56 lines
1.9 KiB
Python
Executable File
56 lines
1.9 KiB
Python
Executable File
#!/usr/bin/env python
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# import the necessary packages
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from __future__ import print_function
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from imutils.object_detection import non_max_suppression
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from imutils import paths
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import numpy as np
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import argparse
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import imutils
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import cv2
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# construct the argument parse and parse the arguments
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ap = argparse.ArgumentParser()
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ap.add_argument("-i", "--images", required=True, help="path to images directory")
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args = vars(ap.parse_args())
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# initialize the HOG descriptor/person detector
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hog = cv2.HOGDescriptor()
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hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
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# loop over the image paths
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for imagePath in paths.list_images(args["images"]):
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# load the image and resize it to (1) reduce detection time
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# and (2) improve detection accuracy
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image = cv2.imread(imagePath)
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image = imutils.resize(image, width=min(400, image.shape[1]))
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orig = image.copy()
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# detect people in the image
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(rects, weights) = hog.detectMultiScale(image, winStride=(4, 4),
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padding=(8, 8), scale=1.05)
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# draw the original bounding boxes
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for (x, y, w, h) in rects:
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cv2.rectangle(orig, (x, y), (x + w, y + h), (0, 0, 255), 2)
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# apply non-maxima suppression to the bounding boxes using a
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# fairly large overlap threshold to try to maintain overlapping
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# boxes that are still people
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rects = np.array([[x, y, x + w, y + h] for (x, y, w, h) in rects])
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pick = non_max_suppression(rects, probs=None, overlapThresh=0.65)
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# draw the final bounding boxes
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for (xA, yA, xB, yB) in pick:
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cv2.rectangle(image, (xA, yA), (xB, yB), (0, 255, 0), 2)
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# show some information on the number of bounding boxes
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filename = imagePath[imagePath.rfind("/") + 1:]
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print("[INFO] {}: {} original boxes, {} after suppression".format(filename, len(rects), len(pick)))
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# show the output images
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if len(pick):
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#cv2.imshow("Before NMS", orig)
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cv2.imshow("After NMS", image)
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cv2.waitKey(0)
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