.gitignore

@@ -2,10 +2,3 @@
 venv/*
 output/*
 .envrc
-training_data/data*
-training_data/info*
-training_data/training_data_*/
-training_data/*.vec
-training_data/backgrounds.txt
-training_data/negatives
-training_data/opencv

Main.py

@@ -82,7 +82,12 @@ 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_10.xml") # CHECK THIS FIRST TROUBLE SHOOTING
+faceCascade = cv2.CascadeClassifier(r"./cascades/lbpcascade_frontalface.xml") # CHECK THIS FIRST TROUBLE SHOOTING
+faceCascade_default = cv2.CascadeClassifier(r"./cascades/haarcascade_frontalface_default.xml")
+faceCascade_alt = cv2.CascadeClassifier(r"./cascades/haarcascade_frontalface_alt.xml")
+faceCascade_alt2 = cv2.CascadeClassifier(r"./cascades/haarcascade_frontalface_alt2.xml")
+faceCascade_alttree = cv2.CascadeClassifier(r"./cascades/haarcascade_frontalface_alt_tree.xml")
+profileFaceCascade = cv2.CascadeClassifier(r"./cascades/haarcascade_profileface.xml")
 
 datestamp = "{:%Y_%m_%d %H_%M_%S}".format(datetime.datetime.now())
 output_dir = r"./output/" + datestamp + r"/"
@@ -107,12 +112,51 @@ while(True):
     # Detect faces in the image
     faces = faceCascade.detectMultiScale(
     gray,
-    scaleFactor=1.2,
-    minNeighbors=2,
-    # minSize=(70, 90)
-    minSize=(200, 200)
+    scaleFactor=1.1,
+    minNeighbors=5,
+    minSize=(30, 30)
     )
 
+    if len(faces) == 0:
+        faces = faceCascade_default.detectMultiScale(
+            gray,
+            scaleFactor=1.1,
+            minNeighbors=5,
+            minSize=(30,30)
+        )
+
+    if len(faces) == 0:
+        faces = profileFaceCascade.detectMultiScale(
+            gray,
+            scaleFactor=1.1,
+            minNeighbors=5,
+            minSize=(30,30)
+        )
+
+    if len(faces) == 0:
+        faces = faceCascade_alt.detectMultiScale(
+            gray,
+            scaleFactor=1.1,
+            minNeighbors=5,
+            minSize=(30,30)
+        )
+
+    if len(faces) == 0:
+        faces = faceCascade_alt2.detectMultiScale(
+            gray,
+            scaleFactor=1.1,
+            minNeighbors=5,
+            minSize=(30,30)
+        )
+
+    if len(faces) == 0:
+        faces = faceCascade_alttree.detectMultiScale(
+            gray,
+            scaleFactor=1.1,
+            minNeighbors=5,
+            minSize=(30,30)
+        )
+
     # Draw a rectangle around the faces
     for (x, y, w, h) in faces:
         if args.training_data: 

