Added more scripts

car-detection-stream.py

@@ -28,6 +28,14 @@
 #vehicle_classifier = cv2.CascadeClassifier('haars/cas4.xml') # from 
 #vehicle_classifier = cv2.CascadeClassifier('haars/cars2.xml') # from https://github.com/andrewssobral/vehicle_detection_haarcascades
 
+record = True
+if record:
+    fps = 10
+    capSize = (1200,675) # Size of video when is resized (original stream 1080x1920)
+    fourcc = cv2.cv.CV_FOURCC('m', 'p', '4', 'v') # note the lower case
+    vout = cv2.VideoWriter()
+    success = vout.open('output.mov',fourcc,fps,capSize,True) 
+
 while True:
     bytes += stream.read(1024)
     a = bytes.find(b'\xff\xd8')
@@ -36,15 +44,22 @@
         jpg = bytes[a:b+2]
         bytes = bytes[b+2:]
         i = cv2.imdecode(np.fromstring(jpg, dtype=np.uint8), cv2.IMREAD_COLOR) # decode image
-        image = imutils.resize(i, width=min(600, i.shape[1])) # resize image (optional)
+        image = imutils.resize(i, width=min(1200, i.shape[1])) # resize image (optional)
         gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
 
         #vehicles = vehicle_classifier.detectMultiScale(gray, 1.1, 2, maxSize=(200,200))
-        vehicles = vehicle_classifier.detectMultiScale(gray, 1.1, 2)
-
+        vehicles = vehicle_classifier.detectMultiScale(gray, 1.1, 5)
+        print len(vehicles), 'vehicles found...' 
         for (x,y,w,h) in vehicles:
             cv2.rectangle(image, (x,y), (x+w, y+h),(255,0,0),2)
 
         cv2.imshow('frame', image)
+        if record:
+            vout.write(image)
         if cv2.waitKey(1) == 27:
-            exit(0)
+            exit(0)
+
+if record:
+    vout.release() 
+    vout = None
+cv2.destroyAllWindows()

haar-detection-stream.py

@@ -18,9 +18,11 @@
 
 # Source from http://212.170.22.153:8080/view/viewer_index.shtml?id=412 - test first in VLC
 stream = urlopen('http://212.170.22.153:8080/mjpg/video.mjpg') # mjpg stream camera from an Axis network camera in Spain
+#stream = urlopen('http://10.0.0.1:60152/liveview.JPG?%211234%21http%2dget%3a%2a%3aimage%2fjpeg%3a%2a%21%21%21%21%21') # qx10 
 bytes = bytes()
 
 # Change to the haar as desired from what you want to test or use
+#classifier = cv2.CascadeClassifier('haars/face.xml')
 classifier = cv2.CascadeClassifier('haars/fullbody.xml')
 #classifier = cv2.CascadeClassifier('haars/lowerbody.xml')
 #classifier = cv2.CascadeClassifier('haars/upperbody.xml')

people-detection.py

@@ -0,0 +1,62 @@
+#!/usr/bin/env python
+
+"""people-detection.py: Detect cars from a web camera."""
+
+__author__ = "Aldo Vargas"
+__copyright__ = "Copyright 2017 Altax.net"
+
+__license__ = "GPL"
+__version__ = "1"
+__maintainer__ = "Aldo Vargas"
+__email__ = "alduxvm@gmail.com"
+__status__ = "Development"
+
+import numpy as np
+import cv2
+import imutils
+from imutils.object_detection import non_max_suppression
+
+cap = cv2.VideoCapture(0)
+#cap = cv2.VideoCapture('video.mp4')
+
+hog = cv2.HOGDescriptor()
+hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
+
+record = False
+
+if record:
+    fps = 15
+    capSize = (400,226) # this is the size of my source video
+    fourcc = cv2.cv.CV_FOURCC('m', 'p', '4', 'v') # note the lower case
+    vout = cv2.VideoWriter()
+    success = vout.open('output.mov',fourcc,fps,capSize,True) 
+
+while(True):
+    ret, image = cap.read()
+
+    image = imutils.resize(image, width=min(400, image.shape[1]))
+    orig = image.copy()
+
+    (rects, weights) = hog.detectMultiScale(image, winStride=(4, 4),
+        padding=(8, 8), scale=1.05)
+
+    for (x, y, w, h) in rects:
+        cv2.rectangle(orig, (x, y), (x + w, y + h), (0, 0, 255), 2)
+
+    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)
+
+    for (xA, yA, xB, yB) in pick:
+        cv2.rectangle(image, (xA, yA), (xB, yB), (0, 255, 0), 2)
+
+    cv2.imshow('frame',image)
+    if record:
+        vout.write(image)
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+cap.release()
+if record:
+    vout.release() 
+    vout = None
+cv2.destroyAllWindows()