Added a barebones GUI file selector

opticalFlow/farnebackOpticalFlow.py

@@ -5,17 +5,43 @@
 Right now, in the result visualization, the intensity of a pixel's motion will change both it's color and magnitude.
 Brighter pixels have more motion.
 The output visualization is stored in the same location as the input video with the name <input_vid_filename>_FB_FLOW.mp4
-
 The idea is that perhaps the data about how certain pixels/features are moving across the screen could be used to figure out how the player camera / aim was changing.
 """
 
+from tkinter import *
+from tkinter import filedialog
+from tkinter.messagebox import showinfo
 import numpy as np
 import cv2 as cv
 
+# GUI FILE BROWSER------------------------------------------------------------
+
+window = Tk()
+window.geometry('300x150') # sets the size of the GUI window
+window.title('Select a Video File') # creates a title for the window
+
+# function allowing you to find/select video in GUI
+def get_file_path():
+    global file_path
+    # Open and return file path
+    file_path = filedialog.askopenfilename(title = "Select a Video File", filetypes = (("mp4", "*.mp4"), ("mov files", "*.mov") ,("wmv", "*.wmv"), ("avi", "*.avi")))
+    showinfo(title='Selected File', message=file_path)
+
+# function allowing you to select the output path in the GUI
+def output():
+    global outpath
+    outpath = filedialog.asksaveasfilename(filetypes=[("mp4", '*.mp4')])
+    window.destroy()
+
+# Creating a button to search for the input file and to select the output destinatio and file name
+b1 = Button(window, text = 'Open a File', command = get_file_path).pack()
+b2 = Button(window, text = 'Save File Name', command = output).pack()
+window.mainloop()
+
 # PARAMETERS--------------------------------
 
 # path to input video file
-vidpath = r""
+vidpath = file_path
 
 # do you want to save the output video?
 savevid = True
@@ -44,11 +70,15 @@
 # create black result image
 hsv_img = np.zeros_like(old_frame)
 hsv_img[...,1] = 255
+# get features from first frame
+print(f"\nRunning farneback Optical Flow on: {vidpath}")
 
 # if saving video
 if savevid:
     # path to save output video
-    savepath = vidpath.split('.')[0] + '_FB_FLOW' + '.mp4'
+    filename = outpath
+    savepath = filename + '_FB_FLOW' + '.mp4'
+    print(f"Saving Output video to: {savepath}")
 
     # get shape of video frames
     height, width, channels = old_frame.shape
@@ -57,33 +87,36 @@
     fourcc = cv.VideoWriter_fourcc(*'mp4v')
     videoOut = cv.VideoWriter(savepath, fourcc, fps, (width, height))
 
+
 # PROCESS VIDEO ---------------------------
 while(True):
     # get frame and convert to grayscale
     _, new_frame = cap.read()
-    new_frame_gray = cv.cvtColor(new_frame, cv.COLOR_BGR2GRAY)
+    if _:
+      new_frame_gray = cv.cvtColor(new_frame, cv.COLOR_BGR2GRAY)
 
     # do Farneback optical flow
-    flow = cv.calcOpticalFlowFarneback(old_frame_gray, new_frame_gray, None, pyr_scale, levels, winsize, iterations, poly_n, poly_sigma, flags)
+      flow = cv.calcOpticalFlowFarneback(old_frame_gray, new_frame_gray, None, pyr_scale, levels, winsize, iterations, poly_n, poly_sigma, flags)
 
     # conversion
-    mag, ang = cv.cartToPolar(flow[...,0], flow[...,1])
+      mag, ang = cv.cartToPolar(flow[...,0], flow[...,1])
 
     # draw onto the result image - color is determined by direction, brightness is by magnitude of motion
     #hsv_img[...,0] = ang*180/np.pi/2
     #hsv_img[...,2] = cv.normalize(mag, None, 0, 255, cv.NORM_MINMAX)
 
     # color and brightness by magnitude
-    hsv_img[...,0] = cv.normalize(mag, None, 0, 255, cv.NORM_MINMAX)
-    hsv_img[...,1] = cv.normalize(mag, None, 0, 255, cv.NORM_MINMAX)
-    hsv_img[...,2] = cv.normalize(mag, None, 0, 255, cv.NORM_MINMAX)
+      hsv_img[...,0] = cv.normalize(mag, None, 0, 255, cv.NORM_MINMAX)
+      hsv_img[...,1] = cv.normalize(mag, None, 0, 255, cv.NORM_MINMAX)
+      hsv_img[...,2] = cv.normalize(mag, None, 0, 255, cv.NORM_MINMAX)
 
