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Data augmentation #304

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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
21d4c31
added data augmentation techniques
deepthi1107 Apr 10, 2023
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89 changes: 87 additions & 2 deletions DataCollection/GenerateImages.py
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Original file line number Diff line number Diff line change
Expand Up @@ -163,7 +163,7 @@ def _decay_value_radically_norm(x, centers, max_value, min_value, dev):
cv2.imwrite(savePath, flip)

# Changing brightness out an image
for num in range (0, 5):
for num in range (0, 6):
gamma = float(decimal.Decimal(random.randrange(10, 1000))/100)
invGamma = 1.0 / gamma
table = np.array([((i / 255.0) ** invGamma) * 255
Expand All @@ -186,7 +186,7 @@ def _decay_value_radically_norm(x, centers, max_value, min_value, dev):

l, a, b = cv2.split(lab)

for num in range(0, 5):
for num in range(0, 6):
value = float(decimal.Decimal(random.randrange(10, 1000))/100)
clahe = cv2.createCLAHE(clipLimit=value, tileGridSize=(8,8))
cl = clahe.apply(l)
Expand Down Expand Up @@ -243,6 +243,91 @@ def _decay_value_radically_norm(x, centers, max_value, min_value, dev):
i += 1
cv2.imwrite(savePath, median)

# rotation of image to 90^
imgRot90=cv2.rotate(image,cv2.ROTATE_90_CLOCKWISE)
savePath = output + str(i) + ".png"
i += 1
cv2.imwrite(savePath, imgRot90)

# rotation of image to 180^
imgRot180=cv2.rotate(image,cv2.ROTATE_180)
savePath = output + str(i) + ".png"
i += 1
cv2.imwrite(savePath, imgRot180)

# rotation of image to 270^
imgRot270=cv2.rotate(image,cv2.ROTATE_90_COUNTERCLOCKWISE)
savePath = output + str(i) + ".png"
i += 1
cv2.imwrite(savePath, imgRot270)

# bgr image
bgrImg=cv2.imread(path)
savePath = output + str(i) + ".png"
i += 1
cv2.imwrite(savePath, bgrImg)

# rgb image
imgRGB=cv2.imread(path,cv2.COLOR_BAYER_BG2RGB)
savePath = output + str(i) + ".png"
i += 1
cv2.imwrite(savePath, imgRGB)

# lab image
imgLAB=cv2.imread(path,cv2.COLOR_BGR2LAB)
savePath = output + str(i) + ".png"
i += 1
cv2.imwrite(savePath, imgLAB)

# xyz image
imgXYZ=cv2.imread(path,cv2.COLOR_BGR2XYZ)
savePath = output + str(i) + ".png"
i += 1
cv2.imwrite(savePath, imgXYZ)

# center crop
height, width = image.shape[:2]
crop_width = crop_height = min(width, height)
center_x = int(width/2)
center_y = int(height/2)
x = center_x - int(crop_width/2)
y = center_y - int(crop_height/2)
w = h = crop_width
cropImg=image[y:y+h, x:x+w]
savePath = output + str(i) + ".png"
i += 1
cv2.imwrite(savePath, cropImg)

# mean blur of image
kernel_size = 3
mean_filtered = cv2.blur(image, (kernel_size, kernel_size))
savePath = output + str(i) + ".png"
i += 1
cv2.imwrite(savePath, mean_filtered)

#median blur
kernel_size = 3
median_filtered = cv2.medianBlur(image, kernel_size)
savePath = output + str(i) + ".png"
i += 1
cv2.imwrite(savePath, median_filtered)

#interpolation of image
height,width,ch=image.shape
dim=[height//2,width//2]
imgAreaResize=cv2.resize(image,dim,interpolation=cv2.INTER_AREA)
savePath = output + str(i) + ".png"
i += 1
cv2.imwrite(savePath, imgAreaResize)

#interpolation 2nd technique
height,width,ch=image.shape
dim=[height//2,width//2]
imgCubicResize=cv2.resize(image,dim,interpolation=cv2.INTER_CUBIC)
savePath = output + str(i) + ".png"
i += 1
cv2.imwrite(savePath, imgCubicResize)

# Bilateral Filtering
blur = cv2.bilateralFilter(image,9,75,75)
savePath = output + str(i) + ".png"
Expand Down
442 changes: 372 additions & 70 deletions DataCollection/ImageAugmentation.ipynb
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169 changes: 169 additions & 0 deletions Object Detection/objectDetection_train.ipynb
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@@ -0,0 +1,169 @@
{
"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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@@ -1,12 +1,15 @@
<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)
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23 changes: 12 additions & 11 deletions requirements.txt
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@@ -1,12 +1,13 @@
numpy==1.19.2
matplotlib==3.3.2
numpy==1.22.0
matplotlib==3.6.3
sklearn==0.0
torch==1.6.0
torchvision==0.7.0
opencv-contrib-python==4.3.0.36
opencv-python==4.3.0.36
scikit-image==0.17.2
scikit-learn==0.23.2
scipy==1.5.2
Pillow==7.2.0
pyflakes==2.2.0
torch==1.13.1
torchvision==0.14.1
opencv-contrib-python==4.7.0.68
opencv-python==4.7.0.68
scikit-image==0.19.3
scikit-learn==0.24.2
scipy==1.10.0
Pillow==9.0.1
pyflakes==2.5.0