removed old file, renamed main file
This commit is contained in:
Nickiel12
parent
fac9e73506
commit
542abea6c7
2 changed files with 186 additions and 285 deletions
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@ -1,186 +1,186 @@
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import cv2
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import numpy as np
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import argparse
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import sys
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import time
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import os
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import datetime
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def dir_path(string):
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if os.path.exists(string):
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return string
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else:
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raise NotADirectoryError(string)
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def init_argparse() -> argparse.ArgumentParser:
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parser = argparse.ArgumentParser(
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prog="FaceDetection",
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usage="%(prog)s [OPTION]",
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description="Run face localization"
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)
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parser.add_argument(
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"-v", "--version", action="version", version=f"{parser.prog} version 1.0.1"
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)
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parser.add_argument(
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"-d", "--dashboard", action='store_true', help="Flag to enable live dashboard with statistics - requires terminal width of 90 columns or greater"
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)
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parser.add_argument(
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"-o", "--output", action='store_true', help="show the resultant directions"
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)
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parser.add_argument(
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"-f", "--file", type=dir_path, nargs="?", help="File to scan instead of using the camera. Useful for generating training data"
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)
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parser.add_argument(
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"-s", "--no-screen", action='store_true', help="Do not show the successful frames"
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)
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parser.add_argument(
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"-t", "--training-data", action='store_true', help="When set, saves successful face-location images and coordinates to use for future training data"
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)
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return parser
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multiplication_factor = 0.05
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def get_adjustment_amount(imgSize, currentX, currentY, currentW, currentH):
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current_top_left = [currentX, currentY]
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current_bottom_right = [currentX + currentW, currentY + currentH]
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current_top_right = [currentX + currentW, currentY]
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# find the difference between the left gap and the right gap, divide it by two, and multiply it by the speed scale
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horizontal_adjustment = multiplication_factor * (currentX - (imgSize[0] - current_top_right[0])) / 2
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vertical_adjustment = multiplication_factor * (currentY - (imgSize[0] - current_bottom_right[1])) / 2
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return [horizontal_adjustment, vertical_adjustment]
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frames_searched = 1
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faces_found = 0
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start_time = datetime.datetime.now()
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def draw_dashboard(keep_stat_line = False):
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global frames_searched, faces_found, start_time
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elapsed_time = datetime.datetime.now() - start_time
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hours, remainder = divmod(elapsed_time.total_seconds(), 3600)
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minutes, seconds = divmod(remainder, 60)
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f_found = f"{faces_found} Faces found".ljust(16, ' ')
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f_searched = f"{frames_searched} Frames searched".ljust(21, ' ')
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success_rate = f"{round((faces_found / frames_searched) * 100, 1)}% Success rate".ljust(16, ' ')
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if keep_stat_line:
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print(f"{f_found} | {f_searched} | {success_rate} | {round(hours)}h {round(minutes)}m {round(seconds)}s elapsed", flush=True)
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else:
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print(f"{f_found} | {f_searched} | {success_rate} | {round(hours)}h {round(minutes)}m {round(seconds)}s elapsed", end="\r", flush=True)
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parser = init_argparse()
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args = parser.parse_args()
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if args.file:
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cap = cv2.VideoCapture(args.file)
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else:
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cap = cv2.VideoCapture(0, cv2.IMREAD_GRAYSCALE) # instead of grayscale you can also use -1, 0, or 1.
