optimize doc display var and update python usage doc

Author: Neutree

README.md

@@ -44,15 +44,15 @@ from maix import camera, display, image, nn
 
 classifier = nn.Classifier(model="/root/models/mobilenetv2.mud")
 cam = camera.Camera(classifier.input_width(), classifier.input_height(), classifier.input_format())
-dis = display.Display()
+disp = display.Display()
 
 while 1:
     img = cam.read()
     res = classifier.classify(img)
     max_idx, max_prob = res[0]
     msg = f"{max_prob:5.2f}: {classifier.labels[max_idx]}"
     img.draw_string(10, 10, msg, image.COLOR_RED)
-    dis.show(img)
+    disp.show(img)
 ```
 
 Result video:

docs/doc/en/basic/app.md

@@ -69,3 +69,8 @@ install_app(pkg_path)
 - Since touchscreens are standard, it is recommended to create a simple interface with touch interaction. You can refer to examples for implementation methods.
 - Avoid making interfaces and buttons too small, as MaixCAM default screen is 2.3 inches with 552x368 resolution and high PPI. Make sure fingers can easily tap without making mistakes.
 - Implement a simple serial interaction for the main functionality of each application based on the [serial protocol](https://github.com/sipeed/MaixCDK/blob/master/docs/doc/convention/protocol.md) (see [example](https://github.com/sipeed/MaixPy/tree/main/examples/communication/protocol)). This way, users can directly use it as a serial module. For instance, in a face detection application, you can output coordinates via serial port when a face is detected.
+
+## APP power on auto start
+
+Refer to [APP auto start on power up](./auto_start.md).
+

docs/doc/en/basic/python.md

@@ -60,7 +60,7 @@ After mastering the basic syntax, you will be able to use MaixPy for programming
 
 ### Using Built-in Packages
 
-Python comes with many commonly used packages and APIs built-in, so if you encounter any issues, you can search for “Python using xxxx” and you might find a solution right away. This applies to various common tasks, such as file handling, networking, system operations, algorithms, and more.
+Python comes with many commonly used packages and APIs built-in, so if you encounter any issues, you can search for “Python using xxxx” and you might find a solution right away. This applies to various common tasks, such as file handling, multi thread, multi process, networking, system operations, algorithms, and more.
 
 For example:
 For those who are new to Python and have only dabbled in basic microcontroller development, they might wonder why there are no examples in the documentation for reading and writing to SD/TF cards. The reason is that a file system is already running on the SD/TF card by default, so you can use Python’s file handling APIs to read and write files directly on the SD card:

docs/doc/en/comm/maix_protocol.md

@@ -55,7 +55,7 @@ while not app.need_exit():
         img.draw_rect(obj.x, obj.y, obj.w, obj.h, color = image.COLOR_RED)
         msg = f'{detector.labels[obj.class_id]}: {obj.score:.2f}'
         img.draw_string(obj.x, obj.y, msg, color = image.COLOR_RED)
-    dis.show(img)
+    disp.show(img)
 ```
 You can see that `objs` is a list of detection results. Here, we draw bounding boxes on the screen, but we can also send these results via UART.
 

docs/doc/en/faq.md

@@ -189,25 +189,25 @@ This is not an error message. It is a log message indicating that the multimedia
 
 By default only support English charactors, if you want to show Chinese, you need to change font, refer to [Custom fonts part of image basic operation](./vision/image_ops.md#Chinese-support-and-custom-fonts)
 
-## Program Exits with Prompt: app exit with code: 1
-
-This occurs because the program encountered an error and exited abnormally. You need to check the logs to identify the issue. Here's how to check the logs:
-
-### Method 1:
-1. First, use **MaixVision** to connect to the device, ensuring all programs occupying the display and camera are closed.
-2. Then connect to the device via **SSH** to access the SSH terminal. For details on connecting, refer to the [Linux Basics](./basic/linux_basic.md).
-3. Execute the following commands:
-   - For Python programs:  
-     ```bash
-     cd /maixapp/apps/xxx && python main.py
-     ```
-     Here, `xxx` is the ID of the application that encountered the error.
-   - For non-Python programs:  
-     ```bash
-     cd /maixapp/apps/xxx && ./xxx
-     ```
-     Again, `xxx` is the ID of the application that encountered the error.
-4. Carefully review the logs to check for errors. Note that errors might not always be at the last line, so check thoroughly from the end upward.
+### Program Exit and Message: "app exit with code: 1, log in /maixapp/tmp/last_run.log"
+
+This indicates that the program encountered an error and exited unexpectedly. You need to check the log to find the issue. 
+
+#### How to Check the Logs:
+
+- **Method 1**: View the `/maixapp/tmp/last_run.log` file immediately after the error:
+  1. On MaixVision, run the script `MaixPy/examples/tools/show_last_run_log.py` to view the log.
+  2. On an SSH terminal, use the command `cat /maixapp/tmp/last_run.log` to view the log.
+
+- **Method 2**:
+  - First, use MaixVision to connect to the device to exit any programs that are using the display or camera.
+  - Then, connect to the device via SSH and enter the SSH terminal. For connection steps, refer to the [Linux Basics](./basic/linux_basic.md).
+  - Manually run the program using the following commands:
+    - If it's a Python program: `cd /maixapp/apps/xxx && python main.py`, where `xxx` is the ID of the application that encountered the error.
+    - If it's not a Python program: `cd /maixapp/apps/xxx && ./xxx`, where `xxx` is the ID of the application that encountered the error.
+  - Carefully examine the logs for any errors. Note that the error may not appear on the last line, so check the logs from the bottom upwards.
+
+- **Method 3**: If the application is written in Python, use MaixVision to run the source code to view runtime errors and fix them. Again, be aware that the error may not appear on the last line, so check the logs carefully from the bottom upwards.
 
