process_opencv.py

# ped image detection drawn from http://www.pyimagesearch.com/2015/11/09/pedestrian-detection-opencv/

import os
from imutils.object_detection import non_max_suppression
from imutils import paths
import numpy as np
import argparse
import imutils
import cv2
import datetime

#figure out how to use trained svm
# svm_trained = "svm_data.dat"
image_dir = "t3"
# image_dir = "training/INRIAPerson/Train/pos"



# construct the argument parse and parse the arguments - useful for invoking from shell
# ap = argparse.ArgumentParser()
# ap.add_argument("-i", "--images", required=True, help="path to images directory")
# args = vars(ap.parse_args())

# initialize the HOG descriptor/person detector
hog = cv2.HOGDescriptor()
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())

# loop over the image paths
for imagePath in paths.list_images(image_dir):
    # load the image and resize it to (1) reduce detection time
    # and (2) improve detection accuracy
    print imagePath
    image = cv2.imread(imagePath)
    print image
    image = imutils.resize(image, width=min(1400, image.shape[1]))
    orig = image.copy()

    # detect people in the image
    start = datetime.datetime.now() # sets up a timer
    (rects, weights) = hog.detectMultiScale(image, winStride=(4, 4),
                                            padding=(4, 4), scale=1.5)

    print("[INFO] detection took: {}s".format(
        (datetime.datetime.now() - start).total_seconds()))

    # 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)
    print len(rects)
    # 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
    filename = imagePath[imagePath.rfind("/") + 1:]
    print("[INFO] {}: {} original boxes, {} after suppression".format(
        filename, len(rects), len(pick)))

    # show the output images
    cv2.imshow("Before NMS", orig)
    cv2.imshow("After NMS", image)
    cv2.waitKey(1)