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faceCascade = cv2.CascadeClassifier(r"./cascades/lbpcascade_frontalface.xml") # CHECK THIS FIRST TROUBLE SHOOTING
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faceCascade_default = cv2.CascadeClassifier(r"./cascades/haarcascade_frontalface_default.xml")
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faceCascade_alt = cv2.CascadeClassifier(r"./cascades/haarcascade_frontalface_alt.xml")
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faceCascade_alt2 = cv2.CascadeClassifier(r"./cascades/haarcascade_frontalface_alt2.xml")
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faceCascade_alttree = cv2.CascadeClassifier(r"./cascades/haarcascade_frontalface_alt_tree.xml")
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profileFaceCascade = cv2.CascadeClassifier(r"./cascades/haarcascade_profileface.xml")
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datestamp = "{:%Y_%m_%d %H_%M_%S}".format(datetime.datetime.now())
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output_dir = r"./output/" + datestamp + r"/"
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if args.training_data:
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if not os.path.exists(output_dir):
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os.makedirs(output_dir)
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with open(output_dir + r"found_faces.csv", 'a') as fd:
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fd.write(f"frame_name, x, y, width, height\n")
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tmp, frm = cap.read()
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height, width, channels = frm.shape
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# print(f"{height*.25}, {width}")
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del tmp, frm
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#Color is 1, grayscale is 0, and the unchanged is -1
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while(True):
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ret, frame = cap.read()
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frames_searched += 1
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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# Detect faces in the image
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faces = faceCascade.detectMultiScale(
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gray,
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scaleFactor=1.1,
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minNeighbors=5,
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minSize=(30, 30)
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)
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if len(faces) == 0:
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faces = faceCascade_default.detectMultiScale(
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gray,
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scaleFactor=1.1,
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minNeighbors=5,
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minSize=(30,30)
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)
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if len(faces) == 0:
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faces = profileFaceCascade.detectMultiScale(
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gray,
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scaleFactor=1.1,
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minNeighbors=5,
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minSize=(30,30)
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)
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if len(faces) == 0:
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faces = faceCascade_alt.detectMultiScale(
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gray,
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scaleFactor=1.1,
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minNeighbors=5,
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minSize=(30,30)
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)
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if len(faces) == 0:
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faces = faceCascade_alt2.detectMultiScale(
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gray,
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scaleFactor=1.1,
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minNeighbors=5,
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minSize=(30,30)
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)
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if len(faces) == 0:
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faces = faceCascade_alttree.detectMultiScale(
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gray,
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scaleFactor=1.1,
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minNeighbors=5,
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minSize=(30,30)
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)
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# Draw a rectangle around the faces
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for (x, y, w, h) in faces:
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if args.training_data:
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frame_name = frames_searched
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with open(output_dir + r"found_faces.csv", 'a') as fd:
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fd.write(f"frame_{frame_name}.jpg, {x}, {y}, {w}, {h}\n")
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cv2.imwrite(output_dir + f"frame_{frame_name}.jpg", frame)
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faces_found += 1
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adjustment_required = get_adjustment_amount([width, height], x, y, w, h)
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cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 255, 255))
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if args.output:
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print(f"Adjust right: {adjustment_required[0]}".ljust(90, ' '), flush=True)
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print(f"Adjust up : {adjustment_required[1]}", flush=True)
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if not args.no_screen:
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cv2.imshow('frame', frame)
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if args.dashboard:
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draw_dashboard()
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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draw_dashboard(keep_stat_line=True)
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cap.release()
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import cv2
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import numpy as np
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import argparse
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import sys
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import time
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import os
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import datetime
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def dir_path(string):
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if os.path.exists(string):
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return string
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else:
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raise NotADirectoryError(string)
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def init_argparse() -> argparse.ArgumentParser:
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parser = argparse.ArgumentParser(
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prog="FaceDetection",
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usage="%(prog)s [OPTION]",
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description="Run face localization"
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)
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parser.add_argument(
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"-v", "--version", action="version", version=f"{parser.prog} version 1.0.1"
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)
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parser.add_argument(
