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cluster.py
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cluster.py
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import cv2
import numpy as np
import time
import shape as shape # 形状检测
import zcode as zcode # zcode检测
'''
以下内容为基本参数
'''
# 标定参数（需要根据实际相机进行标定）
FOCAL_LENGTH = 2.8 # 相机焦距（单位：像素）
KNOWN_WIDTH = 4.2 # 物体的已知宽度（单位：厘米）
blue_lower = np.array([175, 98, 26]) # 蓝色最低值範圍
blue_upper = np.array([244, 182, 112]) # 蓝色最高值範圍
yellow_lower = np.array([26, 120, 200]) # 黄色最低范围
yellow_upper = np.array([70, 248, 255])
red_lower = np.array([16, 10, 144]) # 红色最低范围
red_upper = np.array([90, 100, 255])
def calculate_distance(known_width, focal_length, percieved_width):
return (known_width * focal_length) / percieved_width
# 主程序，负责调用函数，先执行灰度识别，减少误差，识别二维码，然后再识别形状，再识别颜色
# 蓝色球+黄色球为方案1，红色球+黄色球为方案2,目前均写为一种，最后再分割
def main():
cap = cv2.VideoCapture(0)
start_time = time.time()
while True:
ret, img = cap.read()
elapsed_time = (time.time() - start_time) * 1000
focal_length = FOCAL_LENGTH # 焦距
if elapsed_time <= 10000: # 在10秒内进行颜色检测
blue_output = cv2.inRange(img, blue_lower, blue_upper)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (11, 11))
blue_output = cv2.dilate(blue_output, kernel)
blue_output = cv2.erode(blue_output, kernel)
contours, hierarchy = cv2.findContours(blue_output, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
area1 = cv2.contourArea(contour)
blue = (255, 255, 0)
# 蓝色矿圈定范围
if area1 > 5000:
x, y, w, h = cv2.boundingRect(contour)
perceived_width = w
# 计算距离
distance = calculate_distance(KNOWN_WIDTH, focal_length, perceived_width)
# 在图像上绘制距离
cv2.putText(img, f"Distance: {distance:.2f} m", (x, y - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
img = cv2.rectangle(img, (x, y), (x + w, y + h), blue, 3)
print(f"蓝球距离 {distance:.2f} m")
# 上述为蓝色检测代码
red_output = cv2.inRange(img, red_lower, red_upper)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (11, 11))
red_output = cv2.dilate(red_output, kernel)
red_output = cv2.erode(red_output, kernel)
contours, hierarchy = cv2.findContours(red_output, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
area2 = cv2.contourArea(contour)
red = (0, 0, 255)
# 红色矿圈定范围
if area2 > 5000:
x, y, w, h = cv2.boundingRect(contour)
perceived_width = w
# 计算距离
distance = calculate_distance(KNOWN_WIDTH, focal_length, perceived_width)
# 在图像上绘制距离
cv2.putText(img, f"Distance: {distance:.2f} m", (x, y - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
img = cv2.rectangle(img, (x, y), (x + w, y + h), red, 3)
print(f"红球距离: {distance:.2f} m")
# 上述为红色检测代码
yellow_output = cv2.inRange(img, yellow_lower, yellow_upper)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (11, 11))
yellow_output = cv2.dilate(yellow_output, kernel)
yellow_output = cv2.erode(yellow_output, kernel)
contours, hierarchy = cv2.findContours(yellow_output, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
area3 = cv2.contourArea(contour)
yellow = (51, 222, 230)
# 黄色矿圈定范围
if area3 > 5000:
x, y, w, h = cv2.boundingRect(contour)
perceived_width = w
# 计算距离
distance = calculate_distance(KNOWN_WIDTH, focal_length, perceived_width)
# 在图像上绘制距离
cv2.putText(img, f"Distance: {distance:.2f} m", (x, y - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
img = cv2.rectangle(img, (x, y), (x + w, y + h), yellow, 3)
print(f"黄球距离: {distance:.2f} m")
# 上述为黄球检测代码
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
decoded_img = zcode.decodeDisplay(gray)
cv2.imshow('kouge love you', decoded_img)
else: # 10秒后开始形状识别
blue_mask = cv2.inRange(img, blue_lower, blue_upper)
img = shape.detect_and_draw_shapes(img, blue_mask, (255, 255, 0), "Blue")
red_mask = cv2.inRange(img, red_lower, red_upper)
img = shape.detect_and_draw_shapes(img, red_mask, (0, 0, 255), "Red")
cv2.imshow('rectangle detecting', img)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
decoded_img = zcode.decodeDisplay(gray)
cv2.imshow('kouge love you', decoded_img)
if cv2.waitKey(1) == ord('q'): # 按Q跳出执行
break
cap.release()
cv2.destroyAllWindows()
if __name__ == "__main__":
main() # 执行的为检测大类函数(包括形状，颜色，二维码)