xtreme-vision 1.1

A Python Library for Computer-Vision related Tasks

Xtreme-Vision

Xtreme-Vision is a Python Library which is built with simplicity in mind for Computer Vision Tasks, Currently it provides the solution for only Object-Detection Tasks, it provides the support of a list of state-of-the-art algorithms for Object Detection, Video Object Detection and Training on Custom Datasets. Currently it supports 4 different algorithms. For Detection with pre-trained models it provides:

• RetinaNet
• CenterNet
• YOLOv4
• TinyYOLOv4

For Custom Training It Provides:

• YOLOv4
• TinyYOLOv4

In Future it will not be limited to just Object-Detection, it will provide solution for a wide variety of Computer-Vision Tasks such as Image-Segmentation, Image-Prediction, Auto-Encoders and GANs.

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Dependencies:

• Tensorflow >= 2.3.0
• Keras
• Opencv-python
• Numpy
• Pillow
• Matplotlib
• Pandas
• Scikit-learn
• Progressbar2
• Scipy
• H5Py

Get Started:

!pip install xtreme-vision

RetinaNet Examples

Image Object_Detection Using RetinaNet

from xtreme_vision.Detection import Object_Detection

model = Object_Detection()
model.Use_RetinaNet()
model.Detect_From_Image(input_path='kite.jpg',
                        output_path='./retinanet.jpg', 
                        extract_objects=True)

from PIL import Image
Image.open('retinanet.jpg')

Found Existing Weights File...
Loading Existing File...
WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.

Video Object_Detection Using RetinaNet

model.Use_RetinaNet()
model.Detect_From_Video(input_path = 'road.mp4', 
                        output_path='./retinanet.mp4', 
                        extract_objects=False)

Found Existing Weights File...
Loading Existing File...
WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.

There are 38 Frames in this video
--------------------
Detecting Objects in the Video... Please Wait...
--------------------
Done. Processing has been Finished... Please Check Output Video.

Custom_Image_Object_Detection Using RetinaNet

custom_objects = model.Custom_Objects(kite=True, person=False)

model.Detect_Custom_Objects_From_Image(custom_objects, 
                                       input_path = 'kite.jpg',
                                       output_path = './custom_detection.jpg', 
                                       minimum_percentage_probability = 0.2,
                                       extract_objects=False)

Image.open('custom_detection.jpg')

Custom_Video_Object_Detection Using RetinaNet

custom_obj = model.Custom_Objects(car=True)
model.Detect_Custom_Objects_From_Video(custom_obj, 
                                       input_path = 'road.mp4',
                                       output_path = './custom_video.mp4',
                                       minimum_percentage_probability = 0.2, 
                                       extract_objects=False)

Found Existing Weights File...
Loading Existing File...
WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.

There are 38 Frames in this video
--------------------
Detecting Objects in the Video... Please Wait...
--------------------
Done. Processing has been Finished... Please Check Output Video.

CenterNet

Image Object_Detection Using CenterNet

model.Use_CenterNet()
model.Detect_From_Image(input_path= 'kite.jpg', 
                        output_path='./centernet.jpg')

Image.open('centernet.jpg')

Downloading Weights File...
Please Wait...
Downloading data from https://github.com/Licht-T/tf-centernet/releases/download/v1.0.6/centernet_pretrained_coco.h5
766869504/766861424 [==============================] - 12s 0us/step

Video Object_Detection Using CenterNet

model.Use_CenterNet()
model.Detect_From_Video(input_path= 'road.mp4', 
                        output_path='./centernet.mp4')

Found Existing Weights File...
Loading Existing File...

There are 38 Frames in this video
--------------------
Detecting Objects in the Video... Please Wait...
--------------------
Done. Processing has been Finished... Please Check Output Video.

Cutom_Image_Object_Detection Using CenterNet

custom_objects = model.Custom_Objects(kite=False, person=True)
model.Use_CenterNet()
model.Detect_Custom_Objects_From_Image(custom_objects, 
                                       input_path = 'kite.jpg',
                                       output_path = './custom_detection.jpg')

Image.open('custom_detection.jpg')

Found Existing Weights File...
Loading Existing File...

Cutom_Video_Object_Detection Using CenterNet

custom_obj = model.Custom_Objects(car=False, person=True)

model.Detect_Custom_Objects_From_Video(custom_obj, 
                                       input_path = 'road.mp4',
                                       output_path = './custom_video.mp4')

There are 38 Frames in this video
--------------------
Detecting Objects in the Video... Please Wait...
--------------------
Done. Processing has been Finished... Please Check Output Video.

