예측모듈 수정

 import cv2
 import cv2
-import numpy as np
-import matplotlib as plt
-import time
-fname = "./croppedimg/"
-index = 0
-prevTime = 0
-def filter_img(img):
-    #이미지의 RGB값을 분석하여 찾는 실내 Tag가 맞는지 판별
-    img = cv2.resize(img, (10,10))
-    first = [0,0,0]
-    for x_loc in range(0, 10):
-        for y_loc in range(0, 10):
-            bgr_value = img[x_loc,y_loc]
-            first=first+bgr_value
-    first[0] = first[0]/100
-    first[1] = first[1]/100
-    first[2] = first[2]/100
-    blue = first[0]<200 and first[0]>120
-    green = first[1]>120 and first[1]<210
-    red = first[2]>130 and first[2]<230
 
 
-    if(blue and green and red):
-        return True
-    else:
-        return False
-def bboxes(inp,prevTime):
-    #Frame을 인자로 전달받음
-    img = inp
-    start = time.time()
-    curTime = time.time()
-    # img2gray = cv2.imread(fname,0)
-    # img = cv2.namedWindow(img,cv2.WINDOW_NORMAL)
-    # img = cv2.resizeWindow(img,600,600)
-    img_final = inp
-    # img_final = cv2.namedWindow(fname,cv2.WINDOW_NORMAL)
-    # img_final = cv2.resizeWindow(fname,600,600)
-    img2gray = cv2.cvtColor(inp, cv2.COLOR_BGR2GRAY) #GRAY Image 8bit per pixel
-    ret, mask = cv2.threshold(img2gray, 180, 255, cv2.THRESH_BINARY) #threshold : distinguish background, object
-    image_final = cv2.bitwise_and(img2gray, img2gray, mask=mask) #bitwise
-    ret, new_img = cv2.threshold(img_final, 180, 255, cv2.THRESH_BINARY)  # Nfor black text , cv.THRESH_BINARY_IV
-    newimg = cv2.cvtColor(new_img, cv2.COLOR_BGR2GRAY) #Gray Image converting
-    #newimg = cv2.GaussianBlur(newimg, (3,3),0)
-
-    # remove noise from image
-    #kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (5,1))
-    # to manipulate the orientation of dilution , large x means horizonatally dilating  more, large y means vertically dilating more
-    #dilated = cv2.dilate(newimg, kernel, iterations=1)  # dilate
-   # erode = cv2.erode(newimg, kernel)
-    _,contours, _ = cv2.findContours(newimg, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)  # get contours
-    #cv2.CHAIN_APPROX_NONE: 모든 컨투어 포인트를 반환
-    for contour in contours:
-        # get rectangle bounding contour
-        [x, y, w, h] = cv2.boundingRect(contour)
-        # remove small false positives that aren't textq
-        # text인식하기. width, height
-        #if w > 50 or h > 35 or w<25:
-            #continue
-        if h / w > 1.0 or w / h > 2.0:
-            continue
-        #if h>40 or w>70:
-            #continue
-        if y>150:
-            continue
-        cropped = img_final[y :y +  h , x : x + w]
-        # draw rectangle around contour on original image
-        if(filter_img(cropped)):
-            cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 3)
-            cv2.putText(img,"cropped", (x-50,y-10), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0,0,255), 1)
-            #cropped = img_final[y :y +  h , x : x + w]
-        else:
-            continue
-    img = cv2.resize(img, (720, 380))
-    sec = curTime - prevTime
-    prevTime = curTime
-    fps = 1/(sec)
-    #print ("Time {0} ".format(sec))
-    #print ("Estimated fps {0} ".format(fps))
-    str1 = ("FPS : {0}".format(int(fps)))
-    cv2.putText(img, str1, (0, 40), cv2.FONT_HERSHEY_COMPLEX_SMALL, 0.8, (0, 255, 0),1)
-    cv2.imshow('captcha_result', img)
-    return prevTime
-
-cap = cv2.VideoCapture(0) #동영상 파일 읽어옴
+cap = cv2.VideoCapture("http://192.168.35.87:8091/?action=stream")  # 동영상 파일 읽어옴
+print(cap.isOpened())
 while (cap.isOpened()):
 while (cap.isOpened()):
-    ret, inp = cap.read() #프레임을 읽어옴, 읽어온 프레임을 인자로 bboxes 전달
-    if(ret): #success boolean
-        prevTime = bboxes(inp, prevTime)
-    if cv2.waitKey(1) & 0xFF == ord('q'):
-        print("Terminate Process..")
-        break
-cap.release() #파일 닫아줌
\ No newline at end of file
+    ret, inp = cap.read()  # 프레임을 읽어옴, 읽어온 프레임을 인자로 bboxes 전달
+    if (ret):  # success boolean
+        cv2.imshow(inp)
+cap.release()  # 파일 닫아줌

@@ -11,6 +11,7 @@ import numpy as np
 from queue import Full, Empty
 from queue import Full, Empty
 from multiprocessing import Process, Queue
 from multiprocessing import Process, Queue
 
 
+
 class LabelingModule:
 class LabelingModule:
     def __init__(self):
     def __init__(self):
         # self.model1 = load_model('svhn_model.h5')
         # self.model1 = load_model('svhn_model.h5')
@@ -18,8 +19,7 @@ class LabelingModule:
         self.image_queue = Queue(maxsize=3000)
         self.image_queue = Queue(maxsize=3000)
         self.label_queue = Queue(maxsize=10)
         self.label_queue = Queue(maxsize=10)
         self.signal_queue = Queue()
         self.signal_queue = Queue()
-        self.predict_process = Process(target=_predict, \
-            args=(self.model2, self.image_queue, self.label_queue, self.signal_queue))
+        self.predict_process = Process(target=_predict, args=(self.model2, self.image_queue, self.label_queue, self.signal_queue))
 
 
     def run(self):
     def run(self):
         self.predict_process.start()
         self.predict_process.start()
@@ -49,6 +49,7 @@ class LabelingModule:
         img_tensor = img_tensor - img_tensor.mean()
         img_tensor = img_tensor - img_tensor.mean()
         return img_tensor
         return img_tensor
 
 
+
 def _predict(model, input_queue, output_queue, signal_queue):
 def _predict(model, input_queue, output_queue, signal_queue):
     print('predict process started.')
     print('predict process started.')
     while True:
     while True:
@@ -58,7 +59,7 @@ def _predict(model, input_queue, output_queue, signal_queue):
                 break
                 break
         except Empty:
         except Empty:
             pass
             pass
-        
+
         tensor = input_queue.get(timeout=-1)
         tensor = input_queue.get(timeout=-1)
         dat = model.predict(np.array([tensor]))
         dat = model.predict(np.array([tensor]))
         o1 = np.argmax(dat[0])
         o1 = np.argmax(dat[0])