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gui_program.py
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gui_program.py
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import cv2
import threading
import sys
from PyQt5.QtWidgets import (QApplication, QWidget, QGridLayout, QLabel, QLineEdit, QTextEdit, QCheckBox, QPushButton, QHBoxLayout, QVBoxLayout)
from PyQt5 import QtGui
from PyQt5 import QtCore
from PyQt5.QtCore import Qt, QTimer
import matplotlib
import imutils
import timeit
import numpy as np
import serial
import time
import json
from collections import deque
import tensorflow as tf
from tensorflow.keras.models import Model, load_model
port = '/dev/ttyUSB0' # 시리얼 포트
baud = 9600 # 시리얼 보드레이트(통신속도)
msg = {'conveyor_step' : 0, 'sorting_step' : 0}
end_str = '\n'
memory = deque()
img_list = [0, 0]
model = tf.saved_model.load('LegoNet_V7_FP16')
lock = threading.Lock()
class MyApp(QWidget):
def __init__(self):
super().__init__()
self.initUI()
self.opencv_apply_flag = False
self.Dnn_infer_apply_flag = False
self.run_flag = False
self.morph_flag = False
def initUI(self):
self.Name_label = QLabel('Machine Vision System V1')
self.live_checkbox = QCheckBox('Live On/Off')
self.serial_btn = QPushButton('Serial Connect')
self.model_load_btn = QPushButton('Model Load')
self.Opencv_checkbox = QCheckBox('Opencv On/OFF')
self.Dnn_infer_checkbox = QCheckBox('DNN Infer On/OFF')
self.image_label = QLabel('image view')
self.edge_label = QLabel('Edge view')
self.detect_label0 = QLabel('detect view0')
self.detect_label1 = QLabel('detect view1')
self.detect_label2 = QLabel('detect view2')
self.detect_label3 = QLabel('detect view3')
self.detect_label4 = QLabel('detect view4')
self.detect_label5 = QLabel('detect view5')
self.logwindow = QTextEdit()
self.logwindow.setAcceptRichText(False)
self.logwindow.setPlainText("Log start !!!")
Name_label_font = self.Name_label.font()
Name_label_font.setBold(True)
Name_label_font.setPointSize(20)
self.Name_label.setFont(Name_label_font)
self.image_label.resize(640, 480)
self.edge_label.resize(640, 480)
self.detect_label0.resize(100, 100)
self.detect_label1.resize(100, 100)
self.detect_label2.resize(100, 100)
self.detect_label3.resize(100, 100)
self.detect_label4.resize(100, 100)
self.detect_label5.resize(100, 100)
base_img = cv2.imread('keras.jpg')
base_img = cv2.cvtColor(base_img, cv2.COLOR_BGR2RGB)
base_img = cv2.resize(base_img, dsize=(640, 480), interpolation=cv2.INTER_AREA)
base_h, base_w, base_c = base_img.shape
base_qImg = QtGui.QImage(base_img.data, base_w, base_h, base_w*base_c, QtGui.QImage.Format_RGB888)
base_pixmap = QtGui.QPixmap.fromImage(base_qImg)
self.image_label.setPixmap(base_pixmap)
grid = QGridLayout()
grid.addWidget(self.detect_label0, 0,0)
grid.addWidget(self.detect_label1, 0,1)
grid.addWidget(self.detect_label2, 0,2)
grid.addWidget(self.detect_label3, 1,0)
grid.addWidget(self.detect_label4, 1,1)
grid.addWidget(self.detect_label5, 1,2)
hbox1 = QHBoxLayout()
hbox1.addWidget(self.Name_label)
hbox1.addStretch(2)
hbox2 = QHBoxLayout()
hbox2.addWidget(self.live_checkbox)
hbox2.addWidget(self.serial_btn)
hbox2.addWidget(self.model_load_btn)
hbox2.addWidget(self.Opencv_checkbox)
hbox2.addWidget(self.Dnn_infer_checkbox)
hbox2.addStretch(2)
hbox3 = QHBoxLayout()
hbox3.addWidget(self.image_label)
hbox3.addWidget(self.logwindow)
hbox4 = QHBoxLayout()
hbox4.addWidget(self.edge_label)
hbox4.addLayout(grid)
vbox = QVBoxLayout()
vbox.addStretch(0.1)
vbox.addLayout(hbox1)
vbox.addLayout(hbox2)
vbox.addLayout(hbox3)
vbox.addLayout(hbox4)
vbox.addStretch(1)
self.setLayout(vbox)
self.live_checkbox.stateChanged.connect(self.change_mode)
self.serial_btn.clicked.connect(self.serial_connect)
self.model_load_btn.clicked.connect(self.model_load)
self.Opencv_checkbox.stateChanged.connect(self.opencv_apply)
self.Dnn_infer_checkbox.stateChanged.connect(self.Dnn_infer_apply)
self.setWindowTitle('Machine Vision System')
self.setGeometry(300, 100, 1000, 900)
