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Move objdetect HaarCascadeClassifier and HOGDescriptor to contrib xobjdetect (#25198)
* Move objdetect parts to contrib * Move objdetect parts to contrib * Minor fixes.
This commit is contained in:
WU Jia
GitHub
@@ -1,79 +0,0 @@
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#!/usr/bin/env python
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'''
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face detection using haar cascades
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USAGE:
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facedetect.py [--cascade <cascade_fn>] [--nested-cascade <cascade_fn>] [<video_source>]
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'''
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# Python 2/3 compatibility
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from __future__ import print_function
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import numpy as np
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import cv2 as cv
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# local modules
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from video import create_capture
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from common import clock, draw_str
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def detect(img, cascade):
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rects = cascade.detectMultiScale(img, scaleFactor=1.3, minNeighbors=4, minSize=(30, 30),
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flags=cv.CASCADE_SCALE_IMAGE)
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if len(rects) == 0:
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return []
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rects[:,2:] += rects[:,:2]
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return rects
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def draw_rects(img, rects, color):
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for x1, y1, x2, y2 in rects:
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cv.rectangle(img, (x1, y1), (x2, y2), color, 2)
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def main():
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import sys, getopt
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args, video_src = getopt.getopt(sys.argv[1:], '', ['cascade=', 'nested-cascade='])
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try:
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video_src = video_src[0]
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except:
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video_src = 0
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args = dict(args)
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cascade_fn = args.get('--cascade', "haarcascades/haarcascade_frontalface_alt.xml")
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nested_fn = args.get('--nested-cascade', "haarcascades/haarcascade_eye.xml")
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cascade = cv.CascadeClassifier(cv.samples.findFile(cascade_fn))
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nested = cv.CascadeClassifier(cv.samples.findFile(nested_fn))
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cam = create_capture(video_src, fallback='synth:bg={}:noise=0.05'.format(cv.samples.findFile('lena.jpg')))
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while True:
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_ret, img = cam.read()
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gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
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gray = cv.equalizeHist(gray)
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t = clock()
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rects = detect(gray, cascade)
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vis = img.copy()
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draw_rects(vis, rects, (0, 255, 0))
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if not nested.empty():
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for x1, y1, x2, y2 in rects:
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roi = gray[y1:y2, x1:x2]
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vis_roi = vis[y1:y2, x1:x2]
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subrects = detect(roi.copy(), nested)
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draw_rects(vis_roi, subrects, (255, 0, 0))
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dt = clock() - t
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draw_str(vis, (20, 20), 'time: %.1f ms' % (dt*1000))
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cv.imshow('facedetect', vis)
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if cv.waitKey(5) == 27:
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break
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print('Done')
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if __name__ == '__main__':
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print(__doc__)
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main()
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cv.destroyAllWindows()
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@@ -1,76 +0,0 @@
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#!/usr/bin/env python
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'''
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example to detect upright people in images using HOG features
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Usage:
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peopledetect.py <image_names>
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Press any key to continue, ESC to stop.
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'''
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# Python 2/3 compatibility
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from __future__ import print_function
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import numpy as np
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import cv2 as cv
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def inside(r, q):
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rx, ry, rw, rh = r
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qx, qy, qw, qh = q
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return rx > qx and ry > qy and rx + rw < qx + qw and ry + rh < qy + qh
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def draw_detections(img, rects, thickness = 1):
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for x, y, w, h in rects:
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# the HOG detector returns slightly larger rectangles than the real objects.
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# so we slightly shrink the rectangles to get a nicer output.
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pad_w, pad_h = int(0.15*w), int(0.05*h)
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cv.rectangle(img, (x+pad_w, y+pad_h), (x+w-pad_w, y+h-pad_h), (0, 255, 0), thickness)
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def main():
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import sys
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from glob import glob
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import itertools as it
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hog = cv.HOGDescriptor()
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hog.setSVMDetector( cv.HOGDescriptor_getDefaultPeopleDetector() )
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default = [cv.samples.findFile('basketball2.png')] if len(sys.argv[1:]) == 0 else []
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for fn in it.chain(*map(glob, default + sys.argv[1:])):
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print(fn, ' - ',)
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try:
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img = cv.imread(fn)
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if img is None:
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print('Failed to load image file:', fn)
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continue
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except:
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print('loading error')
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continue
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found, _w = hog.detectMultiScale(img, winStride=(8,8), padding=(32,32), scale=1.05)
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found_filtered = []
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for ri, r in enumerate(found):
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for qi, q in enumerate(found):
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if ri != qi and inside(r, q):
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break
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else:
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found_filtered.append(r)
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draw_detections(img, found)
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draw_detections(img, found_filtered, 3)
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print('%d (%d) found' % (len(found_filtered), len(found)))
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cv.imshow('img', img)
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ch = cv.waitKey()
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if ch == 27:
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break
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print('Done')
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if __name__ == '__main__':
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print(__doc__)
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main()
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cv.destroyAllWindows()
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@@ -1,61 +0,0 @@
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from __future__ import print_function
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import cv2 as cv
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import argparse
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def detectAndDisplay(frame):
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frame_gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
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frame_gray = cv.equalizeHist(frame_gray)
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#-- Detect faces
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faces = face_cascade.detectMultiScale(frame_gray)
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for (x,y,w,h) in faces:
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center = (x + w//2, y + h//2)
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frame = cv.ellipse(frame, center, (w//2, h//2), 0, 0, 360, (255, 0, 255), 4)
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faceROI = frame_gray[y:y+h,x:x+w]
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#-- In each face, detect eyes
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eyes = eyes_cascade.detectMultiScale(faceROI)
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for (x2,y2,w2,h2) in eyes:
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eye_center = (x + x2 + w2//2, y + y2 + h2//2)
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radius = int(round((w2 + h2)*0.25))
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frame = cv.circle(frame, eye_center, radius, (255, 0, 0 ), 4)
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cv.imshow('Capture - Face detection', frame)
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parser = argparse.ArgumentParser(description='Code for Cascade Classifier tutorial.')
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parser.add_argument('--face_cascade', help='Path to face cascade.', default='data/haarcascades/haarcascade_frontalface_alt.xml')
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parser.add_argument('--eyes_cascade', help='Path to eyes cascade.', default='data/haarcascades/haarcascade_eye_tree_eyeglasses.xml')
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parser.add_argument('--camera', help='Camera divide number.', type=int, default=0)
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args = parser.parse_args()
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face_cascade_name = args.face_cascade
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eyes_cascade_name = args.eyes_cascade
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face_cascade = cv.CascadeClassifier()
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eyes_cascade = cv.CascadeClassifier()
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#-- 1. Load the cascades
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if not face_cascade.load(cv.samples.findFile(face_cascade_name)):
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print('--(!)Error loading face cascade')
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exit(0)
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if not eyes_cascade.load(cv.samples.findFile(eyes_cascade_name)):
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print('--(!)Error loading eyes cascade')
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exit(0)
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camera_device = args.camera
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#-- 2. Read the video stream
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cap = cv.VideoCapture(camera_device)
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if not cap.isOpened:
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print('--(!)Error opening video capture')
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exit(0)
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while True:
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ret, frame = cap.read()
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if frame is None:
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print('--(!) No captured frame -- Break!')
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break
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detectAndDisplay(frame)
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if cv.waitKey(10) == 27:
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break
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