generate_datasets.ps1

@@ -1,45 +0,0 @@
-if (-Not (Test-Path -Path ".\training_data\negatives")) {
-    Write-Host "The negatives folder is not where it was expected"
-    return
-}
-
-if (-Not (Test-Path -Path ".\training_data\positives")) {
-    Write-Host "The positives folder is not where it was expected"
-    return
-}
-
-if (-Not (Test-Path -Path ".\training_data\1_positive_info")) {
-    New-Item -ItemType Directory -Force -Path ".\training_data\1_positive_info"
-}
-
-if (-Not (Test-Path -Path ".\training_data\2_positive_info")) {
-    New-Item -ItemType Directory -Force -Path ".\training_data\2_positive_info"
-}
-
-if (-Not (Test-Path -Path ".\training_data\5_positive_info")) {
-    New-Item -ItemType Directory -Force -Path ".\training_data\5_positive_info"
-}
-
-Set-Location .\training_data
-
-python .\dat_file_setup.py
-
-.\opencv\build\x64\vc15\bin\opencv_createsamples.exe -img .\positives\face_1.png -bg .\backgrounds.txt -info 1_positive_info/info.lst -pngoutput 1_positive_info -maxxangle 0.5 -maxyangle 0.5 -maxzangle 0.5 -num 1950
-
-.\opencv\build\x64\vc15\bin\opencv_createsamples.exe -img .\positives\face_1.png -bg .\backgrounds.txt -info 2_positive_info/info.lst -pngoutput 2_positive_info -maxxangle 0.5 -maxyangle 0.5 -maxzangle 0.5 -num 1950
-.\opencv\build\x64\vc15\bin\opencv_createsamples.exe -img .\positives\face_2.png -bg .\backgrounds.txt -info 2_positive_info/info.lst -pngoutput 2_positive_info -maxxangle 0.5 -maxyangle 0.5 -maxzangle 0.5 -num 1950
-
-& .\opencv\build\x64\vc15\bin\opencv_createsamples.exe -img .\positives\face_1.png -bg .\backgrounds.txt -info 5_positive_info/info.lst -pngoutput 5_positive_info -maxxangle 0.5 -maxyangle 0.5 -maxzangle 0.5 -num 1950
-& .\opencv\build\x64\vc15\bin\opencv_createsamples.exe -img .\positives\face_2.png -bg .\backgrounds.txt -info 5_positive_info/info.lst -pngoutput 5_positive_info -maxxangle 0.5 -maxyangle 0.5 -maxzangle 0.5 -num 1950
-& .\opencv\build\x64\vc15\bin\opencv_createsamples.exe -img .\positives\face_3.png -bg .\backgrounds.txt -info 5_positive_info/info.lst -pngoutput 5_positive_info -maxxangle 0.5 -maxyangle 0.5 -maxzangle 0.5 -num 1950
-& .\opencv\build\x64\vc15\bin\opencv_createsamples.exe -img .\positives\face_4.png -bg .\backgrounds.txt -info 5_positive_info/info.lst -pngoutput 5_positive_info -maxxangle 0.5 -maxyangle 0.5 -maxzangle 0.5 -num 1950
-& .\opencv\build\x64\vc15\bin\opencv_createsamples.exe -img .\positives\face_5.png -bg .\backgrounds.txt -info 5_positive_info/info.lst -pngoutput 5_positive_info -maxxangle 0.5 -maxyangle 0.5 -maxzangle 0.5 -num 1950
-
-& .\opencv\build\x64\vc15\bin\opencv_createsamples.exe -info .\1_positive_info\info.lst -num 1950 -w 20 -h 20 -vec positives_1.vec
-& .\opencv\build\x64\vc15\bin\opencv_createsamples.exe -info .\2_positive_info\info.lst -num 3900 -w 20 -h 20 -vec positives_2.vec
-& .\opencv\build\x64\vc15\bin\opencv_createsamples.exe -info .\5_positive_info\info.lst -num 9750 -w 20 -h 20 -vec positives_5.vec
-
-Write-Host "Ready for"
-Write-Host ".\opencv\build\x64\vc15\bin\opencv_traincascade.exe -data data -vec .\positives-1.vec -bg .\backgrounds.txt -numPos 1500 -numNeg 900 -numStages 15 -w 20 -h 20"
-Write-Host ".\opencv\build\x64\vc15\bin\opencv_traincascade.exe -data data -vec .\positives-2.vec -bg .\backgrounds.txt -numPos 3000 -numNeg 1500 -numStages 15 -w 20 -h 20"
-Write-Host ".\opencv\build\x64\vc15\bin\opencv_traincascade.exe -data data -vec .\positives-5.vec -bg .\backgrounds.txt -numPos 9000 -numNeg 1950 -numStages 15 -w 20 -h 20"

quickstart.txt

@@ -18,8 +18,3 @@ Now you can run the program. It is recommended to run the program with -d and -o
 
