Final versions of camera-tools

camera/counter_people.py

@@ -17,6 +17,7 @@ python peopleCounter.py -c  # Attempts to detect people using webcam
 
 HOGCV = cv2.HOGDescriptor()
 HOGCV.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
+#HOGCV.set
 VERBOSITY = False
 
 def detector(image):
@@ -26,7 +27,9 @@ def detector(image):
 
     clone = image.copy()
 
-    (rects, _) = HOGCV.detectMultiScale(image, winStride=(4, 4), padding=(8, 8), scale=1.05)
+
+
+    (rects, _) = HOGCV.detectMultiScale(image, winStride=(2, 2), padding=(8, 8), scale=1.05)
 
     # draw the original bounding boxes
     for (x, y, w, h) in rects:
@@ -129,11 +132,32 @@ def videoDetect(cap):
 
 def remoteDetect(image_hub):
     while True:
-        rpi_name, image = image_hub.recv_image()
+        rpi_name, frame = image_hub.recv_image()
-        cv2.imshow(rpi_name, image) # 1 window for each RPi
-        cv2.waitKey(1)
         image_hub.send_reply(b'OK')
 
+        frame = imutils.resize(frame, width=min(400, frame.shape[1]))
+        result = detector(frame.copy())
+
+        # shows the result
+        for (xA, yA, xB, yB) in result:
+            cv2.rectangle(frame, (xA, yA), (xB, yB), (0, 255, 0), 2)
+        
+        #if VERBOSITY:
+        cv2.imshow('frame', frame)
+        #cv2.waitKey(0)
+
+        #if time.time() - init >= sample_time:
+        if len(result):
+            print("{} people detected.".format(len(result)))
+        #init = time.time()
+
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+        
+        #cv2.imshow(rpi_name, frame) # 1 window for each RPi
+        #cv2.waitKey(1)
+        
+    
 
 
 def detectPeople(args):

camera/person_detection.py

@@ -1,52 +1,58 @@
 #!/usr/bin/env python
 
 import argparse
-#from datetime import datetime, time
 import time
 from statistics import median
-
 import imutils
 from imutils.video import VideoStream
-#from imutils.video import FPS
-
 import cv2
 import numpy as np
-
+import paho.mqtt.client as mqtt
-frame_timer = None
+from video_stream import imagezmq
-contour_timer = None
-detection_timer = None
-
-frame_time = []
-contour_time = []
-detection_time = []
 
 VISUAL_DEBUG = True
+BROKER = "141.75.33.126"
+PORT = 1883
 
 def getArgs():
     """ Arguments """
     ap = argparse.ArgumentParser()
     ap.add_argument("-v", "--video", help="path to the video file")
     ap.add_argument("-a", "--min-area", type=int, default=500, help="minimum area size")
-    ap.add_argument("-t", "--tracker", type=str, default="csrt", help="OpenCV object tracker type")
     return vars(ap.parse_args())
 
 
 def main():
+    try:
+        mqtt_client = mqtt.Client("pi-camera")
+        mqtt_client.connect(BROKER, PORT)
+    except:
+        print("Connection to MQTT-Broker failed.")
+        return 1
+
+    try:
         args = getArgs()
         timer = Timer()
 
         # if the video argument is None, then the code will read from webcam (work in progress)
         if args.get("video", None) is None:
-        vs = VideoStream(src=0).start()
+            #vs = VideoStream(src=0).start()
+            image_hub = imagezmq.ImageHub()
+            
             time.sleep(2.0)
         # otherwise, we are reading from a video file
         else:
             vs = cv2.VideoCapture(args["video"])
 