self.show()
def gstreamer_pipeline(self,
capture_width=640,
capture_height=480,
display_width=640,
display_height=480,
framerate=20,
flip_method=0,
):
return (
"nvarguscamerasrc ! "
"video/x-raw(memory:NVMM), "
"width=(int)%d, height=(int)%d, "
"format=(string)NV12, framerate=(fraction)%d/1 ! "
"nvvidconv flip-method=%d ! "
"video/x-raw, width=(int)%d, height=(int)%d, format=(string)BGRx ! "
"videoconvert ! "
"video/x-raw, format=(string)BGR ! appsink"
% (
capture_width,
capture_height,
framerate,
flip_method,
display_width,
display_height,
)
)
def clamp(self, val):
if val < 0:
val = 0
return val
def saturare(frame):
saturate_val = 70
imghsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
(h, s, v) = cv2.split(imghsv)
s = s + saturate_val
s = numpy.clip(s, 0, 255)
imghsv = cv2.merge([h, s, v])
imgrgb = cv2.cvtColor(imghsv.astype("uint8"), cv2.COLOR_HSV2BGR)
return imgrgb
def video_run(self):
global running
cap = cv2.VideoCapture(self.gstreamer_pipeline(flip_method=0), cv2.CAP_GSTREAMER)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
kernel1 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11, 11))
while running:
self.run_flag = True
lock.acquire()
ret, frame = cap.read()
start_t = timeit.default_timer()
if not ret:
print("영상 종료")
break
video_fps = cv2.CAP_PROP_FPS
frame = imutils.resize(frame, width=480)
#frame = imutils.rotate(frame, angle=-90)
#frame = frame[0:400, 160:560]
origin_frame = frame.copy()
if self.opencv_apply_flag == True:
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
#lab = cv2.cvtColor(frame, cv2.COLOR_BGR2LAB)
#L = lab[:, :, 0]
#med_L = cv2.medianBlur(L, 99)
#invert_L = cv2.bitwise_not(med_L)
#composed = cv2.addWeighted(gray, 0.6, invert_L, 0.4, 0)
#blur = cv2.GaussianBlur(gray, (5, 5), 0)
morph_grad = cv2.morphologyEx(gray, cv2.MORPH_GRADIENT, kernel1)
binary = cv2.adaptiveThreshold(morph_grad, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV,45,4)
median_blur = cv2.medianBlur(binary, 7)
morph_close = cv2.morphologyEx(median_blur, cv2.MORPH_CLOSE, kernel1)
contours, _ = cv2.findContours(morph_close, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if (len(contours) > 0):
margin = 10
real_contours = contours[0]
max = 0
for i in range(0, len(contours)):
area = cv2.contourArea(contours[i])
if max < area:
max = area
real_contours = contours[i]
cnt = real_contours
x, y, w, h = cv2.boundingRect(cnt)
if w > h: # 물체가 rect 중앙에 오게함
y = int(y - ((w - h) / 2))
h = int(h + (((w - h) / 2) * 2))
else:
x = int(x - ((h - w) / 2))
w = int(w + (((h - w) / 2) * 2))
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 3)
if w > 30 and h > 30:
crop_fist_x = self.clamp(x - margin)
crop_fist_y = self.clamp(y - margin)
crop_twice_x = self.clamp(x + w + margin)
crop_twice_y = self.clamp(y + h + margin)
cropped_img = origin_frame[crop_fist_y: crop_twice_y, crop_fist_x: crop_twice_x]
cropped_img = cv2.resize(cropped_img, dsize=(88, 88), interpolation=cv2.INTER_LINEAR)
cropped_img = cv2.cvtColor(cropped_img, cv2.COLOR_BGR2GRAY)
clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8))
img_clahe = clahe.apply(cropped_img)
memory.append(cropped_img)
if len(memory) > 7:
if abs(cropped_img.shape[0] - cropped_img.shape[1]) > 10: # width 와 height 가 10이상 차이나면 저장 x
continue
if self.Dnn_infer_apply_flag == True:
cropped_img = cropped_img / 255
merge_img = cropped_img.reshape((1, 88, 88, 1))
infer = model.signatures["serving_default"]
predict = infer(tf.convert_to_tensor(merge_img, dtype = tf.float32))
#predict = model.predict(merge_img)
predict_tensor = predict['dense_1']
predict_np = predict_tensor.numpy()
#print(predict_np.shape)
#print(predict_np)
yhat = np.argmax(predict_np)
#print(yhat)
if yhat == 0:
print("1")
cv2.putText(frame, "1", (crop_fist_x, crop_fist_y), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (102, 0, 153))
str_1x1 = str(round((predict_np[0][0]), 4)) +'%'
cv2.putText(frame,str_1x1 , (crop_fist_x + 115, crop_fist_y), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255))
