🚀 实现板端人脸检测：FireBeetle 2 ESP32 P4 📸

本文介绍了 DFRobot FireBeetle 2 ESP32 P4 开发套件调用固件中预训练的模型实现板端人脸检测推理的项目设计。

包括项目介绍、流程图、工程代码、效果展示等。

项目介绍

摄像头人脸检测；

板端推理测试：流程图、MicroPython 代码、效果展示；

摄像头测试

开发板连接 CSI 摄像头，上传测试代码；

打开HTTP网页，摄像头对准人脸画面，识别结果由红色线框显示出来；

为了提升识别效果和显示效果，这里使用板端推理本地图片的方式。

板端推理

包括流程图、工程代码、测试效果等。

流程图

代码

运行 Thonny IDE 软件，配置解释器和设备端口，新建文件并添加如下代码

from espdl import FaceDetector
from jpeg import Decoder, Encoder

# Initialize components
decoder = Decoder()
encoder = Encoder(width=320, height=240, pixel_format="RGB888")
face_detector = FaceDetector()

# Input and output paths
input_filename = "sarah.jpg"
output_dir = "out"
output_filename = output_dir + "/" + input_filename.split(".")[0] + "_out.jpg"

# Read input image
img = open(input_filename, "rb").read()
framebuffer = decoder.decode(img)
framebuffer = bytearray(framebuffer)

# Face detection
results = face_detector.run(framebuffer)

def draw_rectangle(buffer, width, height, x, y, w, h, list1, color=(255, 0, 0), thickness=3):
   def set_pixel(buffer, width, x, y, color):
   if 0 <= x < width and 0 <= y < height:
   offset = (y * width + x) * 3
   buffer[offset] = color[0]
   buffer[offset + 1] = color[1]
   buffer[offset + 2] = color[2]

   def draw_large_dot(buffer, width, x, y, color, size=3):
   for i in range(x - size, x + size + 1):
   for j in range(y - size, y + size + 1):
   set_pixel(buffer, width, i, j, color)

   # Draw thick borders
   for t in range(thickness):
   # Top border
   for i in range(x - t, x + w + t + 1):
   set_pixel(buffer, width, i, y - t, color)
   # Bottom border
   for i in range(x - t, x + w + t + 1):
   set_pixel(buffer, width, i, y + h + t, color)
   # Left border
   for j in range(y - t, y + h + t + 1):
   set_pixel(buffer, width, x - t, j, color)
   # Right border
   for j in range(y - t, y + h + t + 1):
   set_pixel(buffer, width, x + w + t, j, color)

   # Draw feature points
   if list1:
   draw_large_dot(buffer, width, list1[0], list1[1], (0, 0, 255), size=2)
   draw_large_dot(buffer, width, list1[2], list1[3], (0, 0, 255), size=2)
   draw_large_dot(buffer, width, list1[4], list1[5], (0, 255, 0), size=2)
   draw_large_dot(buffer, width, list1[6], list1[7], (255, 0, 0), size=2)
   draw_large_dot(buffer, width, list1[8], list1[9], (255, 0, 0), size=2)

# Draw bounding boxes
for face in results:
   x1, y1, x2, y2 = face['box']
   draw_rectangle(
   framebuffer,
   320,
   240,
   x1,
   y1,
   x2 - x1,
   y2 - y1,
   face['features'],
   color=(255, 0, 0),
   thickness=3
   )

# Encode and save output
marked_img = encoder.encode(framebuffer)

with open(output_filename, "wb") as f:
   f.write(marked_img)

print("Saved processed image to:", output_filename)