MicroPython openmv識別直角

ID:323148 · 發表于 2018-5-14 21:23

openmv 識別直角

import sensor
import image
import time
import math
import pyb
from pyb import Pin, Timer, UART,LED
from GeometryFeature import GeometryFeature
# 當光線弱的時候，用于補光
LED(4).on()
# 是否開啟debug模式
# 如果是False就不print, 不進行可視化繪制，可以提高幀率
is_debug = True
#--------------感光芯片配置 START -------------------
DISTORTION_FACTOR = 1.5 # 設定畸變系數
IMG_WIDTH = 64
IMG_HEIGHT = 64
def init_sensor():
'''
初始化感光芯片
'''
sensor.reset()
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.B64X64) # 分辨率為B64X64
sensor.skip_frames(time=2000)
sensor.set_auto_gain(False) # 顏色追蹤關閉自動增益
sensor.set_auto_whitebal(False) # 顏色追蹤關閉白平衡
init_sensor()
#--------------感光芯片配置 END -------------------
#--------------定時器部分 START -------------------
is_need_send_data = False # 是否需要發送數據的信號標志
def uart_time_trigger(timer):
'''
串口發送數據的定時器，定時器的回調函數
'''
global is_need_send_data
is_need_send_data = True
# 初始化定時器頻率為20HZ 每秒執行20次
tim = Timer(4, freq=20)
# 設定定時器的回調函數
tim.callback(uart_time_trigger)
#--------------定時器部分 END -------------------
#--------------直線與直角檢測部分 START -------------------
INTERSERCT_ANGLE_THRESHOLD = (45,90)
# 直線灰度圖顏色閾值
LINE_COLOR_THRESHOLD = [(0, 120)]
# 如果直線是白色的，閾值修改為：
# LINE_COLOR_THRESHOLD = [(128, 255)]
# 取樣窗口
ROIS = {
'down': (0, 55, 64, 8), # 橫向取樣-下方 1
'middle': (0, 28, 64, 8), # 橫向取樣-中間 2
'up': (0, 0, 64, 8), # 橫向取樣-上方 3
'left': (0, 0, 8, 64), # 縱向取樣-左側 4
'right': (56, 0, 8, 64) # 縱向取樣-右側 5
}
BLOB_MAX_WIDTH = 15 # 色塊的最大寬度
BLOB_MIN_WIDTH = 5 # 色塊的最小寬度
BLOB_MAX_HEIGHT = 15 # 色塊的最大高度
BLOB_MIN_HEIGHT = 5 # 色塊的最小寬度
def find_blobs_in_rois(img):
'''
在ROIS中尋找色塊，獲取ROI中色塊的中心區域與是否有色塊的信息
'''
global ROIS
global is_debug
roi_blobs_result = {} # 在各個ROI中尋找色塊的結果記錄
for roi_direct in ROIS.keys():
roi_blobs_result[roi_direct] = {
'cx': -1,
'cy': -1,
'blob_flag': False
}
for roi_direct, roi in ROIS.items():
blobs=img.find_blobs(LINE_COLOR_THRESHOLD, roi=roi, merge=True, pixels_area=10)
if len(blobs) == 0:
continue
largest_blob = max(blobs, key=lambda b: b.pixels())
x,y,width,height = largest_blob[:4]
if not(width >= BLOB_MIN_WIDTH and width <= BLOB_MAX_WIDTH and height >= BLOB_MIN_HEIGHT and height <= BLOB_MAX_HEIGHT):
# 根據色塊的寬度進行過濾
continue
roi_blobs_result[roi_direct]['cx'] = largest_blob.cx()
roi_blobs_result[roi_direct]['cy'] = largest_blob.cy()
roi_blobs_result[roi_direct]['blob_flag'] = True
if is_debug:
img.draw_rectangle((x,y,width, height), color=(255))
return roi_blobs_result
def visualize_result(canvas, cx_mean, cx, cy, is_turn_left, is_turn_right, is_t, is_cross):
'''
可視化結果
'''
if not(is_turn_left or is_turn_right or is_t or is_cross):