YOLOv4 Examples

Image Object_Detection Using YOLOv4

model.Use_YOLOv4(iou = 0.45, score = 0.25)
model.Detect_From_Image(input_path= 'kite.jpg', 
                        output_path= './yolov4.jpg')
Image.open('yolov4.jpg')

Downloading weights file...
Please wait...
Downloading data from https://github.com/Adeel-Intizar/Xtreme-Vision/releases/download/1.0/yolov4.weights
257720320/257717640 [==============================] - 8s 0us/step
Downloading Classes File...
Please wait...
Downloading data from https://raw.githubusercontent.com/pjreddie/darknet/master/data/coco.names
8192/625 [=========================================================================================================================================================================================================================================================================================================================================================================================================] - 0s 0us/step

Video Object_Detection Using YOLOv4

model.Use_YOLOv4()
model.Detect_From_Video(input_path= 'road.mp4', 
                        output_path= './yolo.mp4')

Found Existing weights file...
Loading existing file...
Found Existing Classes File...
Loading Existing File...

There are 38 Frames in this video
--------------------
Detecting Objects in the Video... Please Wait...
--------------------
Done. Processing has been Finished... Please Check Output Video.

Cutom_Image_Object_Detection Using YOLOv4

custom_objects = model.Custom_Objects(kite=False, person=True)
model.Use_YOLOv4()
model.Detect_Custom_Objects_From_Image(custom_objects, 
                                       input_path = 'kite.jpg',
                                       output_path = './custom_detection.jpg')

Image.open('custom_detection.jpg')

Found Existing weights file...
Loading existing file...
Found Existing Classes File...
Loading Existing File...

Cutom_Video_Object_Detection Using YOLOv4

custom_obj = model.Custom_Objects(car=True, person=False)
model.Use_YOLOv4()
model.Detect_Custom_Objects_From_Video(custom_obj, 
                                       input_path = 'road.mp4',
                                       output_path = './custom_video.mp4')

Found Existing weights file...
Loading existing file...
Found Existing Classes File...
Loading Existing File...

There are 38 Frames in this video
--------------------
Detecting Objects in the Video... Please Wait...
--------------------
Done. Processing has been Finished... Please Check Output Video.

TinyYOLOv4

Image Object_Detection Using TinyYOLOv4

model.Use_TinyYOLOv4()
model.Detect_From_Image(input_path= 'kite.jpg', 
                        output_path= './tinyyolov4.jpg')
Image.open('tinyyolov4.jpg')

Downloading weights file...
Please wait...
Downloading data from https://github.com/Adeel-Intizar/Xtreme-Vision/releases/download/1.0/yolov4-tiny.weights
24256512/24251276 [==============================] - 0s 0us/step
Found Existing Classes File...
Loading Existing File...

Video Object_Detection Using TinyYOLOv4

model.Use_TinyYOLOv4()
model.Detect_From_Video(input_path= 'road.mp4', 
                        output_path= './tinyyolo.mp4')

Found Existing weights file...
Loading existing file...
Found Existing Classes File...
Loading Existing File...

There are 38 Frames in this video
--------------------
Detecting Objects in the Video... Please Wait...
--------------------
Done. Processing has been Finished... Please Check Output Video.

Cutom_Image_Object_Detection Using TinyYOLOv4

custom_objects = model.Custom_Objects(kite=False, person=True)
model.Use_TinyYOLOv4()
model.Detect_Custom_Objects_From_Image(custom_objects, 
                                       input_path = 'kite.jpg',
                                       output_path = './custom_detection.jpg')

Image.open('custom_detection.jpg')

Found Existing weights file...
Loading existing file...
Found Existing Classes File...
Loading Existing File...

Cutom_Video_Object_Detection Using TinyYOLOv4

custom_obj = model.Custom_Objects(car=True, person=False)
model.Use_TinyYOLOv4()
model.Detect_Custom_Objects_From_Video(custom_obj, 
                                       input_path = 'road.mp4',
                                       output_path = './custom_video.mp4')

Found Existing weights file...
Loading existing file...
Found Existing Classes File...
Loading Existing File...

