Merge branch 'master' of https://git.efi.th-nuernberg.de/gitea/heimbsle69869/smarthome-presence-detect

.gitignore

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

camera/.vscode/launch.json

@@ -0,0 +1,21 @@
+{
+    "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

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

camera/person-detection.py

@@ -1,52 +0,0 @@
-# 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)