 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

training_data/create_ground_truth.py

@@ -1,93 +0,0 @@
-import cv2
-import sys
- 
-(major_ver, minor_ver, subminor_ver) = (cv2.__version__).split('.')
- 
-if __name__ == '__main__' :
- 
-    # Set up tracker.
-    # Instead of MIL, you can also use
- 
-    tracker_types = ['BOOSTING', 'MIL','KCF', 'TLD', 'MEDIANFLOW', 'GOTURN', 'MOSSE', 'CSRT']
-    tracker_type = tracker_types[1]
- 
-    if int(minor_ver) < 3:
-        tracker = cv2.Tracker_create(tracker_type)
-    else:
-        if tracker_type == 'BOOSTING':
-            tracker = cv2.TrackerBoosting_create()
-        if tracker_type == 'MIL':
-            tracker = cv2.TrackerMIL_create()
-        if tracker_type == 'KCF':
-            tracker = cv2.TrackerKCF_create()
-        if tracker_type == 'TLD':
-            tracker = cv2.TrackerTLD_create()
-        if tracker_type == 'MEDIANFLOW':
-            tracker = cv2.TrackerMedianFlow_create()
-        if tracker_type == 'GOTURN':
-            tracker = cv2.TrackerGOTURN_create()
-        if tracker_type == 'MOSSE':
-            tracker = cv2.TrackerMOSSE_create()
-        if tracker_type == "CSRT":
-            tracker = cv2.TrackerCSRT_create()
- 
-    # Read video
-    video = cv2.VideoCapture("./TestVideo.mp4")
- 
-    # Exit if video not opened.
-    if not video.isOpened():
-        print("Could not open video")
-        sys.exit()
- 
-    # Read first frame.
-    ok, frame = video.read()
-    if not ok:
-        print('Cannot read video file')
-        sys.exit()
-     
-    # Define an initial bounding box
-    bbox = (287, 23, 86, 320)
- 
-    # Uncomment the line below to select a different bounding box
-    bbox = cv2.selectROI(frame, False)
- 
-    # Initialize tracker with first frame and bounding box
-    ok = tracker.init(frame, bbox)
- 
-    while True:
-        # Read a new frame
-        ok, frame = video.read()
-        if not ok:
-            break
-         
-        # Start timer
-        timer = cv2.getTickCount()
- 
-        # Update tracker
-        ok, bbox = tracker.update(frame)
- 
-        # Calculate Frames per second (FPS)
-        fps = cv2.getTickFrequency() / (cv2.getTickCount() - timer);
- 
-        # Draw bounding box
-        if ok:
-            # Tracking success
-            p1 = (int(bbox[0]), int(bbox[1]))
-            p2 = (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3]))
-            cv2.rectangle(frame, p1, p2, (255,0,0), 2, 1)
-        else :
-            # Tracking failure
-            cv2.putText(frame, "Tracking failure detected", (100,80), cv2.FONT_HERSHEY_SIMPLEX, 0.75,(0,0,255),2)
- 
-        # Display tracker type on frame
-        cv2.putText(frame, tracker_type + " Tracker", (100,20), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50,170,50),2);
-     
-        # Display FPS on frame
-        cv2.putText(frame, "FPS : " + str(int(fps)), (100,50), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50,170,50), 2);
- 
-        # Display result
-        cv2.imshow("Tracking", frame)
- 
-        # Exit if ESC pressed
-        k = cv2.waitKey(1) & 0xff
-        if k == 27 : break