There are 38 Frames in this video
--------------------
Detecting Objects in the Video... Please Wait...
--------------------
Done. Processing has been Finished... Please Check Output Video.

Training YOLOv4

from xtreme_vision.Detection.Custom import Train_Custom_Detector

model = Train_Custom_Detector()

model.Use_YOLOv4(classes_path = 'classes.names',
                 input_size = 608,
                 batch_size = 4)

model.load_data(train_annot_path = 'training.txt',
                train_img_dir = './',
                val_annot_path = 'validation.txt',
                val_img_dir = './',
                weights_path = None)

model.train(epochs=10,
            lr = 0.001)

Downloading weights file...
Please wait...
Downloading data from https://github.com/Adeel-Intizar/Xtreme-Vision/releases/download/1.0/yolov4.conv.137
170041344/170038676 [==============================] - 6s 0us/step
grid: 76 iou_loss: 0 conf_loss: 11595.1035 prob_loss: 0 total_loss 11595.1035
grid: 38 iou_loss: 0 conf_loss: 3064.89893 prob_loss: 0 total_loss 3064.89893
grid: 19 iou_loss: 5.32760048 conf_loss: 754.432129 prob_loss: 12.6405106 total_loss 772.400208
grid: 76 iou_loss: 0 conf_loss: 11580.3936 prob_loss: 0 total_loss 11580.3936
grid: 38 iou_loss: 0 conf_loss: 3053.11182 prob_loss: 0 total_loss 3053.11182
grid: 19 iou_loss: 2.52579117 conf_loss: 738.940308 prob_loss: 7.94215679 total_loss 749.408264
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/summary_ops_v2.py:1277: stop (from tensorflow.python.eager.profiler) is deprecated and will be removed after 2020-07-01.
Instructions for updating:
use `tf.profiler.experimental.stop` instead.
WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.4511s vs `on_train_batch_end` time: 0.9003s). Check your callbacks.
grid: 76 iou_loss: 0 conf_loss: 11562.1973 prob_loss: 0 total_loss 11562.1973
grid: 38 iou_loss: 0 conf_loss: 3040.45654 prob_loss: 0 total_loss 3040.45654
grid: 19 iou_loss: 3.14010191 conf_loss: 746.206482 prob_loss: 9.11074448 total_loss 758.457275
100/100 - 101s - loss: 13846.6777 - output_1_loss: 10834.2402 - output_2_loss: 2450.5334 - output_3_loss: 510.8344 - val_loss: 14023.4814 - val_output_1_loss: 10223.1758 - val_output_2_loss: 3002.9417 - val_output_3_loss: 746.2946

Training TinyYOLOv4

from xtreme_vision.Detection.Custom import Train_Custom_Detector

model = Train_Custom_Detector()

model.Use_TinyYOLOv4(classes_path = 'classes.names',
                 input_size = 608,
                 batch_size = 4)

model.load_data(train_annot_path = 'training.txt',
                train_img_dir = './',
                val_annot_path = 'validation.txt',
                val_img_dir = './',
                weights_path = None)

model.train(epochs=10,
            lr = 0.001)

Downloading weights file...
Please wait...
Downloading data from https://github.com/Adeel-Intizar/Xtreme-Vision/releases/download/1.0/yolov4-tiny.conv.29
19791872/19789716 [==============================] - 0s 0us/step
grid: 38 iou_loss: 0 conf_loss: 3029.71387 prob_loss: 0 total_loss 3029.71387
grid: 19 iou_loss: 5.33834457 conf_loss: 748.023438 prob_loss: 13.9181871 total_loss 767.279968
grid: 38 iou_loss: 0 conf_loss: 3026.50098 prob_loss: 0 total_loss 3026.50098
grid: 19 iou_loss: 1.93527794 conf_loss: 744.744629 prob_loss: 6.48417282 total_loss 753.164124
WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0785s vs `on_train_batch_end` time: 0.9440s). Check your callbacks.
grid: 38 iou_loss: 0 conf_loss: 3022.73486 prob_loss: 0 total_loss 3022.73486
grid: 19 iou_loss: 2.52412939 conf_loss: 745.861145 prob_loss: 6.20057869 total_loss 754.585815
100/100 - 22s - loss: 3590.1985 - output_1_loss: 2886.6526 - output_2_loss: 698.5931 - val_loss: 3517.7322 - val_output_1_loss: 2858.8828 - val_output_2_loss: 653.8980