This project demonstrates streaming visual data in real-time from a PC to an ESP32-C3 microcontroller equipped with a TFT display. The multi-threaded Python server on the PC can capture a selected screen region or generate various data visualizations (like a BIOS-style screen, CPU monitor, or Prometheus metrics dashboard). An Arduino sketch on the ESP32-C3 receives and renders this stream over a TCP connection. Differential updates with an adaptive threshold are used to minimize latency and bandwidth, optimizing for visual quality versus frame rate. Server-side color correction options are included for better display fidelity.

• Wiki: https://deepwiki.com/vpuhoff/Python-ESP32-TFT-Stream

• Multiple Stream Sources:
  • Streams a selected screen region from a PC (Windows/macOS/Linux).
  • Generates and streams a pseudo BIOS/POST screen.
  • Generates and streams a real-time CPU usage monitor.
  • Generates and streams a dashboard of system metrics collected from a Prometheus instance.
• Displays the stream on an ESP32-C3 driven TFT screen (using the TFT_eSPI library).
• Uses TCP for communication with TCP_NODELAY enabled for potentially lower latency.
• Advanced Differential Updates:
  • Implements differential updates (sending only changed screen regions).
  • Features an adaptive threshold for dirty_rect detection, dynamically balancing image quality and frame rate based on processing performance.
• Handles large updates by chunking them into smaller packets.
• Includes server-side gamma and white balance correction for fine-tuning color reproduction.
• Multi-threaded Python Server: Utilizes separate threads for frame generation and frame processing/sending, improving responsiveness and throughput.
• Arduino client for receiving and rendering on ESP32.
• Configurable resolution, capture area (for screen streaming), and target FPS for adaptive threshold.
• Handles connection drops and attempts reconnection with visual feedback on ESP32.
• Comprehensive Prometheus Exporter: The Python server includes a Prometheus exporter to monitor its own performance in detail (e.g., processing times for different stages, packet sizes, queue lengths, calculated FPS, adaptive threshold value).

• ESP32-C3 Development Board: Any ESP32-C3 based board (e.g., ESP32-C3-DevKitM-1, Seeed Studio XIAO ESP32C3, Lolin C3 Mini).
• SPI TFT Display: A display compatible with the TFT_eSPI library (e.g., based on ILI9341, ST7789, ST7735 controllers). Common resolutions are 320x240, 240x240, etc.
• PC: A computer running Windows, macOS, or Linux to host the Python server.
• Wi-Fi Network: A router or access point that both the PC and ESP32-C3 can connect to.
• Wiring: Jumper wires to connect the TFT display to the ESP32-C3 via SPI.
• (Optional for Prometheus Monitor): A running Prometheus instance (https://prometheus.io/) collecting desired system metrics (e.g., via node_exporter, windows_exporter, nvidia_gpu_exporter).

• Python: Version 3.7 or higher recommended.

• Pip: Python package installer (usually comes with Python).

• Python Libraries: Install using pip install -r requirements.txt. Key libraries include:

  • mss: For efficient cross-platform screen capture.
  • Pillow: For image manipulation (resizing, format conversion, drawing).
  • numpy: For efficient numerical operations (used in color correction and optimized diffing).
  • psutil: For CPU utilization metrics (used by the CPU monitor).
  • py-cpuinfo: For fetching CPU name (used by the CPU monitor).
  • prometheus_api_client: For querying a Prometheus instance (used by the Prometheus monitor generator).
  • prometheus-client: For exposing the server's own performance metrics.
  • (Optional, for specific window capture): pywin32 (Windows), python-xlib (Linux), pyobjc-core & pyobjc-framework-Quartz (macOS).

  pip install -r requirements.txt

• Arduino IDE (version 1.8.19 or 2.x) OR PlatformIO IDE (within VS Code).
• ESP32 Arduino Core: Board support package for ESP32. Install via the Boards Manager in Arduino IDE or PlatformIO's interface.
• TFT_eSPI Library: Install via the Arduino Library Manager or manually from Bodmer's GitHub.
  • Crucially, you MUST configure TFT_eSPI for your specific ESP32 board and TFT display. This involves editing the library's User_Setup.h file (or selecting the correct setup in User_Setup_Select.h) to define the correct pins (MOSI, SCLK, CS, DC, RST, BL - if used) and the display driver chip (e.g., ILI9341_DRIVER, ST7789_DRIVER).