-    bgr_img = cv.cvtColor(hsv_img, cv.COLOR_HSV2BGR)
+      bgr_img = cv.cvtColor(hsv_img, cv.COLOR_HSV2BGR)
 
     #  show the image and break out if ESC pressed
-    cv.imshow('Farneback Optical Flow', bgr_img)
-    k = cv.waitKey(30) & 0xff
-    if k == 27:
+      cv.imshow('Farneback Optical Flow', bgr_img)
+      k = cv.waitKey(30) & 0xff
+    else:
+        k == 27
         break
 
     # write frames to new output video
@@ -95,4 +128,7 @@
 
 # cleanup
 videoOut.release()
-cv.destroyAllWindows()
+cv.destroyAllWindows()
+
+# after video is finished
+print('\nComplete!\n')

opticalFlow/lucasKanadeOpticalFlow.py

@@ -10,13 +10,41 @@
 The idea is that perhaps the data about how certain pixels/features are moving across the screen could be used to figure out how the player camera / aim was changing.
 """
 
+from tkinter import *
+from tkinter import filedialog
 import cv2 as cv
 import numpy as np
+from tkinter.messagebox import showinfo
+
+# GUI FILE BROWSER------------------------------------------------------------
+
+window = Tk()
+window.geometry('300x150') # sets the size of the GUI window
+window.title('Select a Video File') # creates a title for the window
+
+# function allowing you to find/select video in GUI
+def get_file_path():
+    global file_path
+    # Open and return file path
+    file_path = filedialog.askopenfilename(title = "Select a Video File", filetypes = (("mp4", "*.mp4"), ("mov files", "*.mov") ,("wmv", "*.wmv"), ("avi", "*.avi")))
+    showinfo(title='Selected File', message=file_path)
+
+# function allowing you to select the output path in the GUI
+def output():
+    global outpath
+    outpath = filedialog.asksaveasfilename(filetypes=[("mp4", '*.mp4')])
+    window.destroy()
+
+# Creating a button to search for the input file and to select the output destinatio and file name
+b1 = Button(window, text = 'Open a File', command = get_file_path).pack()
+b2 = Button(window, text = 'Save File Name', command = output).pack()
+window.mainloop()
+
 
 # PARAMETERS------------------------------------------------------------------
 
 # path to input videofile
-vidpath = r""
+vidpath = file_path
 
 # do you want to save the video?
 savevid = True
@@ -83,8 +111,8 @@
 # if saving video
 if savevid:
     # path to save output video
-    pathparts = vidpath.split('.')
-    savepath = '.'+ vidpath.split('.')[-2] + '_LK_FLOW' + '.mp4'
+    filename = outpath
+    savepath = filename + '_LK_FLOW' + '.mp4'
     print(f"Saving Output video to: {savepath}")
 
     # get shape of video frames

yolov3_testing/coco.names

@@ -0,0 +1,80 @@
+person
+bicycle
+car
+motorbike
+aeroplane
+bus
+train
+truck
+boat
+traffic light
+fire hydrant
+stop sign
+parking meter
+bench
+bird
+cat
+dog
+horse
+sheep
+cow
+elephant
+bear
+zebra
+giraffe
+backpack
+umbrella
+handbag
+tie
+suitcase
+frisbee
+skis
+snowboard
+sports ball
+kite
+baseball bat
+baseball glove
+skateboard
+surfboard
+tennis racket
+bottle
+wine glass
+cup
+fork
+knife
+spoon
+bowl
+banana
+apple
+sandwich
+orange
+broccoli
+carrot
+hot dog
+pizza
+donut
+cake
+chair
+sofa
+pottedplant
+bed
+diningtable
+toilet
+tvmonitor
+laptop
+mouse
+remote
+keyboard
+cell phone
+microwave
+oven
+toaster
+sink
+refrigerator
+book
+clock
+vase
+scissors
+teddy bear
+hair drier
+toothbrush

yolov3_testing/readme.md

@@ -0,0 +1,8 @@
+Testing the premade CNN model using "yolov3" on CSGO footage.
+
+It does pretty good job at tracking human objects in the environment, but it also picks and ID's some of the background as well.
+
+Thanks to @CaptnBaguette for bringing it up in the data-classification channel in the Discord.
+
+I did not see any code posted for it, so I went ahead an made a very basic example.
+Everything you need to run the script is included EXCEPT for the model weights. Those can be retrieved from: https://pjreddie.com/darknet/yolo/ (it's the 76MB one).