mid_x = int(canvas.width()/2)
mid_y = int(canvas.height()/2)
# 繪制x的均值點
canvas.draw_circle(int(cx_mean), mid_y, 5, color=(255))
# 繪制屏幕中心點
canvas.draw_circle(mid_x, mid_y, 8, color=(0))
canvas.draw_line((mid_x, mid_y, int(cx_mean), mid_y), color=(255))
turn_type = 'N' # 啥轉角也不是
if is_t or is_cross:
# 十字形或者T形
canvas.draw_cross(int(cx), int(cy), size=10, color=(255))
canvas.draw_circle(int(cx), int(cy), 5, color=(255))
if is_t:
turn_type = 'T' # T字形狀
elif is_cross:
turn_type = 'C' # 十字形
elif is_turn_left:
turn_type = 'L' # 左轉
elif is_turn_right:
turn_type = 'R' # 右轉
canvas.draw_string(0, 0, turn_type, color=(0))
#--------------直線與直角檢測部分 END -------------------
#---------------------MAIN-----------------------
last_cx = 0
last_cy = 0
while True:
if not is_need_send_data:
# 不需要發送數據
continue
is_need_send_data = False
# 拍攝圖片
img = sensor.snapshot()
# 去除圖像畸變
img.lens_corr(DISTORTION_FACTOR)
# 創建畫布
# canvas = img.copy()
# 為了IDE顯示方便，直接在代碼結尾用IMG繪制
# 注意：林林的代碼里計算直線之間的交點的代碼沒有用到
lines = img.find_lines(threshold=1000, theta_margin = 50, rho_margin = 50)
# 尋找相交的點要求滿足角度閾值
intersect_pt = GeometryFeature.find_interserct_lines(lines, angle_threshold=(45,90), window_size=(IMG_WIDTH, IMG_HEIGHT))
if intersect_pt is None:
# 直線與直線之間的夾角不滿足閾值范圍
intersect_x = 0
intersect_y = 0
else:
intersect_x, intersect_y = intersect_pt
reslut = find_blobs_in_rois(img)
# 判斷是否需要左轉與右轉
is_turn_left = False
is_turn_right = False
if (not reslut['up']['blob_flag'] ) and reslut['down']['blob_flag']:
if reslut['left']['blob_flag']:
is_turn_left = True
if reslut['right']['blob_flag']:
is_turn_right = True
# 判斷是否為T形的軌道
is_t = False
# 判斷是否十字形軌道
is_cross = False
cnt = 0
for roi_direct in ['up', 'down', 'left', 'right']:
if reslut[roi_direct]['blob_flag']:
cnt += 1
is_t = cnt == 3
is_cross = cnt == 4
# cx_mean 用于確定視角中的軌道中心
# 用于表示左右偏移量
cx_mean = 0
for roi_direct in ['up', 'down', 'middle']:
if reslut[roi_direct]['blob_flag']:
cx_mean += reslut[roi_direct]['cx']
else:
cx_mean += IMG_WIDTH / 2
cx_mean /= 3
# cx, cy 只有在T形區域檢測出來的時候才有用，
# 用于確定軌道中圓形的大致區域，用于定點，是計算圓心的一種近似方法
cx = 0
cy = 0
if is_cross or is_t:
# 只在出現十字形或者T字形才計算圓心坐標
cnt = 0
for roi_direct in ['up', 'down']:
if reslut[roi_direct]['blob_flag']:
cnt += 1
cx += reslut[roi_direct]['cx']
if cnt == 0:
cx = last_cx
else:
cx /= cnt
cnt = 0
for roi_direct in ['left', 'right']:
if reslut[roi_direct]['blob_flag']:
cnt += 1
cy += reslut[roi_direct]['cy']
if cnt == 0:
cy = last_cy
else:
cy /= cnt
last_cx = cx
last_cy = cy
if is_debug:
visualize_result(img, cx_mean, cx, cy, is_turn_left, is_turn_right, is_t, is_cross)