% 234/234 [1:32:34<00:00, 23.74s/it]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 127/127 [36:36<00:00, 17.30s/it]\n",
" all 4033 15159 0.00765 0.571 0.00917 0.00362\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 1/2 0G 0.04627 0.03401 0.0956 42 640: 100% 234/234 [1:32:10<00:00, 23.64s/it]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 127/127 [33:53<00:00, 16.01s/it]\n",
" all 4033 15159 0.0113 0.895 0.0142 0.0071\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 2/2 0G 0.03428 0.03075 0.09459 33 640: 100% 234/234 [1:32:29<00:00, 23.72s/it]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 127/127 [35:07<00:00, 16.59s/it]\n",
" all 4033 15159 0.0116 0.919 0.0149 0.00975\n",
"\n",
"3 epochs completed in 6.383 hours.\n",
"Optimizer stripped from runs/train/exp/weights/last.pt, 14.7MB\n",
"Optimizer stripped from runs/train/exp/weights/best.pt, 14.7MB\n",
"\n",
"Validating runs/train/exp/weights/best.pt...\n",
"Fusing layers... \n",
"Model summary: 157 layers, 7150369 parameters, 0 gradients, 16.2 GFLOPs\n",
" Class Images Instances P R mAP50 mAP50-95: 61% 78/127 [22:11<14:06, 17.28s/it]"
]
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "-GdpCP0TItC5"
},
"execution_count": null,
"outputs": []
}
]
}
11 changes: 7 additions & 4 deletions README.md
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<img height='175' src="https://user-images.githubusercontent.com/37406965/51083189-d5dc3a80-173b-11e9-8ca0-28015e0893ac.png" align="left" hspace="1" vspace="1">
<img height='90' src="https://user-images.githubusercontent.com/37406965/51083189-d5dc3a80-173b-11e9-8ca0-28015e0893ac.png" align="left" hspace="1" vspace="1">

# PPI-Vision :iphone:
The **Poverty Probability Index (PPI)** is a poverty measurement tool for organizations and businesses with a mission to serve the poor. Taking this into account Vision PPI is designed to facilitate this measurement process much accurate and efficient.
Vision PPI is a computer vision and machine learning based android app designed to help in filling out the PPI Survey. There are two broad aspects to this project - The android app which provides the interface to the field officer for conducting the survey and the machine learning models which are used in the backend of app to analyze the images captured.
The **Poverty Probability Index (PPI)** is a poverty measurement tool for organizations and businesses with a mission to serve the poor. Taking this into account Vision PPI is designed to facilitate this measurement process much accurate and efficient. Vision PPI is a computer vision and machine learning based android app designed to help in filling out PPI Survey.

*There are two broad aspects to this project:-*
- The android app which provides the interface to the field officer for conducting the survey.
- The machine learning models which are used in the backend of app to analyze the images captured.


## Wiki 📙
For more information about the usecases of the project and details about PPI, the API used and demo credentials, please take a look at the [project wiki](https://github.com/openMF/ppi-vision/wiki).
For more information about the usecases of the project and details about PPI, the API used and demo credentials, please take a look at the **[project wiki](https://github.com/openMF/ppi-vision/wiki)**.

## Screenshots :camera:
![Untitled design (5)](https://user-images.githubusercontent.com/75531664/189319200-f7f3b143-2757-46c8-9342-0021fada2ca2.png)
Expand Down
184 changes: 184 additions & 0 deletions analyse_screen/MainActivity.kt
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package com.example.analyse_screen

import android.Manifest.permission.CAMERA
import android.Manifest.permission_group.CAMERA
import android.app.Activity
import android.Manifest
import android.content.Intent
import android.content.pm.PackageManager
import android.graphics.Bitmap
import android.graphics.BitmapFactory
import android.hardware.SensorPrivacyManager.Sensors.CAMERA
import android.media.MediaRecorder.VideoSource.CAMERA
import android.os.Bundle
import android.os.Environment
import android.provider.MediaStore
import android.view.View
import android.widget.Button
import android.widget.ImageView
import androidx.appcompat.app.AppCompatActivity
import androidx.core.app.ActivityCompat
import androidx.core.content.ContextCompat
import androidx.fragment.app.Fragment
import androidx.fragment.app.FragmentTransaction
import java.io.File
import java.io.FileOutputStream
import java.io.IOException



