Zmiana readme, poprzednie bylo od YOLO module

README.md

@@ -1,43 +1,11 @@
-# Object detection using deep learning with OpenCV and Python 
+# Moduł rozpoznawania kota na zdjęciu
 
-OpenCV `dnn` module supports running inference on pre-trained deep learning models from popular frameworks like Caffe, Torch and TensorFlow. 
+Moduł dostaje ścieżkę niezależną do zdjęcia i na podstawie tego zdjęcie podaje JSONa czy jest kot czy go też nie ma
 
-When it comes to object detection, popular detection frameworks are
+## Zależności
- * YOLO
- * SSD
- * Faster R-CNN
- 
- Support for running YOLO/DarkNet has been added to OpenCV dnn module recently. 
- 
- ## Dependencies
   * opencv
   * numpy
+  * flask
   
-`pip install numpy opencv-python`
+`pip3 install numpy opencv-python flask`
 
-**Note: Compatability with Python 2.x is not officially tested.**
-
- ## YOLO (You Only Look Once)
- 
- Download the pre-trained YOLO v3 weights file from this [link](https://pjreddie.com/media/files/yolov3.weights) and place it in the current directory or you can directly download to the current directory in terminal using
- 
- `$ wget https://pjreddie.com/media/files/yolov3.weights`
- 
- Provided all the files are in the current directory, below command will apply object detection on the input image `dog.jpg`.
- 
- `$ python yolo_opencv.py --image dog.jpg --config yolov3.cfg --weights yolov3.weights --classes yolov3.txt`
- 
- 
- **Command format** 
- 
- _$ python yolo_opencv.py --image /path/to/input/image --config /path/to/config/file --weights /path/to/weights/file --classes /path/to/classes/file_
- 
- Checkout the [blog post](http://www.arunponnusamy.com/yolo-object-detection-opencv-python.html) to learn more.
- 
- ### sample output :
- ![](object-detection.jpg)
- 
-Checkout the object detection implementation available in [cvlib](http:cvlib.net) which enables detecting common objects in the context through a single function call `detect_common_objects()`.
- 
- 
- (_SSD and Faster R-CNN examples will be added soon_)