docs

Application/Config.py

@@ -1,7 +1,7 @@
 
 class Config:
     c = {
-        "min_area" : 5000,
+        "min_area" : 50,
         "max_area" : 40000,
         "threashold" : 7,
         "resizeWidth" : 512,
@@ -11,7 +11,7 @@ class Config:
         "minLayerLength": 40,    
         "tolerance": 20,
         "maxLength": None,
-        "ttolerance": 20,
+        "ttolerance": 60,
         "videoBufferLength": 500,
         "LayersPerContour": 220,
         "avgNum":10

Application/ContourExctractor.py

@@ -50,19 +50,21 @@ class ContourExtractor:
 
     def extractContours(self):      
         videoReader = VideoReader(self.config)    
+        self.fps = videoReader.getFPS()
+        self.length = videoReader.getLength()
         videoReader.fillBuffer()
 
         threads = self.config["videoBufferLength"]
         self.start = time.time()
         # start a bunch of frames and let them read from the video reader buffer until the video reader reaches EOF
-        with ThreadPool(16) as pool:
+        with ThreadPool(2) as pool:
             while not videoReader.videoEnded():
                 if videoReader.buffer.qsize() == 0:
                     time.sleep(.5)
 
                 tmpData = [videoReader.pop() for i in range(0, videoReader.buffer.qsize())]
-                self.computeMovingAverage(tmpData)
+                pool.map(self.computeMovingAverage, (tmpData,))
-                pool.map_async(self.getContours, tmpData)
+                pool.map(self.async2, (tmpData,))
                 #for data in tmpData:
                 #    self.getContours(data)
                 frameCount = tmpData[-1][0]
@@ -70,6 +72,10 @@ class ContourExtractor:
         videoReader.thread.join()
         return self.extractedContours, self.extractedMasks
 
+    def async2(self, tmpData):
+        with ThreadPool(16) as pool2:
+            pool2.map(self.getContours, tmpData)
+
     def getContours(self, data):
         frameCount, frame = data
         # wait for the reference frame, which is calculated by averaging some revious frames
@@ -77,8 +83,8 @@ class ContourExtractor:
             time.sleep(0.1)
         firstFrame = self.averages.pop(frameCount, None)
        
-        if frameCount % (60*30) == 1:
+        if frameCount % (10*self.fps) == 1:
-            print(f" \r {frameCount/(60*30)} Minutes processed in {round((time.time() - self.start)/(frameCount/(60*30)), 2)} each", end='\r')
+            print(f" \r {round((frameCount/self.fps)/self.length, 4)*100} % processed in {round(time.time() - self.start, 2)}s", end='\r')
 
         gray = self.prepareFrame(frame)
         frameDelta = cv2.absdiff(gray, firstFrame)
@@ -88,7 +94,6 @@ class ContourExtractor:
         #cv2.imshow("changes x", thresh)
         #cv2.waitKey(10) & 0XFF
         cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-        self.diff.append(np.count_nonzero(thresh))
         cnts = imutils.grab_contours(cnts)
 
         contours = []

Application/VideoReader.py

@@ -28,6 +28,7 @@ class VideoReader:
         self.stopped = False
         self.getWH()
         self.calcFPS()
+        self.calcLength()
         self.calcStartTime()
         if setOfFrames is not None:
             self.listOfFrames = sorted(setOfFrames)      
@@ -103,12 +104,22 @@ class VideoReader:
         self.fps = self.vc.get(cv2.CAP_PROP_FPS)
 
     def getFPS(self):
+        if self.fps is None:
+            self.calcFPS()
         return self.fps
     
+    def calcLength(self):
+        fc = int(self.vc.get(cv2.CAP_PROP_FRAME_COUNT))
+        self.length = fc / self.getFPS()
+
+    def getLength(self):
+        if self.length is None:
+            self.calcLength()
+        return self.length
+
     def calcStartTime(self):
         starttime = os.stat(self.videoPath).st_mtime
-        fc = int(self.vc.get(cv2.CAP_PROP_FRAME_COUNT))
+        length = self.getLength()
-        length = fc / self.getFPS()
         starttime = starttime - length
         self.starttime = starttime
 

README.md

@@ -1,12 +1,18 @@
 
-# wip
+# Video Synopsis and Classification
+
+## Example:
+![docs/demo.gif](./docs/demo.gif)  
+What you see above is a 15 second exerpt of a 2 minute overlayed synopsis of a 2.5h video from an on campus web cam.  
+The synopsis took 40 minutes from start to finish on a 8 core machine and used a maximum of 6Gb of RAM.
+
+## Benchmark
+Below you can find the benchmark results for a 10 minutes clip, with the stacked time per componenent on the x-axis.  
+The tests were done on a machine with a Ryzen 3700X with 8 cores 16 threads and 32 Gb of RAM.
+![docs/demo.gif](./docs/bm.jpg)  
+
+
 