1. Clone Repository:

   git clone [https://github.com/vpuhoff/Python-ESP32-TFT-Stream.git](https://github.com/vpuhoff/Python-ESP32-TFT-Stream.git) esp32_stream
   cd esp32_stream

2. Configure TFT_eSPI:

   • Locate the installed TFT_eSPI library folder (usually in your Arduino libraries folder).
   • Edit User_Setup.h (or User_Setup_Select.h) according to your ESP32-C3 board's SPI pins and your specific TFT display model/driver. This step is essential for the display to work correctly.

3. Configure ESP32 Client (esp32.ino.txt or your .ino file):

   • Open the .ino file in your Arduino IDE or PlatformIO.
   • Modify the following variables at the top of the file:
     • ssid: Your Wi-Fi network name.
     • password: Your Wi-Fi password.
     • server_ip: The static IP address of the PC running the Python server.
     • server_port: The port the server will listen on (must match Python script). Default: 8888.
     • PIXEL_BUFFER_SIZE: Ensure this is large enough to hold the biggest data chunk sent by Python (must be >= MAX_CHUNK_DATA_SIZE in Python). Default: 10 * 1024 (10KB).
   • Select your ESP32-C3 board from the IDE's board menu.
   • Compile and upload the sketch to the ESP32-C3.

4. Configure Python Server (server.py):

   • Navigate to the server script directory.
   • Install Python dependencies if you haven't already: pip install -r requirements.txt
   • Edit the script and configure these settings near the top:
     • IMAGE_SOURCE_MODE: Choose the desired source: "SCREEN_CAPTURE", "BIOS", "CPU_MONITOR", or "PROMETHEUS_MONITOR".
     • PROMETHEUS_EXPORTER_PORT: Port for the server's own performance metrics (default: 8000).
     • ESP32_PORT: The port to listen on for ESP32 connections (must match ESP32 sketch). Default: 8888.
     • TARGET_WIDTH, TARGET_HEIGHT: The resolution of the ESP32's display.
     • GENERATOR_TARGET_INTERVAL_SEC: Approximate interval for the frame generation thread (e.g., 0.05 for 20 FPS target generation rate if resources allow).
     • MAX_CHUNK_DATA_SIZE: Maximum size (in bytes) of pixel data per network packet.
     • Adaptive Threshold Settings:
       • TARGET_FPS: Desired FPS for the consumer thread, influences threshold adaptation.
       • MIN_DIRTY_RECT_THRESHOLD, MAX_DIRTY_RECT_THRESHOLD: Range for the adaptive threshold.
       • THRESHOLD_ADJUSTMENT_STEP_UP, THRESHOLD_ADJUSTMENT_STEP_DOWN: How aggressively the threshold changes.
       • FPS_HISTORY_SIZE, FPS_HYSTERESIS_FACTOR: Parameters for FPS calculation and adaptation stability.
     • For Screen Capture:
       • CAPTURE_REGION: Dictionary defining the screen area to capture.
     • For Prometheus Monitor:
       • In prometheus_monitor_generator.py, configure PROMETHEUS_URL and review METRIC_CONFIG for your desired metrics and queries.
     • Color Correction (applied to all visual streams):
       • GAMMA: Gamma correction value.
       • WB_SCALE: Tuple for white balance adjustment (R_mult, G_mult, B_mult).

1. Network: Ensure the PC and ESP32-C3 are connected to the same Wi-Fi network. It's recommended to assign a static IP address to the PC running the server.
2. (Optional) Prometheus Setup: If using PROMETHEUS_MONITOR mode, ensure your Prometheus instance is running and scraping the necessary exporters.
3. Start Server: Open a terminal or command prompt on your PC, navigate to the script directory, and run the server:

   python server.py

   The server will start listening for a connection from the ESP32 and begin exposing its own metrics on http://localhost:PROMETHEUS_EXPORTER_PORT (or your PC's IP).
4. Start Client: Power on or reset your ESP32-C3 board.
   • It will connect to Wi-Fi.
   • It will display an initial screen showing its IP address and connection status.
   • It will then attempt to connect to the configured server_ip and server_port.
5. Streaming: Once the ESP32 connects, the TFT display should start showing the content generated or captured by the Python server.
6. (Optional) Monitor Server Performance: Point your Prometheus instance to scrape http://<PC_IP_ADDRESS>:PROMETHEUS_EXPORTER_PORT to collect server performance metrics. Visualize them using Grafana or the Prometheus UI. Key metrics include esp32_consumer_calculated_fps and esp32_current_dynamic_threshold.