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"-d", "--dashboard", action='store_true', help="Flag to enable live dashboard with statistics - requires terminal width of 90 columns or greater"
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)
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parser.add_argument(
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"-o", "--output", action='store_true', help="show the resultant directions"
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)
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parser.add_argument(
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"-f", "--file", type=dir_path, nargs="?", help="File to scan instead of using the camera. Useful for generating training data"
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)
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parser.add_argument(
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"-s", "--no-screen", action='store_true', help="Do not show the successful frames"
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)
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parser.add_argument(
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"-t", "--training-data", action='store_true', help="When set, saves successful face-location images and coordinates to use for future training data"
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)
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return parser
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multiplication_factor = 0.05
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def get_adjustment_amount(imgSize, currentX, currentY, currentW, currentH):
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current_top_left = [currentX, currentY]
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current_bottom_right = [currentX + currentW, currentY + currentH]
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current_top_right = [currentX + currentW, currentY]
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# find the difference between the left gap and the right gap, divide it by two, and multiply it by the speed scale
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horizontal_adjustment = multiplication_factor * (currentX - (imgSize[0] - current_top_right[0])) / 2
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vertical_adjustment = multiplication_factor * (currentY - (imgSize[0] - current_bottom_right[1])) / 2
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return [horizontal_adjustment, vertical_adjustment]
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frames_searched = 1
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faces_found = 0
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start_time = datetime.datetime.now()
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def draw_dashboard(keep_stat_line = False):
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global frames_searched, faces_found, start_time
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elapsed_time = datetime.datetime.now() - start_time
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hours, remainder = divmod(elapsed_time.total_seconds(), 3600)
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minutes, seconds = divmod(remainder, 60)
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f_found = f"{faces_found} Faces found".ljust(16, ' ')
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f_searched = f"{frames_searched} Frames searched".ljust(21, ' ')
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success_rate = f"{round((faces_found / frames_searched) * 100, 1)}% Success rate".ljust(16, ' ')
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if keep_stat_line:
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print(f"{f_found} | {f_searched} | {success_rate} | {round(hours)}h {round(minutes)}m {round(seconds)}s elapsed", flush=True)
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else:
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print(f"{f_found} | {f_searched} | {success_rate} | {round(hours)}h {round(minutes)}m {round(seconds)}s elapsed", end="\r", flush=True)
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parser = init_argparse()
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args = parser.parse_args()
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if args.file:
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cap = cv2.VideoCapture(args.file)
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else:
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cap = cv2.VideoCapture(0, cv2.IMREAD_GRAYSCALE) # instead of grayscale you can also use -1, 0, or 1.
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faceCascade = cv2.CascadeClassifier(r"./cascades/lbpcascade_frontalface.xml") # CHECK THIS FIRST TROUBLE SHOOTING
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faceCascade_default = cv2.CascadeClassifier(r"./cascades/haarcascade_frontalface_default.xml")
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faceCascade_alt = cv2.CascadeClassifier(r"./cascades/haarcascade_frontalface_alt.xml")
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faceCascade_alt2 = cv2.CascadeClassifier(r"./cascades/haarcascade_frontalface_alt2.xml")
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faceCascade_alttree = cv2.CascadeClassifier(r"./cascades/haarcascade_frontalface_alt_tree.xml")
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profileFaceCascade = cv2.CascadeClassifier(r"./cascades/haarcascade_profileface.xml")
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datestamp = "{:%Y_%m_%d %H_%M_%S}".format(datetime.datetime.now())
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output_dir = r"./output/" + datestamp + r"/"
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if args.training_data:
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if not os.path.exists(output_dir):
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os.makedirs(output_dir)
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with open(output_dir + r"found_faces.csv", 'a') as fd:
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fd.write(f"frame_name, x, y, width, height\n")
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tmp, frm = cap.read()
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height, width, channels = frm.shape
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# print(f"{height*.25}, {width}")
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del tmp, frm
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#Color is 1, grayscale is 0, and the unchanged is -1
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while(True):
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ret, frame = cap.read()
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frames_searched += 1
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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# Detect faces in the image
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faces = faceCascade.detectMultiScale(
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gray,
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scaleFactor=1.1,
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minNeighbors=5,
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minSize=(30, 30)
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)
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if len(faces) == 0:
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faces = faceCascade_default.detectMultiScale(
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gray,
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scaleFactor=1.1,
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minNeighbors=5,
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minSize=(30,30)
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)
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if len(faces) == 0:
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faces = profileFaceCascade.detectMultiScale(
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gray,
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scaleFactor=1.1,
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minNeighbors=5,
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minSize=(30,30)
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)
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if len(faces) == 0:
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faces = faceCascade_alt.detectMultiScale(
