# This work is licensed under the MIT license.
# Copyright (c) 2013-2023 OpenMV LLC. All rights reserved.
# https://github.com/openmv/openmv/blob/master/LICENSE
#
# Face Eye Detection Example
#
# This script uses the built-in frontalface detector to find a face and then
# the eyes within the face. If you want to determine the eye gaze please see the
# iris_detection script for an example on how to do that.

import sensor
import time
import image

# Reset sensor
sensor.reset()

# Sensor settings
sensor.set_contrast(1)
sensor.set_gainceiling(16)
sensor.set_framesize(sensor.HQVGA)
sensor.set_pixformat(sensor.GRAYSCALE)

# Load Haar Cascade
# By default this will use all stages, lower satges is faster but less accurate.
face_cascade = image.HaarCascade("frontalface", stages=25)
eyes_cascade = image.HaarCascade("eye", stages=24)
print(face_cascade, eyes_cascade)

# FPS clock
clock = time.clock()

while True:
    clock.tick()

    # Capture snapshot
    img = sensor.snapshot()

    # Find a face !
    # Note: Lower scale factor scales-down the image more and detects smaller objects.
    # Higher threshold results in a higher detection rate, with more false positives.
    objects = img.find_features(face_cascade, threshold=0.5, scale_factor=1.5)

    # Draw faces
    for face in objects:
        img.draw_rectangle(face)
        # Now find eyes within each face.
        # Note: Use a higher threshold here (more detections) and lower scale (to find small objects)
        eyes = img.find_features(
            eyes_cascade, threshold=0.5, scale_factor=1.2, roi=face
        )
        for e in eyes:
            img.draw_rectangle(e)

    # Print FPS.
    # Note: Actual FPS is higher, streaming the FB makes it slower.
    print(clock.fps())

如题，怎么导入外部的图片并显示？

实测用 img.find_rects()返回的矩形四点坐标是外接矩形的坐标（图中红点坐标），如何识别内接矩形的（图中黄点坐标）?

用QVGA就爆内存，QQVGA分辨率又太低

# Edge Impulse - OpenMV Object Detection Example

import sensor, image, time, os, tf, math, uos, gc

sensor.reset()                         # Reset and initialize the sensor.
sensor.set_pixformat(sensor.GRAYSCALE)    # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QQVGA)      # Set frame size to QVGA (320x240)
sensor.set_windowing((240, 240))       # Set 240x240 window.
sensor.skip_frames(time=2000)          # Let the camera adjust.

net = None
labels = None
min_confidence = 0.5

try:
    # load the model, alloc the model file on the heap if we have at least 64K free after loading
    net = tf.load("trained.tflite", load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (64*1024)))
except Exception as e:
    raise Exception('Failed to load "trained.tflite", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')

try:
    labels = [line.rstrip('\n') for line in open("labels.txt")]
except Exception as e:
    raise Exception('Failed to load "labels.txt", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')

colors = [ # Add more colors if you are detecting more than 7 types of classes at once.
    (255,   0,   0),
    (  0, 255,   0),
    (255, 255,   0),
    (  0,   0, 255),
    (255,   0, 255),
    (  0, 255, 255),
    (255, 255, 255),
]

clock = time.clock()
while(True):
        clock.tick()

        img = sensor.snapshot().lens_corr(1.85)

    # detect() returns all objects found in the image (splitted out per class already)
    # we skip class index 0, as that is the background, and then draw circles of the center
    # of our objects
   ###  if r:
        for i, detection_list in enumerate(net.detect(img, thresholds=[(math.ceil(min_confidence * 255), 255)])):
           if (i == 0): continue # background class
           if (len(detection_list) == 0): continue # no detections for this class?

           print("********** %s **********" % labels[i])
           for d in detection_list:
               [x, y, w, h] = d.rect()
               center_x = math.floor(x + (w / 2))
               center_y = math.floor(y + (h / 2))
               print('x %d\ty %d' % (center_x, center_y))
               img.draw_circle((center_x, center_y, 12), color=colors[i], thickness=2)

        print(clock.fps(), "fps", end="\n\n")

图一为QVGA
图二为QQVGA

import sensor, image, time,math,pyb
from pyb import UART,LED
import json
import ustruct

sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.skip_frames(time = 2000)
sensor.set_auto_gain(False) # must be turned off for color tracking
sensor.set_auto_whitebal(False) # must be turned off for color tracking
red_threshold_01=(10, 100, 127, 32, -43, 67)
clock = time.clock()

uart = UART(3,115200)   #定义串口3变量
uart.init(115200, bits=8, parity=None, stop=1) # init with given parameters

def find_max(blobs):    #定义寻找色块面积最大的函数
    max_size=0
    for blob in blobs:
        if blob.pixels() > max_size:
            max_blob=blob
            max_size = blob.pixels()
    return max_blob


def sending_data(cx,cy,cw,ch):
    global uart;
    #frame=[0x2C,18,cx%0xff,int(cx/0xff),cy%0xff,int(cy/0xff),0x5B];
    #data = bytearray(frame)
    data = ustruct.pack("<bbhhhhb",      #格式为俩个字符俩个短整型(2字节)
                   0x2C,                      #帧头1
                   0x12,                      #帧头2
                   int(cx), # up sample by 4   #数据1
                   int(cy), # up sample by 4    #数据2
                   int(cw), # up sample by 4    #数据1
                   int(ch), # up sample by 4    #数据2
                   0x5B)
    uart.write(data);   #必须要传入一个字节数组


while(True):
    clock.tick()
    img = sensor.snapshot()
    blobs = img.find_blobs([red_threshold_01])
    max_b = find_max(blobs)
    cx=0;cy=0;
    if blobs:
            #如果找到了目标颜色
            cx=max_b[5]
            cy=max_b[6]
            cw=max_b[2]
            ch=max_b[3]
            img.draw_rectangle(max_b[0:4]) # rect
            img.draw_cross(max_b[5], max_b[6]) # cx, cy
            FH = bytearray([0x2C,0x12,cx,cy,cw,ch,0x5B])
            #sending_data(cx,cy,cw,ch)
            uart.write(FH)
            print(cx,cy,cw,ch)