class MainActivity : AppCompatActivity() {



private val REQUEST_IMAGE_CAPTURE = 1
private val REQUEST_IMAGE_GALLERY = 2



private lateinit var imageView: ImageView



override fun onCreate(savedInstanceState: Bundle?) {
super.onCreate(savedInstanceState)
setContentView(R.layout.activity_main)



imageView = findViewById(R.id.imageView2)
val cameraButton = findViewById<ImageView>(R.id.cameraButton)
val galleryButton = findViewById<ImageView>(R.id.galleryButton)
val analyseButton = findViewById<Button>(R.id.analyseButton)



// Check camera permission before opening the camera
cameraButton.setOnClickListener {
if (ContextCompat.checkSelfPermission(this, Manifest.permission.CAMERA) == PackageManager.PERMISSION_GRANTED) {
// Permission already granted, proceed with camera operation
dispatchTakePictureIntent()
} else {
// Request camera permission
ActivityCompat.requestPermissions(this, arrayOf(Manifest.permission.CAMERA), REQUEST_IMAGE_CAPTURE)
}
}



galleryButton.setOnClickListener {
dispatchGalleryIntent()
}



analyseButton.setOnClickListener {
onAnalysisButtonClick()
}
}



private fun dispatchTakePictureIntent() {
Intent(MediaStore.ACTION_IMAGE_CAPTURE).also { takePictureIntent ->
takePictureIntent.resolveActivity(packageManager)?.also {
startActivityForResult(takePictureIntent, REQUEST_IMAGE_CAPTURE)
}
}
}



private fun dispatchGalleryIntent() {
Intent(Intent.ACTION_PICK, MediaStore.Images.Media.EXTERNAL_CONTENT_URI).also { galleryIntent ->
galleryIntent.type = "image/*"
startActivityForResult(galleryIntent, REQUEST_IMAGE_GALLERY)
}
}



override fun onActivityResult(requestCode: Int, resultCode: Int, data: Intent?) {
super.onActivityResult(requestCode, resultCode, data)



if (resultCode == Activity.RESULT_OK) {
when (requestCode) {
REQUEST_IMAGE_CAPTURE -> {
val imageBitmap = data?.extras?.get("data") as Bitmap?
if (imageBitmap != null) {
// Save the full-size image to a file
val imageFile = saveImageToFile(imageBitmap)



// Load and display the full-size image into the ImageView
if (imageFile != null) {
val fullSizeBitmap = BitmapFactory.decodeFile(imageFile.path)
imageView.setImageBitmap(fullSizeBitmap)
} else {
// Handle the case when the image couldn't be saved or loaded
}
} else {
// Handle the case when imageBitmap is null (e.g., user canceled the capture)
}
}
REQUEST_IMAGE_GALLERY -> {
val imageUri = data?.data
val imageBitmap = MediaStore.Images.Media.getBitmap(this.contentResolver, imageUri)
imageView.setImageBitmap(imageBitmap)
}
}
}
}



private fun saveImageToFile(imageBitmap: Bitmap): File? {
return try {
// Create a file to save the image
val storageDir = getExternalFilesDir(Environment.DIRECTORY_PICTURES)
val imageFile = File.createTempFile(
"JPEG_${System.currentTimeMillis()}_", /* prefix */
".jpg", /* suffix */
storageDir /* directory */
)



// Save the bitmap to the file
val outputStream = FileOutputStream(imageFile)
imageBitmap.compress(Bitmap.CompressFormat.JPEG, 100, outputStream)
outputStream.flush()
outputStream.close()



imageFile
} catch (e: IOException) {
e.printStackTrace()
null
}
}




private fun onAnalysisButtonClick() {
println("Analyse")
}



private fun openFragment(fragment: Fragment) {
val transaction: FragmentTransaction = supportFragmentManager.beginTransaction()
transaction.replace(R.id.fragmentContainer, fragment)
transaction.addToBackStack(null)
transaction.commit()
}




}
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