 #### notes:
 
-Time consumed reading video:  369.0188868045807s     3.06GB 26min 1080p downscaled 500p 30fps
-Time consumed reading video:  240.s     3.06GB 26min 1080p downscaled 500p 30fps when multithreaded
-contour extraction: 10.5 Sec. when only 2 Threads
-                    8 secs when also mapping getContours()
-                    
-
 install tensorflow==1.15.0 and tensorflow-gpu==1.15.0, cuda 10.2 and 10.0, copy missing files from 10.0 to 10.2, restart computer, set maximum vram

docs/plot.py

@@ -1,22 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-import pandas as pd
-[
-    {"threads": "32, 16",  "Contour Extractor": 106.0405707359314,  "Layer Factory": 0.5175004005432129, "Layer Manager": 1.4864997863769531, "Exporter": 34.591999769210815},
-    {"threads": "1, 1",    "Contour Extractor": 463.66750025749207, "Layer Factory": 0.5290005207061768, "Layer Manager": 1.551999807357788,  "Exporter": 34.5339994430542},
-    {"threads": "1, 4",    "Contour Extractor": 193.51898574829102, "Layer Factory": 0.5249984264373779, "Layer Manager": 1.5125021934509277, "Exporter": 35.06749963760376},
-    {"threads": "4, 4",    "Contour Extractor": 186.46951842308044, "Layer Factory": 0.5295009613037109, "Layer Manager": 1.545145034790039,  "Exporter": 34.260000705718994},
-    {"threads": "4, 8",    "Contour Extractor": 126.90402793884277, "Layer Factory": 0.5275006294250488, "Layer Manager": 1.536078929901123,  "Exporter": 34.61099886894226},
-    {"threads": "16, 16",  "Contour Extractor": 109.17592716217041, "Layer Factory": 0.4179983139038086, "Layer Manager": 1.5620002746582031, "Exporter": 33.80550146102905},
-    
-]
-x = {
-    "threads":["1, 1","1, 4","4, 4","4, 8","16, 16", "32, 16"],
-    "Contour Extraction":[ 463.66750025749207, 193.51898574829102,186.46951842308044,126.90402793884277,109.17592716217041,106.0405707359314],
-}
-
-df = pd.DataFrame.from_dict(x)
-ax = df.plot.bar(x="threads", title="Benötigte Zeit für Konturenextraktion mit unterschiedlicher Anzahlen von Threads", figsize=(10,4))
-ax.set_xlabel("Threads für Durchschnittsbildung | Threads für Differenzberechnung")
-ax.set_ylabel("Zeit in Sekunden")
-plt.show()

docs/stats.json

@@ -1,9 +0,0 @@
-[
-    {"File Name": "c:/projects/time compression\\output\\X23-1.txt", "threads": "32, 16",  "Contour Extractor": 106.0405707359314,  "Layer Factory": 0.5175004005432129, "Layer Manager": 1.4864997863769531, "Exporter": 34.591999769210815},
-    {"File Name": "c:/projects/time compression\\output\\X23-1.txt", "threads": "1, 1",    "Contour Extractor": 463.66750025749207, "Layer Factory": 0.5290005207061768, "Layer Manager": 1.551999807357788,  "Exporter": 34.5339994430542},
-    {"File Name": "c:/projects/time compression\\output\\X23-1.txt", "threads": "1, 4",    "Contour Extractor": 193.51898574829102, "Layer Factory": 0.5249984264373779, "Layer Manager": 1.5125021934509277, "Exporter": 35.06749963760376},
-    {"File Name": "c:/projects/time compression\\output\\X23-1.txt", "threads": "4, 4",    "Contour Extractor": 186.46951842308044, "Layer Factory": 0.5295009613037109, "Layer Manager": 1.545145034790039,  "Exporter": 34.260000705718994},
-    {"File Name": "c:/projects/time compression\\output\\X23-1.txt", "threads": "4, 8",    "Contour Extractor": 126.90402793884277, "Layer Factory": 0.5275006294250488, "Layer Manager": 1.536078929901123,  "Exporter": 34.61099886894226},
-    {"File Name": "c:/projects/time compression\\output\\X23-1.txt", "threads": "16, 16",  "Contour Extractor": 109.17592716217041, "Layer Factory": 0.4179983139038086, "Layer Manager": 1.5620002746582031, "Exporter": 33.80550146102905},
-    
-]

main.py

@@ -15,7 +15,7 @@ def main():
     start = startTotal
     config = Config()
 
-    fileName = "3.mp4"
+    fileName = "x23.mp4"
     outputPath = os.path.join(os.path.dirname(__file__), "output")
     dirName = os.path.join(os.path.dirname(__file__), "generate test footage")
 
@@ -25,26 +25,20 @@ def main():
     config["importPath"] = os.path.join(outputPath, fileName.split(".")[0] + ".txt")
 
     config["w"], config["h"] = VideoReader(config).getWH()
-    stats = []
+
     if not os.path.exists(config["importPath"]):
         contours, masks = ContourExtractor(config).extractContours()
-        stats.append(time.time() - start)
-        start = time.time()
 
-        print("Time consumed extracting contours: ", stats["Contour Extractor"])
         layerFactory = LayerFactory(config)
         layers = layerFactory.extractLayers(contours, masks)
-        stats.append(time.time() - start)
-        start = time.time()
     else:
         layers, contours, masks = Importer(config).importRawData()
-        #layerFactory = LayerFactory(config)
+        layerFactory = LayerFactory(config)
-        #layers = layerFactory.extractLayers(contours, masks)
+        layers = layerFactory.extractLayers(contours, masks)
 
     layerManager = LayerManager(config, layers)
     layerManager.transformLayers()
-    stats.append(time.time() - start)
+
-    start = time.time()
 
     #layerManager.tagLayers()
     layers = layerManager.layers
@@ -54,11 +48,8 @@ def main():
     exporter = Exporter(config)
     print(f"Exporting {len(contours)} Contours and {len(layers)} Layers")
     exporter.export(layers, contours, masks, raw=True, overlayed=True)
-    stats.append(time.time() - start)
 
     print("Total time: ", time.time() - startTotal)
-    stats.append(time.time() - startTotal)
-    print(stats)
     exit(0)
 
 if __name__ == "__main__":