utilized exporter module

ContourExctractor.py

@@ -15,30 +15,36 @@ class ContourExtractor:
 
     #X = {frame_number: [(contour, (x,y,w,h)), ...], }
     extractedContours = dict()
+    min_area = 990
+    max_area = 30000
+    threashold = 25
+    xDim = 0
+    yDim = 0
 
     def __init__(self, videoPath):
         print("ContourExtractor initiated")
 
 
-        min_area = 100
+        min_area = self.min_area
-        max_area = 30000
+        max_area = self.max_area
-
+        threashold = self.threashold
-        threashold = 10
 
         # initialize the first frame in the video stream
         vs = cv2.VideoCapture(videoPath)
 
-        res = vs.read()[0]
+        res, image = vs.read()
+        self.xDim = image.shape[1]
+        self.yDim = image.shape[0]
         firstFrame = None
         # loop over the frames of the video
         frameCount = 0
         while res:
-
             res, frame = vs.read()
-
             # resize the frame, convert it to grayscale, and blur it
             if frame is None:
+                print("ContourExtractor: frame was None")
                 return
+
             frame = imutils.resize(frame, width=500)
             cv2.imshow( "frame", frame)  
             gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
@@ -50,16 +56,12 @@ class ContourExtractor:
                 continue
 
             frameDelta = cv2.absdiff(gray, firstFrame)
-
             thresh = cv2.threshold(frameDelta, threashold, 255, cv2.THRESH_BINARY)[1]
-
-
             # dilate the thresholded image to fill in holes, then find contours
             thresh = cv2.dilate(thresh, None, iterations=3)
             cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
             cnts = imutils.grab_contours(cnts)
 
-            # loop over the contours
             contours = []
             for c in cnts:
                 if cv2.contourArea(c) < min_area or cv2.contourArea(c) > max_area:
@@ -68,8 +70,6 @@ class ContourExtractor:
                 (x, y, w, h) = cv2.boundingRect(c)
                 contours.append((frame[y:y+h, x:x+w], (x, y, w, h)))
                 #cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
-                text = "Occupied"
-
             self.extractedContours[frameCount] = contours
             frameCount += 1
 
@@ -78,27 +78,22 @@ class ContourExtractor:
             #cv2.waitKey(10) & 0XFF
     
     def displayContours(self):
-        
         values = self.extractedContours.values()
-        frame = np.zeros(shape=[1080, 1920, 3], dtype=np.uint8)
-        frame = imutils.resize(frame, width=512)
         frames = []
-        writer = imageio.get_writer(os.path.join(os.path.dirname(__file__), "./short.mp4"), fps=30)
         for xx in values:
             for v1 in xx:
                 (x, y, w, h) = v1[1]
                 v = v1[0]
-                
+                frame = np.zeros(shape=[self.yDim, self.xDim, 3], dtype=np.uint8)
+                frame = imutils.resize(frame, width=512)
                 #cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
 
                 frame[y:y+v.shape[0], x:x+v.shape[1]] = v
                 frames.append(frame)
-                writer.append_data(np.array(frame))
+               
                 #cv2.imshow("changes overlayed", frame)
                 #cv2.waitKey(10) & 0XFF
         #cv2.waitKey(0)
         #cv2.destroyAllWindows()
-
-        writer.close()
         return frames
 

Exporter.py

@@ -2,11 +2,14 @@ import imageio
 import numpy as np
 class Exporter:
     fps = 30
-    def __init__(self, data, outputPath):
+
+    def __init__(self):
         print("Exporter initiated")
+
+    def export(self, frames, outputPath):
         fps = self.fps
         writer = imageio.get_writer(outputPath, fps=fps)
-        for frame in data:
+        for frame in frames:
             writer.append_data(np.array(frame))
 
         writer.close()

main.py

@@ -4,20 +4,21 @@ from ContourExctractor import ContourExtractor
 from Exporter import Exporter
 #TODO
 #   finden von relevanten Stellen anhand von zu findenen metriken für vergleichsbilder
-#   diff zu den ref bildern aufnehmen
+# X diff zu den ref bildern aufnehmen
-#   zeichenn on contour inhalt
+#   zeichen von contour inhalt
 #   langes video
 
 def demo():
     print("startup")
-    footagePath = os.path.join(os.path.dirname(__file__), "./generate test footage/out.mp4")
+    footagePath = os.path.join(os.path.dirname(__file__), "./generate test footage/2.mp4")
 
     start = time.time()
     contourExtractor = ContourExtractor(footagePath)
     print("Time consumed in working: ",time.time() - start)
 
     frames = contourExtractor.displayContours()
-    #exporter = Exporter(frames,os.path.join(os.path.dirname(__file__), "./short.mp4"))
+    Exporter().export(frames,os.path.join(os.path.dirname(__file__), "./short.mp4"))
+
 
 def init():
     print("not needed yet")