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gray,
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scaleFactor=1.1,
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minNeighbors=5,
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minSize=(30,30)
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)
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if len(faces) == 0:
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faces = faceCascade_alt2.detectMultiScale(
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gray,
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scaleFactor=1.1,
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minNeighbors=5,
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minSize=(30,30)
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)
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if len(faces) == 0:
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faces = faceCascade_alttree.detectMultiScale(
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gray,
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scaleFactor=1.1,
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minNeighbors=5,
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minSize=(30,30)
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)
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# Draw a rectangle around the faces
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for (x, y, w, h) in faces:
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if args.training_data:
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frame_name = frames_searched
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with open(output_dir + r"found_faces.csv", 'a') as fd:
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fd.write(f"frame_{frame_name}.jpg, {x}, {y}, {w}, {h}\n")
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cv2.imwrite(output_dir + f"frame_{frame_name}.jpg", frame)
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faces_found += 1
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adjustment_required = get_adjustment_amount([width, height], x, y, w, h)
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cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 255, 255))
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if args.output:
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print(f"Adjust right: {adjustment_required[0]}".ljust(90, ' '), flush=True)
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print(f"Adjust up : {adjustment_required[1]}", flush=True)
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if not args.no_screen:
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cv2.imshow('frame', frame)
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if args.dashboard:
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draw_dashboard()
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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draw_dashboard(keep_stat_line=True)
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cap.release()
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@ -1,99 +0,0 @@
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import cv2
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import numpy as np
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multiplication_factor = 0.05
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def get_adjustment_amount(imgSize, currentX, currentY, currentW, currentH):
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current_top_left = [currentX, currentY]
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current_bottom_right = [currentX + currentW, currentY + currentH]
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current_top_right = [currentX + currentW, currentY]
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# find the difference between the left gap and the right gap, divide it by two, and multiply it by the speed scale
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horizontal_adjustment = multiplication_factor * (currentX - (imgSize[0] - current_top_right[0])) / 2
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vertical_adjustment = multiplication_factor * (currentY - (imgSize[0] - current_bottom_right[1])) / 2
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return [horizontal_adjustment, vertical_adjustment]
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cap = cv2.VideoCapture(0, cv2.IMREAD_GRAYSCALE) # instead of grayscale you can also use -1, 0, or 1.
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faceCascade = cv2.CascadeClassifier(r"./lbpcascade_frontalface.xml") # CHECK THIS FIRST TROUBLE SHOOTING
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faceCascade_default = cv2.CascadeClassifier(r"./haarcascade_frontalface_default.xml")
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faceCascade_alt = cv2.CascadeClassifier(r"./haarcascade_frontalface_alt.xml")
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faceCascade_alt2 = cv2.CascadeClassifier(r"./haarcascade_frontalface_alt2.xml")
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faceCascade_alttree = cv2.CascadeClassifier(r"./haarcascade_frontalface_alt_tree.xml")
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profileFaceCascade = cv2.CascadeClassifier(r"./haarcascade_profileface.xml")
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tmp, frm = cap.read()
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height, width, channels = frm.shape
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print(f"{height*.25}, {width}")
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del tmp, frm
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#Color is 1, grayscale is 0, and the unchanged is -1
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while(True):
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ret, frame = cap.read()
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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# Detect faces in the image
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faces = faceCascade.detectMultiScale(
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gray,
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scaleFactor=1.1,
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minNeighbors=5,
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minSize=(30, 30)
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)
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if len(faces) == 0:
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faces = faceCascade_default.detectMultiScale(
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gray,
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scaleFactor=1.1,
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minNeighbors=5,
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minSize=(30,30)
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)
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if len(faces) == 0:
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faces = profileFaceCascade.detectMultiScale(
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gray,
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scaleFactor=1.1,
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minNeighbors=5,
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minSize=(30,30)
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)
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if len(faces) == 0:
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faces = faceCascade_alt.detectMultiScale(
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gray,
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scaleFactor=1.1,
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minNeighbors=5,
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minSize=(30,30)
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)
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if len(faces) == 0:
|
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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:
|
||||
adjustment_required = get_adjustment_amount([width, height], x, y, w, h)
|
||||
cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 255, 255))
|
||||
print(f"Adjust right: {adjustment_required[0]}")
|
||||
print(f"Adjust up : {adjustment_required[1]}")
|
||||
cv2.imshow('frame', frame)
|
||||
|
||||
if cv2.waitKey(1) & 0xFF == ord('q'):
|
||||
break
|
||||
|
||||
cap.release()
|
||||
Loading…
Reference in a new issue