fixed merge conflict

Main.py

@@ -83,7 +83,7 @@ if args.file:
     cap = cv2.VideoCapture(args.file)
 else:
     cap = cv2.VideoCapture(0, cv2.IMREAD_GRAYSCALE) # instead of grayscale you can also use -1, 0, or 1.
-faceCascade = cv2.CascadeClassifier(r"./cascades/cascade_5.xml") # CHECK THIS FIRST TROUBLE SHOOTING
+faceCascade = cv2.CascadeClassifier(r"./cascades/cascade_10.xml") # CHECK THIS FIRST TROUBLE SHOOTING
 
 datestamp = "{:%Y_%m_%d %H_%M_%S}".format(datetime.datetime.now())
 output_dir = r"./output/" + datestamp + r"/"

quickstart.txt

@@ -1,25 +0,0 @@
-NixOS/Nix: 
-If you run on a Nix or NixOS environment, you can use the included shell.nix file to create a nix shell to run this in.
-
-Windows/Other Linux:
-This program was developed with python 3.11, so please use that version of python to create the virtual environment. After making sure you are using the correct python version, run the following commands:
-
-python -m venv venv
-
-to create a new virtual environment ".\venv" 
-
-now enter the the virtual environment by running either .\venv\Scripts\Activate.ps1 or ./venv/Scripts/activate depending on if you use windows and install the following packages (found in requirements.txt)
-
-pip install numpy
-pip install opencv-python
-
-Now you can run the program. It is recommended to run the program with -d and -o set while testing. This enables the dashboard which shows live statistics, and output, which shows the calculated adjustments required to center the face in the frame.
-
-
-Training Data:
-https://www.kaggle.com/datasets/utkarshsaxenadn/landscape-recognition-image-dataset-12k-images
-
-
-create positives from the negatives: \opencv\build\x64\vc15\bin\opencv_createsamples.exe -img .\positives\face_1.png -bg .\bg.txt -info info/info.lst -pngoutput info -maxxangle 0.8 -maxyangle 0.8 -maxzangle 0.8 -num 1950
-Create vec files from positives: .\opencv\build\x64\vc15\bin\opencv_createsamples.exe -info .\info\info.lst -num 1950 -w 80 -h 80 -vec positives-80.vec
-(I created a 20, 40, and 80) we have 1650 positives

validation/compare_to_gt.py

@@ -22,6 +22,9 @@ def init_argparse() -> argparse.ArgumentParser:
     parser.add_argument(
         "out_file", help="the file to write the output to"
     )
+    parser.add_argument(
+        "-f", "--faces_count_file", help="the file output the number of faces found in each frame"
+    )
     return parser
 
 multiplication_factor = 0.05
@@ -71,6 +74,13 @@ for test_fb in test_results:
     def bring_up_gt():
         if test_fb.frame_number > ground_truth[0].frame_number:
             ground_truth.pop(0)
+
+            if test_fb.frame_number > ground_truth[0].frame_number:
+                # we need to include the empty frames too
+                if args.faces_count_file:
+                    with open(args.faces_count_file, 'a') as out_file:
+                        out_file.write(f"{ground_truth[0].frame_number}, 0,\n")
+
             bring_up_gt()
 
     bring_up_gt()
@@ -81,9 +91,13 @@ for test_fb in test_results:
     gt_adjustment = get_adjustment_amount((1920, 1000), gt_frame.top_left[0], gt_frame.top_left[1], gt_frame.bottom_right[0], gt_frame.bottom_right[1])
     test_adjustment = get_adjustment_amount((1920, 1000), test_fb.top_left[0], test_fb.top_left[1], test_fb.bottom_right[0], test_fb.bottom_right[1])
 
-    if last_frame_num != test_fb.frame_number and average_count != 0:
+    if last_frame_num != test_fb.frame_number :
-        with open(args.out_file, 'a') as out_file:
+        if average_count > 0:
-            out_file.write(f"{average_sum},\n")
+            with open(args.out_file, 'a') as out_file:
+                out_file.write(f"{average_sum},\n")
+        if args.faces_count_file:
+            with open(args.faces_count_file, 'a') as out_file:
+                out_file.write(f"{test_fb.frame_number}, {average_count},\n")
         average_sum = 0
         average_count = 0