         cv2.namedWindow('Video stream', cv2.WINDOW_NORMAL)
-    detector = DetectionFromFrame(args["min_area"], 0.5)
+        detector = DetectionFromFrame(args["min_area"], 0.8)
         while True:
+            people_count = 0
             timer.start_frame_timer()
+            if args.get("video", None) is None:
+                rpi_name, detector.currentFrame = image_hub.recv_image()
+                image_hub.send_reply(b'OK')
+            else:
                 detector.currentFrame = vs.read()
                 detector.currentFrame = detector.currentFrame if args.get("video", None) is None else detector.currentFrame[1]
             # if the frame can not be grabbed, then we have reached the end of the video
@@ -57,10 +63,10 @@ def main():
             detector.currentFrame = imutils.resize(detector.currentFrame, width=500)
             detector.framecounter += 1
             if detector.framecounter > 1:
+                
                 cnts = detector.prepareFrame()
 
                 for c in cnts:
-                timer.start_contour_timer()
                     bound_rect = cv2.boundingRect(c)
                     #(x, y, w, h) = cv2.boundingRect(c)
                     #initBB2 =(x,y,w,h)
@@ -74,26 +80,23 @@ def main():
                                                     bound_rect[0]:bound_rect[0]+bound_rect[2]]
                     trackbox = cv2.resize(trackbox, (224, 224))
                     #cv2.imshow('image',trackbox)
-                timer.start_detection_timer()
                     blob = cv2.dnn.blobFromImage(cv2.resize(trackbox, (300, 300)),0.007843, (300, 300), 127.5)
                     net.setInput(blob)
                     detections = net.forward()
                     
                     for i in np.arange(0, detections.shape[2]):
-                    
+                        people_count += detector.detectConfidentiallyPeople(i, detections, bound_rect)
-                    detector.detectConfidentiallyPeople(i, detections, bound_rect)
-                timer.stop_detection_timer()
 
                     cv2.rectangle(detector.currentFrame, (bound_rect[0], bound_rect[1]), 
                                     (bound_rect[0] + bound_rect[2], bound_rect[1] + bound_rect[3]), (255, 255, 0), 1)
                 
-                timer.stop_contour_timer()
-
-            
             # show the frame and record if the user presses a key
             cv2.imshow("Video stream", detector.currentFrame)
             key = cv2.waitKey(1) & 0xFF
 
+            # send number of people detected via mqtt
+            mqtt_client.publish("/gso/bb/104/Camera", str(people_count))
+
             # if the `q` key is pressed, break from the lop
             if key == ord("q"):
                 break
@@ -101,55 +104,17 @@ def main():
                 detector.firstFrame = None
             #detector.lastFrame = detector.currentFrame
 
-        timer.print_time()
+            timer.print_frame_time()
+
 
         # finally, stop the camera/stream and close any open windows
+        if args.get("video", None) is not None:
             vs.stop() if args.get("video", None) is None else vs.release()
+
         cv2.destroyAllWindows()
-
+    finally:
-class Timer:
+        if args.get("video", None) is None:
-    def __init__(self):
+            image_hub.send_reply(b'OK')
-        self.frame_timer = None
-        self.contour_timer = None
-        self.detection_timer = None
-
-        self.contour_time = []
-        self.detection_time = []
-
-    def start_frame_timer(self):
-        self.frame_timer = time.time()
-
-    def get_frame_time(self):
-        return time.time() - self.frame_timer
-
-    def start_contour_timer(self):
-        self.contour_timer = time.time()
-
-    def stop_contour_timer(self):
-        self.contour_time.append(time.time() - self.contour_timer)
-
-    def start_detection_timer(self):
-        self.detection_timer = time.time()
-
-    def stop_detection_timer(self):
-        self.detection_time.append(time.time() - self.detection_timer)
-
-    def print_time(self):
-        average_contour = 0 if not self.contour_time else sum(self.contour_time)/float(len(self.contour_time))
-        average_detection = 0 if not self.detection_time else sum(self.detection_time)/float(len(self.detection_time))
-
-        median_contour = 0 if not self.contour_time else median(self.contour_time)
-        median_detection = 0 if not self.detection_time else median(self.detection_time)
-
-        total_contour = sum(self.contour_time)
-        total_detection = sum(self.detection_time)
-
-        print("Time for Frame: {:.2f}. Contour Total: {:.2f}. Contour Median: {:.2f}. Contour Average: {:.2f}. Detection Total: {:.2f}. Detection Median: {:.2f}. Detection Average: {:.2f}. ".format(
-            self.get_frame_time(), total_contour, median_contour, average_contour, total_detection, median_detection, average_detection))
-        #print("Contour Times:" + str(timer.contour_time))
-        #print("Detection Times:" + str(timer.detection_time))
-        self.contour_time = []
-        self.detection_time = []
 
