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import cv2
import imutils
import numpy as np
from imutils import paths
from imutils.object_detection import non_max_suppression
def hog_clf(descriptor_type='default'):
if descriptor_type == 'daimler':
winSize = (48, 96)
blockSize = (16, 16)
blockStride = (8, 8)
cellSize = (8, 8)
nbins = 9
hog = cv2.HOGDescriptor(winSize, blockSize, blockStride, cellSize, nbins)
hog.setSVMDetector(cv2.HOGDescriptor_getDaimlerPeopleDetector())
return hog
else:
winSize = (64, 128)
blockSize = (16, 16)
blockStride = (8, 8)
cellSize = (8, 8)
nbins = 9
hog = cv2.HOGDescriptor(winSize, blockSize, blockStride, cellSize, nbins)
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
return hog
def detect_image(hog, image):
# image = cv2.imread(image_path)
image = imutils.resize(image, width=min(400, image.shape[1]))
orig = image.copy()
# detect people in the image
(rects, weights) = hog.detectMultiScale(image, winStride=(4, 4),
padding=(8, 8), scale=1.1)
# draw the original bounding boxes
for (x, y, w, h) in rects:
cv2.rectangle(orig, (x, y), (x + w, y + h), (0, 0, 255), 2)
# apply non-maxima suppression to the bounding boxes using a
# fairly large overlap threshold to try to maintain overlapping
# boxes that are still people
rects = np.array([[x, y, x + w, y + h] for (x, y, w, h) in rects])
pick = non_max_suppression(rects, probs=None, overlapThresh=0.65)
# draw the final bounding boxes
for (xA, yA, xB, yB) in pick:
cv2.rectangle(image, (xA, yA), (xB, yB), (0, 255, 0), 2)
# show some information on the number of bounding boxes
print("[INFO] {} original boxes, {} after suppression".format(
len(rects), len(pick)))
return image
def detect_images(hog, images_path):
# loop over the image paths
for image_path in paths.list_images(images_path):
# load the image and resize it to (1) reduce detection time
# and (2) improve detection accuracy
orig = cv2.imread(image_path)
image = detect_image(hog, orig)
# show the output images
cv2.imshow("Before NMS", orig)
cv2.imshow("After NMS", image)
if cv2.waitKey(0) & 0xFF == ord('q'):
break
def detect_video(hog, video_path):
cap = cv2.VideoCapture(video_path)
while True:
ret, frame = cap.read()
if not ret:
break
detected = detect_image(hog, frame)
cv2.imshow("capture", detected)
if cv2.waitKey(100) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
if __name__ == '__main__':
# ap = argparse.ArgumentParser()
# ap.add_argument("-i", "--images", required=True, help="path to images directory")
# args = vars(ap.parse_args())
# detect_images(args['images'])
hog = cv2.HOGDescriptor()
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
hog = hog_clf()
images_path = '../data/imgs/persons'
detect_images(hog, images_path)
video_path = '../data/video/vtest.avi'
detect_video(hog, video_path)
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