.

.gitignore

@@ -2,3 +2,5 @@
 generate test footage/images/
 
 generate test footage/3.MP4
+
+short.mp4

Analyzer.py

@@ -1,3 +1,3 @@
 class Analyzer:
-    def __init__(self):
+    def __init__(self, footage):
         print("Analyzer constructed")

ContourExctractor.py

@@ -10,6 +10,10 @@ import traceback
 import _thread
 import imageio
 import numpy as np
+from threading import Thread
+from multiprocessing import Queue, Process, Pool
+from multiprocessing.pool import ThreadPool
+import concurrent.futures
 
 class ContourExtractor:
 
@@ -28,9 +32,7 @@ class ContourExtractor:
         print("ContourExtractor initiated")
 
     def extractContours(self, videoPath, resizeWidth):
-        min_area = self.min_area
-        max_area = self.max_area
-        threashold = self.threashold
+
 
         # initialize the first frame in the video stream
         vs = cv2.VideoCapture(videoPath)
@@ -40,57 +42,72 @@ class ContourExtractor:
         self.yDim = image.shape[0]
         firstFrame = None
         # loop over the frames of the video
-        frameCount = 0
+        frameCount = -1
         extractedContours = dict()
-        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")
-                break
         
-            frame = imutils.resize(frame, width=resizeWidth)
+        results = []
+        extractedContours = dict()
 
-            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2LAB)
-            gray = np.asarray(gray[:,:,1]/2 + gray[:,:,2]/2).astype(np.uint8)
+        imageBuffer = []
         
-            #gray = cv2.GaussianBlur(gray, (5, 5), 0)
+        with concurrent.futures.ProcessPoolExecutor() as executor:
+            while res:
+                frameCount += 1
+                if frameCount % (60*30) == 0:
+                    print("Minutes processed: ", frameCount/(60*30))
                 
-            # if the first frame is None, initialize it
-            if firstFrame is None:
-                firstFrame = gray
-                continue
 
-            frameDelta = cv2.absdiff(gray, firstFrame)
+                res, frame = vs.read()
+                # resize the frame, convert it to grayscale, and blur it
+                if frame is None:
+                    print("ContourExtractor: frame was None")
+                    break
 
-            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)
+                frame = imutils.resize(frame, width=resizeWidth)
+                gray = cv2.cvtColor(frame, cv2.COLOR_BGR2LAB)
 
-            contours = []
-            for c in cnts:
-                ca = cv2.contourArea(c)
-                if ca < min_area or ca > max_area:
+                # if the first frame is None, initialize it
+                if firstFrame is None:
+                    gray = np.asarray(gray[:,:,1]/2 + gray[:,:,2]/2).astype(np.uint8)       
+                    gray = cv2.GaussianBlur(gray, (5, 5), 0)
+                    firstFrame = gray
                     continue
-                (x, y, w, h) = cv2.boundingRect(c)
-                #print((x, y, w, h))
-                contours.append((x, y, w, h))
 
-                #cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
-            if len(contours) != 0:
-                extractedContours[frameCount] = contours
+                results.append(executor.submit(self.getContours, frameCount, gray, firstFrame))
 
-            if frameCount % (60*30) == 0:
-                print("Minutes processed: ", frameCount/(60*30))
-            frameCount += 1
+                #contours = self.getContours(frameCount, gray, firstFrame)
+
+            for f in concurrent.futures.as_completed(results):
+                x=f.result()
+                if x is not None:
+                    extractedContours = {**extractedContours, **x} 
         
-            #cv2.imshow( "annotated", thresh )  
-            #cv2.waitKey(10) & 0XFF
         self.extractedContours = extractedContours
         return extractedContours
             
+    def getContours(self, frameCount, gray, firstFrame):
+        gray = np.asarray(gray[:,:,1]/2 + gray[:,:,2]/2).astype(np.uint8)       
+        gray = cv2.GaussianBlur(gray, (5, 5), 0)
+        frameDelta = cv2.absdiff(gray, firstFrame)
+        thresh = cv2.threshold(frameDelta, self.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)
+
+        contours = []
+        for c in cnts:
+            ca = cv2.contourArea(c)
+            if ca < self.min_area or ca > self.max_area:
+                continue
+            (x, y, w, h) = cv2.boundingRect(c)
+            #print((x, y, w, h))
+            contours.append((x, y, w, h))
+
+        if len(contours) != 0: 
+            return {frameCount: contours}
+        else:
+            return None
 
     def displayContours(self):
         values = self.extractedContours.values()

LayerFactory.py

@@ -35,7 +35,8 @@ class LayerFactory:
             layers.append(Layer(frameNumber, contour))
 
         # inserts all the fucking contours as layers?
-        for frameNumber, contours in data.items():
+        for frameNumber in sorted(data):
+            contours = data[frameNumber]
             for (x,y,w,h) in contours:
                 foundLayer = False
                 i = 0

main.py

@@ -3,17 +3,19 @@ import time
 from ContourExctractor import ContourExtractor
 from Exporter import Exporter
 from LayerFactory import LayerFactory
+from Analyzer import Analyzer
 import cv2
 #TODO
 #   finden von relevanten Stellen anhand von zu findenen metriken für vergleichsbilder
 
 def demo():
     print("startup")
-    resizeWidth = 512
+    resizeWidth = 1024
     maxLayerLength = 1*60*30
     start = time.time()
 
-    footagePath = os.path.join(os.path.dirname(__file__), "./generate test footage/out.mp4")
+    footagePath = os.path.join(os.path.dirname(__file__), "./generate test footage/3.MP4")
+    analyzer = Analyzer(footagePath)
     contours = ContourExtractor().extractContours(footagePath, resizeWidth)
     print("Time consumed in working: ", time.time() - start)
     layerFactory = LayerFactory(contours)
@@ -21,7 +23,7 @@ def demo():
     layerFactory.sortLayers()
     layerFactory.fillLayers(footagePath)
     underlay = cv2.VideoCapture(footagePath).read()[1]
-    Exporter().exportLayers(underlay, layerFactory.layers, os.path.join(os.path.dirname(__file__), "./short.mp4"), resizeWidth)
+    Exporter().exportOverlayed(underlay, layerFactory.layers, os.path.join(os.path.dirname(__file__), "./short.mp4"), resizeWidth)
     print("Total time: ", time.time() - start)
 def init():
     print("not needed yet")