add reasoning_visualization_experience.md doc

Author: hellowaywewe

docs/en/source/_static/tinyms_visulization_origin.jpg

@@ -268,7 +268,7 @@ detection_bbox_data = object_detection_predict(input, detector, is_training=Fals
 # 5.Draw the box for the input image and and view it using OpenCV.
 detection_image_np = visualize_boxes_on_image(image_np, detection_bbox_data, box_color=(0, 255, 0),
                                               box_thickness=3, text_font=cv2.FONT_HERSHEY_PLAIN,
-                                              font_scale=3, text_color=(0, 0, 255), font_size=3, show_scores=True)
+                                              font_scale=2, text_color=(0, 0, 255), font_size=3, show_scores=True)
 cv2.imshow('object detection image', cv2.resize(detection_image_np, (600, 1000)))
 cv2.waitKey(0)
 ```
@@ -301,7 +301,7 @@ while True:
     # 5.Draw the box for the input frame image and view it using OpenCV.
     detection_image_np = visualize_boxes_on_image(image_np, detection_bbox_data, box_color=(0, 255, 0),
                                                   box_thickness=3, text_font=cv2.FONT_HERSHEY_PLAIN,
-                                                  font_scale=3, text_color=(0, 0, 255), font_size=3, show_scores=True)
+                                                  font_scale=2, text_color=(0, 0, 255), font_size=3, show_scores=True)
     cv2.imshow('object detection camera', cv2.resize(detection_image_np, (800, 600)))
 
     if cv2.waitKey(25) & 0xFF == ord('q'):

docs/en/source/_static/tinyms_visulization_reasoning.jpg

@@ -16,6 +16,7 @@ designed to providing quick-start guidelines for machine learning beginners.
    quickstart/overview
    quickstart/install
    quickstart/quickstart_in_one_minute.ipynb
+   quickstart/reasoning_visualization_experience
 
 .. toctree::
    :maxdepth: 2

docs/en/source/_static/tinyms_web_index.jpg

@@ -0,0 +1,57 @@
+# TinyMS Reasoning Visualization Experience
+
+Combined with OpenCV image vision library, TinyMS V0.3.0 focus visualization features. Through simple and intuitive image visualization, it helps users to understand the effect of model reasoning more quickly.
+
+For users who do not want to write code, TinyMS provides a visual interface of WEB UI. Users only need to upload the image to be inferred on the browser page to experience easily. Currently, it supports for `LeNet5`, `CycleGan` and `SSD300` models.
+
+### WEB UI reasoning visualization
+
+Users need to deploy the visual server first, details please see [TinyMS Nginx Verion Installation](https://tinyms.readthedocs.io/en/latest/quickstart/install.html). After the server is successfully deployed, the home page and reasoning effect page (taking `CycleGan` model as an example) presented by the browser are as follows:
+
+![Index Page](../_static/tinyms_web_index.jpg)
+
+![Predict Page](../_static/tinyms_web_reasoning.jpg)
+
+For users who want to run code, TinyMS provides model reasoning visualization module, which only needs `5` step code to experience quickly. Currently, it only supports `SSD300` object detection model.
+
+### Model reasoning visualization module application
+
+If users need to experience the model reasoning visualization module application for the first time, they can download code from [TinyMS Official Repo](https://github.com/tinyms-ai/tinyms), then do the following operations:
+
+* Static image detection
+
+Note: Please make sure your system has a visual desktop.
+
+```script
+# Download the TinyMS project
+git clone https://github.com/tinyms-ai/tinyms.git
+cd tinyms/tests/st/app/object_detection/
+# Run static image detection
+python opencv_image_app.py
+```
+
+The image to be detected and the image after inference are shown as follows:
+
+![Input Image](../_static/tinyms_visulization_origin.jpg)
+
+![Reasoning Image](../_static/tinyms_visulization_reasoning.jpg)
+
+* Real-time dynamic detection of video images collected by computer camera
+
+Note: In addition to ensuring that your system has a visual desktop, you also need to have a camera.
+
+If using the container to run code, the camera of the host must be mounted to the container. For example, the host is Ubuntu 18.04 with a camera device and a visual desktop. Perform from step 1 to the final.  If using the host server, perform from step 4 to the final.
+
+```script
+# 1.Install xServer on the host and set permissions
+apt install x11-xserver-utils
+# 2.Allow all users to access the display interface
+xhost +
+# 3.Run container
+docker run -it --rm --device=/dev/video0 -e DISPLAY=unix$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix tinyms/tinyms:0.3.0 /bin/bash
+# 4.Download the TinyMS project in the container
+git clone https://github.com/tinyms-ai/tinyms.git
+cd tinyms/tests/st/app/object_detection/
+# 5.Run dynamic video image detection collected by camera
+python opencv_camera_app.py
+```