 ### Method 2:
 If the application is written in Python, use **MaixVision** to run the source code directly, examine the runtime errors, and make corrections. Be aware that errors may not be at the last line, so inspect the logs carefully from the end upward.

docs/doc/en/vision/body_key_points.md

@@ -21,7 +21,7 @@ detector = nn.YOLOv8(model="/root/models/yolov8n_pose.mud", dual_buff=True)
 # detector = nn.YOLO11(model="/root/models/yolo11n_pose.mud", dual_buff=True)
 
 cam = camera.Camera(detector.input_width(), detector.input_height(), detector.input_format())
-dis = display.Display()
+disp = display.Display()
 
 while not app.need_exit():
     img = cam.read()
@@ -31,7 +31,7 @@ while not app.need_exit():
         msg = f'{detector.labels[obj.class_id]}: {obj.score:.2f}'
         img.draw_string(obj.x, obj.y, msg, color=image.COLOR_RED)
         detector.draw_pose(img, obj.points, 8 if detector.input_width() > 480 else 4, image.COLOR_RED)
-    dis.show(img)
+    disp.show(img)
 ```
 
 You can also find the code in the [MaixPy/examples/vision](https://github.com/sipeed/MaixPy/tree/main/examples/vision/ai_vision) directory.

docs/doc/en/vision/classify.md

@@ -15,15 +15,15 @@ from maix import camera, display, image, nn
 
 classifier = nn.Classifier(model="/root/models/mobilenetv2.mud", dual_buff = True)
 cam = camera.Camera(classifier.input_width(), classifier.input_height(), classifier.input_format())
-dis = display.Display()
+disp = display.Display()
 
 while 1:
     img = cam.read()
     res = classifier.classify(img)
     max_idx, max_prob = res[0]
     msg = f"{max_prob:5.2f}: {classifier.labels[max_idx]}"
     img.draw_string(10, 10, msg, image.COLOR_RED)
-    dis.show(img)
+    disp.show(img)
 ```
 
 Result video:

docs/doc/en/vision/dual_buff.md

@@ -13,7 +13,7 @@ detector = nn.YOLOv5(model="/root/models/yolov5s.mud", dual_buff=True)
 # detector = nn.YOLOv8(model="/root/models/yolov8n.mud", dual_buff=True)
 
 cam = camera.Camera(detector.input_width(), detector.input_height(), detector.input_format())
-dis = display.Display()
+disp = display.Display()
 
 while not app.need_exit():
     img = cam.read()
@@ -22,7 +22,7 @@ while not app.need_exit():
         img.draw_rect(obj.x, obj.y, obj.w, obj.h, color = image.COLOR_RED)
         msg = f'{detector.labels[obj.class_id]}: {obj.score:.2f}'
         img.draw_string(obj.x, obj.y, msg, color = image.COLOR_RED)
-    dis.show(img)
+    disp.show(img)
 ```
 
 Generally, this parameter defaults to `True`, unless you manually set `dual_buff=False` to disable the dual buffer function.

docs/doc/en/vision/face_detection.md

@@ -24,7 +24,7 @@ from maix import camera, display, image, nn, app
 detector = nn.YOLOv8(model="/root/models/yolov8n_face.mud", dual_buff = True)
 
 cam = camera.Camera(detector.input_width(), detector.input_height(), detector.input_format())
-dis = display.Display()
+disp = display.Display()
 
 while not app.need_exit():
     img = cam.read()
@@ -34,7 +34,7 @@ while not app.need_exit():
         msg = f'{detector.labels[obj.class_id]}: {obj.score:.2f}'
         img.draw_string(obj.x, obj.y, msg, color = image.COLOR_RED)
         detector.draw_pose(img, obj.points, 2, image.COLOR_RED)
-    dis.show(img)
+    disp.show(img)
 ```
 
 For the other two models:
@@ -48,7 +48,7 @@ detector = nn.Retinaface(model="/root/models/retinaface.mud")
 # detector = nn.FaceDetector(model="/root/models/face_detector.mud")
 
 cam = camera.Camera(detector.input_width(), detector.input_height(), detector.input_format())
-dis = display.Display()
+disp = display.Display()
 
 while not app.need_exit():
     img = cam.read()
@@ -57,7 +57,7 @@ while not app.need_exit():
         img.draw_rect(obj.x, obj.y, obj.w, obj.h, color = image.COLOR_RED)
         radius = math.ceil(obj.w / 10)
         img.draw_keypoints(obj.points, image.COLOR_RED, size = radius if radius < 5 else 4)
-    dis.show(img)
+    disp.show(img)
 ```
 
 ## Model Downloads and Other Resolution Models

docs/doc/en/vision/face_recognition.md

@@ -33,7 +33,7 @@ recognizer = nn.FaceRecognizer(detect_model="/root/models/yolov8n_face.mud", fea
 if os.path.exists("/root/faces.bin"):
     recognizer.load_faces("/root/faces.bin")
 cam = camera.Camera(recognizer.input_width(), recognizer.input_height(), recognizer.input_format())
-dis = display.Display()
+disp = display.Display()
 
 while 1:
     img = cam.read()
@@ -44,7 +44,7 @@ while 1:
         img.draw_keypoints(obj.points, image.COLOR_RED, size = radius if radius < 5 else 4)
         msg = f'{recognizer.labels[obj.class_id]}: {obj.score:.2f}'
         img.draw_string(obj.x, obj.y, msg, color = image.COLOR_RED)
-    dis.show(img)
+    disp.show(img)
 ```
 
 When you first run this code, it can detect faces but will not recognize them. We need to enter a mode to learn faces.