.gitignore

@@ -1,5 +1,2 @@
-*.orig
 *.pyc
 camera/venv
-camera/images
-camera/videos

camera/.vscode/launch.json

@@ -1,21 +0,0 @@
-{
-    "version": "0.2.0",
-    "configurations": [
-        {
-            "name": "Python: Current File",
-            "type": "python",
-            "request": "launch",
-            "program": "${file}",
-            "console": "integratedTerminal"
-        },
-
-        {
-            "name": "Python: Current File with args",
-            "type": "python",
-            "request": "launch",
-            "program": "${file}",
-            "console": "integratedTerminal",
-            "args": ["-v", "~/Videos/video.h264"]
-        }
-    ]
-}

camera/.vscode/settings.json

@@ -1,3 +0,0 @@
-{
-    "python.pythonPath": "venv/bin/python"
-}

camera/person-detection.py

@@ -0,0 +1,52 @@
+# import the necessary packages
+from __future__ import print_function
+from imutils.object_detection import non_max_suppression
+from imutils import paths
+import numpy as np
+import argparse
+import imutils
+import cv2
+
+# construct the argument parse and parse the arguments
+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(args["images"]):
+    # load the image and resize it to (1) reduce detection time
+    # and (2) improve detection accuracy
+    image = cv2.imread(imagePath)
+    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=1.05)
+
+    # 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
+    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(0)