msg['sorting_step'] = 10
elif yhat == 1:
print("10")
cv2.putText(frame, "10", (crop_fist_x, crop_fist_y), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (102, 0, 153))
str_1x4 = str(round((predict_np[0][1]), 4)) +'%'
cv2.putText(frame,str_1x4 , (crop_fist_x + 115, crop_fist_y ), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255))
msg['sorting_step'] = 30
elif yhat == 2:
print("11")
cv2.putText(frame, "11", (crop_fist_x, crop_fist_y), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (102, 0, 153))
str_2x6 = str(round((predict_np[0][2]), 4)) +'%'
cv2.putText(frame,str_2x6 , (crop_fist_x + 115, crop_fist_y ), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255))
msg['sorting_step'] = 50
elif yhat == 3:
print("2")
cv2.putText(frame, "2", (crop_fist_x, crop_fist_y), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (102, 0, 153))
str_2x6 = str(round((predict_np[0][3]), 4)) +'%'
cv2.putText(frame,str_2x6 , (crop_fist_x + 115, crop_fist_y ), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255))
msg['sorting_step'] = 70
elif yhat == 4:
print("3")
cv2.putText(frame, "3", (crop_fist_x, crop_fist_y), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (102, 0, 153))
str_2x6 = str(round((predict_np[0][4]), 4)) +'%'
cv2.putText(frame,str_2x6 , (crop_fist_x + 115, crop_fist_y ), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255))
msg['sorting_step'] = 90
elif yhat == 5:
print("4")
cv2.putText(frame, "4", (crop_fist_x, crop_fist_y), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (102, 0, 153))
str_2x6 = str(round((predict_np[0][5]), 4)) +'%'
cv2.putText(frame,str_2x6 , (crop_fist_x + 115, crop_fist_y ), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255))
msg['sorting_step'] = 110
elif yhat == 6:
print("5")
cv2.putText(frame, "5", (crop_fist_x, crop_fist_y), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (102, 0, 153))
str_2x6 = str(round((predict_np[0][6]), 4)) +'%'
cv2.putText(frame,str_2x6 , (crop_fist_x + 115, crop_fist_y ), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255))
msg['sorting_step'] = 130
elif yhat == 7:
print("6")
cv2.putText(frame, "6", (crop_fist_x, crop_fist_y), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (102, 0, 153))
str_2x6 = str(round((predict_np[0][7]), 4)) +'%'
cv2.putText(frame,str_2x6 , (crop_fist_x + 115, crop_fist_y ), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255))
msg['sorting_step'] = 145
elif yhat == 8:
print("7")
cv2.putText(frame, "7", (crop_fist_x, crop_fist_y), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (102, 0, 153))
str_2x6 = str(round((predict_np[0][8]), 4)) +'%'
cv2.putText(frame,str_2x6 , (crop_fist_x + 115, crop_fist_y ), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255))
msg['sorting_step'] = 160
elif yhat == 9:
print("8")
cv2.putText(frame, "8", (crop_fist_x, crop_fist_y), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (102, 0, 153))
str_2x6 = str(round((predict_np[0][9]), 4)) +'%'
cv2.putText(frame,str_2x6 , (crop_fist_x + 115, crop_fist_y ), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255))
msg['sorting_step'] = 175
elif yhat == 10:
print("9")
cv2.putText(frame, "9", (crop_fist_x, crop_fist_y), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (102, 0, 153))
str_2x6 = str(round((predict_np[0][10]), 4)) +'%'
cv2.putText(frame,str_2x6 , (crop_fist_x + 115, crop_fist_y ), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255))
msg['sorting_step'] = 180
else:
print("base")
json_msg = json.dumps(msg)
print(json_msg)
self.ser.write(json_msg.encode())
self.ser.write(end_str.encode())
img_list[1] = morph_close
self.morph_flag = True
terminate_t = timeit.default_timer()
FPS = int(1. / (terminate_t - start_t))
fps_str = "FPS : %0.1f" % FPS
cv2.putText(frame, fps_str, (0, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0))
img_list[0] = frame
QApplication.processEvents()
lock.release()
cap.release()
self.run_flag = False
print("Thread end.")