 class DetectionFromFrame:
     def __init__(self, min_size, confidence):
@@ -205,27 +170,65 @@ class DetectionFromFrame:
         confidence = detections[0, 0, i, 2]
 
         if confidence > self.confidence_level:
-            # extract the index of the class label from the `detections`, then compute the (x, y)-coordinates of
+            # draw a rectangle in green over the detected area
-            # the bounding box for the object
-            #idx = int(detections[0, 0, i, 1])
-            #box = detections[0, 0, i, 3:7] * np.array([bound_rect[2], bound_rect[3], bound_rect[2], bound_rect[3]])
-            #(startX, startY, endX, endY) = box.astype("int")
-            # draw the prediction on the frame
-            
-            #label = "{}: {:.2f}%".format(CLASSES[idx], confidence * 100)
-            label = "{:.2f}%".format(confidence * 100)
-            
-            #cv2.rectangle(frame, (startX, startY), (endX, endY), COLORS[idx], 2)
             cv2.rectangle(self.currentFrame, (bound_rect[0], bound_rect[1]),
                                 (bound_rect[0] + bound_rect[2], bound_rect[1] + bound_rect[3]), detected_color, 3)
             
-            y = bound_rect[1] - 15 if bound_rect[1] - 15 > 15 else bound_rect[1] + 15
+            label = "{:.2f}%".format(confidence * 100)
-
-            #cv2.putText(frame, label, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
             cv2.putText(self.currentFrame, label, (bound_rect[0], bound_rect[1]-5), cv2.FONT_HERSHEY_SIMPLEX, 0.3, detected_color, 1)
-            #cv2.imshow("Video stream", self.currentFrame)
-            #print("Person found")
 
+            return 1
+        else:
+            return 0
+
+class Timer:
+    def __init__(self):
+        self.frame_timer = None
+        self.contour_timer = None
+        self.detection_timer = None
+
+        self.contour_time = []
+        self.detection_time = []
+
+    def start_frame_timer(self):
+        self.frame_timer = time.time()
+
+    def get_frame_time(self):
+        return time.time() - self.frame_timer
+
+    def start_contour_timer(self):
+        self.contour_timer = time.time()
+
+    def stop_contour_timer(self):
+        self.contour_time.append(time.time() - self.contour_timer)
+
+    def start_detection_timer(self):
+        self.detection_timer = time.time()
+
+    def stop_detection_timer(self):
+        self.detection_time.append(time.time() - self.detection_timer)
+
+    def print_frame_time(self):
+        print("Time for Frame: {:.2f}.".format(self.get_frame_time()))
+        
+
+    def print_other_times(self):
+        average_contour = 0 if not self.contour_time else sum(self.contour_time)/float(len(self.contour_time))
+        average_detection = 0 if not self.detection_time else sum(self.detection_time)/float(len(self.detection_time))
+
+        median_contour = 0 if not self.contour_time else median(self.contour_time)
+        median_detection = 0 if not self.detection_time else median(self.detection_time)
+
+        total_contour = sum(self.contour_time)
+        total_detection = sum(self.detection_time)
+
+        print("Contour Total: {:.2f}. Contour Median: {:.2f}. Contour Average: {:.2f}.".format(
+            total_contour, median_contour, average_contour))
+        print("Detection Total: {:.2f}. Detection Median: {:.2f}. Detection Average: {:.2f}. ".format(
+            total_detection, median_detection, average_detection))
+
+        self.contour_time = []
+        self.detection_time = []
 
 
 if __name__ == "__main__":