1. TCP Connection: ESP32 client connects to Python server. TCP_NODELAY is enabled.
2. Multi-threaded Server Architecture (Python):
   • Frame Generation Thread: Captures or generates "raw" image frames based on IMAGE_SOURCE_MODE.
     • Screen Capture: mss captures the screen region.
     • CPU Monitor: psutil and py-cpuinfo gather data; Pillow draws.
     • BIOS Screen: Pillow draws.
     • Prometheus Monitor: prometheus_api_client fetches metrics; graphics_engine.py renders.
     • Generated frames are placed into a thread-safe queue.
   • Frame Consumer Thread:
     • Retrieves raw frames from the queue.
     • Image Processing:
       • Resizing: Pillow resizes to target ESP32 resolution.
       • Color Correction: numpy and Pillow apply gamma/white balance.
       • Diffing: An numpy-based algorithm compares with the previous frame to find dirty_rects using an adaptive threshold based on current processing FPS.
     • Packetizing & Transmission:
       • Changed regions are converted to RGB565.
       • Large updates are chunked.
       • Headers (X, Y, W, H, DataLen) are packed.
       • Packets sent via TCP to ESP32.
     • Adaptive Threshold Control: Adjusts the dirty_rect threshold to maintain a target FPS.
3. Reception & Rendering (ESP32):
   • Client reads TCP stream, parses header, reads pixel data.
   • TFT_eSPI.pushImage() renders data.
4. Connection Management (ESP32): Handles connection state and retries.
5. Server Performance Monitoring (Python): prometheus-client exposes internal metrics (stage durations, FPS, threshold, queue size, etc.).

• ESP32 Cannot Connect: Check server_ip in ESP32 code, server running, PC firewall, Wi-Fi.
• ESP32 Reboots / Crashes: Check for Out of Memory (try reducing PIXEL_BUFFER_SIZE on ESP32), verify TFT_eSPI pin configuration and driver.
• ESP32 "Exceeds buffer size" error: Ensure MAX_CHUNK_DATA_SIZE (Python) <= PIXEL_BUFFER_SIZE (ESP32).
• Display Blank / Garbage / Wrong Colors:
  • Crucial: Double-check TFT_eSPI configuration (User_Setup.h) for pins and driver.
  • Verify wiring.
  • Adjust tft.invertDisplay(true/false) in ESP32 setup().
  • Fine-tune GAMMA and WB_SCALE in server.py.
• Slow / Laggy Performance / High Latency:
  • Check server Prometheus metrics: esp32_consumer_calculated_fps (is it near TARGET_FPS?), esp32_dirty_rects_send_duration_seconds (is network send slow?), esp32_current_dynamic_threshold (is it very high, indicating struggle?).
  • High resolution/large capture area, or very frequent updates from the source generator.
  • Slow Wi-Fi.
  • Inefficient ESP32-side processing/rendering.
  • Try adjusting TARGET_FPS and threshold range (MIN_DIRTY_RECT_THRESHOLD, MAX_DIRTY_RECT_THRESHOLD) in server.py.
• Too Many Artifacts / Blocky Updates:
  • The current_dynamic_threshold might be too high. Try increasing TARGET_FPS to encourage a lower threshold, or narrow the MAX_DIRTY_RECT_THRESHOLD.
• Prometheus Monitor Issues:
  • Prometheus server running and accessible.
  • Correct PromQL queries in prometheus_monitor_generator.py.
  • Necessary exporters running.
• mss related errors in frame_generator_thread_func: Ensure mss is correctly initialized within the thread if capturing screen. The current code does this.

• Further optimize find_dirty_rects (e.g., merging adjacent small dirty rectangles).
• Implement reliable specific window capture on the server.
• Add options for different pixel formats (e.g., grayscale).
• Explore simple lossless compression (e.g., Run-Length Encoding) if beneficial.
• Make server settings configurable via command-line arguments or a configuration file.
• Implement dithering during RGB565 conversion for smoother gradients.
• More sophisticated error handling and reporting.
• Allow dynamic selection of IMAGE_SOURCE_MODE without restarting the server.
• Refine the adaptive threshold algorithm for smoother transitions and better target FPS adherence.

This project is released under the MIT License.

• Python
• mss library
• Pillow library
• NumPy library
• psutil library
• py-cpuinfo library
• Prometheus Python Client
• Prometheus API Client Python
• TFT_eSPI library by Bodmer
• Espressif IoT Development Framework (ESP-IDF) and the Arduino Core for ESP32