training_data/training_data_setup.py

@@ -1,120 +0,0 @@
-from PIL import Image
-import os
-import subprocess
-import shutil
-
-backgrounds_file_path = "backgrounds.txt"
-info_base_path = r"./info"
-negatives_path = r"./negatives"
-positives_path = r"./positives"
-training_data_base = r"./training_data_"
-
-opencv_path = r".\opencv\build\x64\vc15\bin\opencv_createsamples.exe"
-
-set_sizes = [1, 2, 5, 10]
-
-max_xangle = 0.5
-max_yangle = 0.5
-max_zangle = 0.5
-
-w, h = 25, 18
-
-class InfoEntry:
-    info_lst_line: str 
-    image_path: str
-
-    def __init__(self, info_line, file_path):
-        self.info_lst_line = info_line 
-        self.image_path = file_path
-
-    def __str__(self):
-        return f"Image Entry: {self.info_lst_line}, {self.image_path}"
-
-
-max_x = 750
-max_y = 800
-
-# remove too small images
-for image in os.listdir("./negatives"):
-    im = Image.open(f"./negatives/{image}")
-    width, height = im.size
-    del im
-    if width <= max_x:
-        os.remove(f"./negatives/{image}")
-    elif height <= max_y:
-        os.remove(f"./negatives/{image}")
-
-# remove any existing file and assume old data
-if os.path.exists(backgrounds_file_path):
-    os.remove(backgrounds_file_path)
-
-# regenerate the available negatives list
-count_negatives = len(os.listdir(negatives_path))
-for img in os.listdir(negatives_path):
-    line = f"{negatives_path}/" + img + "\n"
-    with open(backgrounds_file_path, 'a') as f:
-        f.write(line)
-
-info_dirs = []
-
-if len(os.listdir(positives_path)) > max(set_sizes):
-    print("Your set sizes were larger than the available positive images!")
-    quit(2)
-
-for img in os.listdir(positives_path):
-    i = len(info_dirs)
-    info_dir = f"{info_base_path}{i}"
-
-    com = f"{opencv_path} -img positives/" + str(i) + ".png -bg backgrounds.txt -info " + info_dir + "/info.lst" + \
-    " -pngoutput " + info_dir + " -maxxangle " + str(max_xangle) + " -maxyangle " + str(max_yangle) + " -maxzangle " + str(max_zangle) + \
-    " -num " + str(count_negatives)
-
-    if not os.path.exists(info_dir):
-        subprocess.call(com, shell=True)
-
-    info_dirs.append(info_dir)
-
-for i in set_sizes:
-    if not os.path.exists(training_data_base + str(i)):
-        os.makedirs(training_data_base + str(i))
-
-def join_info_folders(info_dirs: list, output_dir: str):
-    info_dir: str
-    cur_entry_name = 0
-    for info_dir in info_dirs:
-        info_lines = []
-        with open(info_dir + "/info.lst", 'r') as info_file:
-            for line in info_file.readlines():
-                image_path = f"{info_dir}/{line.split(' ')[0]}"
-                info_lines.append(InfoEntry(line.strip(), image_path))
-
-        item: InfoEntry
-        for item in info_lines:
-            shutil.copy(item.image_path, f"{output_dir}/{str(cur_entry_name)}.jpg")
-            with open(f"{output_dir}/info.lst", 'a') as info_file:
-                to_write = []
-                to_write.append(str(cur_entry_name) + ".jpg")
-                to_write = to_write + item.info_lst_line.split(" ")[1:]
-                to_write.append("\n")
-                info_file.write(" ".join(to_write))
-            cur_entry_name += 1
-
-for i in set_sizes:
-    join_info_folders(info_dirs[:i], training_data_base + str(i)) 
-
-commands = []
-
-for i in set_sizes:
-    num_positives = len(os.listdir(training_data_base + str(i)))
-    if os.path.exists(training_data_base + str(i) + ".vec"):
-        os.remove(training_data_base + str(i) + ".vec")
-    com = f"{opencv_path} -info {training_data_base + str(i)}\info.lst -num {num_positives} -w {w} -h {h} -vec {training_data_base + str(i)}.vec"
-    subprocess.call(com, shell=True)
-    commands.append(f".\opencv\\build\\x64\\vc15\\bin\opencv_traincascade.exe -data data_{str(i)} -vec .\\{training_data_base + str(i)}.vec -bg .\\{backgrounds_file_path} -numPos {num_positives} -numNeg {num_positives / 2} -numStages 15 -w {w} -h {h}")
-
-    if not os.path.exists(".\data_" + str(i)):
-        os.makedirs(".\data_" + str(i))
-
-for i in commands:
-    print(f"You are ready to train the models with: \n {i}")
-