1 files changed, 101 insertions, 0 deletions
diff --git a/dip/hog_det/pedestrian_det.py b/dip/hog_det/pedestrian_det.py
new file mode 100644
index 0000000..11f54b0
--- /dev/null
+++ b/dip/hog_det/pedestrian_det.py
@@ -0,0 +1,101 @@
+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)