def stop(self):
global running
running = False
print("stoped..")
def start(self):
global running
running = True
th = threading.Thread(target=self.video_run)
th.daemon = True
th.start()
print("started..")
def onExit(self):
print("exit")
stop()
def change_mode(self, state):
if state == Qt.Checked:
self.start()
else:
self.stop()
print(state)
def serial_connect(self):
self.logwindow.append("Serial Connect...")
self.serial_btn.toggle()
try:
self.ser = serial.Serial(port,baud)
self.logwindow.append("Ok!!")
except:
self.logwindow.append("Failed...")
def model_load(self):
print("model_load_thread start!!!")
def opencv_apply(self, state):
if state == Qt.Checked:
self.opencv_apply_flag = True
self.logwindow.append("Opencv apply On!")
else:
self.opencv_apply_flag = False
self.logwindow.append("Opencv apply Off!")
def Dnn_infer_apply(self, state):
if state == Qt.Checked:
self.Dnn_infer_apply_flag = True
self.logwindow.append("Dnn_infer apply On!")
else:
self.Dnn_infer_apply_flag = False
self.logwindow.append("Dnn_infer apply Off!")
def draw_ui(self):
if len(img_list) > 1:
if self.run_flag == True:
frame = img_list[0]
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
h,w,c = frame.shape
qImg = QtGui.QImage(frame.data, w, h, w*c, QtGui.QImage.Format_RGB888)
pixmap = QtGui.QPixmap.fromImage(qImg)
self.image_label.setPixmap(pixmap)
if self.morph_flag == True:
morph_close = img_list[1]
morph_close = cv2.cvtColor(morph_close, cv2.COLOR_BGR2RGB)
morph_h,morph_w,morph_c = morph_close.shape
morph_qImg = QtGui.QImage(morph_close.data, morph_w, morph_h, morph_w*morph_c, QtGui.QImage.Format_RGB888)
morph_pixmap = QtGui.QPixmap.fromImage(morph_qImg)
self.edge_label.setPixmap(morph_pixmap)
if self.opencv_apply_flag == True:
for i in range(0, len(memory)):
detect_img = cv2.cvtColor(memory[i], cv2.COLOR_BGR2RGB)
h,w,c = detect_img.shape
qImg = QtGui.QImage(detect_img.data, w, h, w*c, QtGui.QImage.Format_RGB888)
pixmap = QtGui.QPixmap.fromImage(qImg)
if i == 0:
self.detect_label0.setPixmap(pixmap)
elif i == 1:
self.detect_label1.setPixmap(pixmap)
elif i == 2:
self.detect_label2.setPixmap(pixmap)
elif i == 3:
self.detect_label3.setPixmap(pixmap)
elif i == 4:
self.detect_label4.setPixmap(pixmap)
elif i == 5:
self.detect_label5.setPixmap(pixmap)
if __name__ == '__main__':
app = QApplication(sys.argv)
ex = MyApp()
Timer = QTimer()
Timer.setInterval(1)
Timer.timeout.connect(lambda:ex.draw_ui())
Timer.start()
sys.exit(app.exec_())