.gitignore

@@ -8,6 +8,3 @@ camera/videos
 *.h264
 *.mp4
 *.png
-.vscode/
-camera/.vscode/
-camera/.vscode/launch.json

camera/.vscode/launch.json

@@ -1,7 +1,6 @@
 {
     "version": "0.2.0",
     "configurations": [
-
         {
             "name": "Python: Current File",
             "type": "python",
@@ -16,7 +15,7 @@
             "request": "launch",
             "program": "${file}",
             "console": "integratedTerminal",
-            "args": ["-v", "run.mp4"]
+            "args": ["-v", "~/Videos/video.h264"]
         }
     ]
 }

camera/counter_people.py

@@ -1,12 +1,11 @@
-#!/usr/bin/env python
-
-import argparse
-import numpy as np
-import cv2
-import imutils
 from imutils.object_detection import non_max_suppression
-from video_stream import imagezmq
+import numpy as np
-
+import imutils
+import cv2
+import time
+import argparse
+import time
+import base64
 
 '''
 Usage:
@@ -17,7 +16,6 @@ python peopleCounter.py -c  # Attempts to detect people using webcam
 
 HOGCV = cv2.HOGDescriptor()
 HOGCV.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
-VERBOSITY = False
 
 def detector(image):
     '''
@@ -26,7 +24,7 @@ def detector(image):
 
     clone = image.copy()
 
-    (rects, _) = HOGCV.detectMultiScale(image, winStride=(4, 4), padding=(8, 8), scale=1.05)
+    (rects, weights) = HOGCV.detectMultiScale(image, winStride=(4, 4), padding=(8, 8), scale=1.05)
 
     # draw the original bounding boxes
     for (x, y, w, h) in rects:
@@ -39,65 +37,54 @@ def detector(image):
 
     return result
 
-def args_parser():
-    ''' images, videos, remote or a local camera feed allowed
-        verbose for added debugging'''
-    ap = argparse.ArgumentParser()
-    ap.add_argument("-i", "--image", default=None, 
-                    help="path to image test file directory")
-    ap.add_argument("-c", "--camera", action="store_true", default=False, 
-                    help="Set as true if you wish to use the camera")
-    ap.add_argument("-v", "--video", default=None, 
-                    help="path to the video file")
-    ap.add_argument("-r", "--remote", action="store_true", default=False, 
-                    help="video comes from remote source via imagezmq")
-    ap.add_argument("--verbose", action="store_true", default=False,
-                    help="increase output verbosity")
-    args = vars(ap.parse_args())
 
-    if args["verbose"]:
+def buildPayload(variable, value, context):
-        VERBOSITY = True
+    return {variable: {"value": value, "context": context}}
+
+
+def argsParser():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("-i", "--image", default=None, help="path to image test file directory")
+    ap.add_argument("-c", "--camera", default=False, help="Set as true if you wish to use the camera")
+    ap.add_argument("-v", "--video", default=None, help="path to the video file")
+    args = vars(ap.parse_args())
 
     return args
 
-def usage():
-    print("usage: counter_people.py [-h] [-i IMAGE] [-c] [-v] [-r REMOTE] [--verbose]")
-    print()
-    print("optional arguments:")
-    print("  -h, --help            show this help message and exit")
-    print("  -i IMAGE, --image IMAGE")
-    print("                        path to image test file directory")
-    print("  -c, --camera          Set as true if you wish to use the camera")
-    print("  -v, --video           path to the video file")
-    print("  -r REMOTE, --remote REMOTE")
-    print("                        video comes from remote source via imagezmq")
-    print("  --verbose             increase output verbosity")
 
 def localDetect(image_path):
     result = []
     image = cv2.imread(image_path)
     image = imutils.resize(image, width=min(400, image.shape[1]))
+    clone = image.copy()
     if len(image) <= 0:
         print("[ERROR] could not read local image")
         return result
     print("[INFO] Detecting people")
     result = detector(image)
 
-    if VERBOSITY:
+    """# shows the result
-    # shows the result
     for (xA, yA, xB, yB) in result:
         cv2.rectangle(image, (xA, yA), (xB, yB), (0, 255, 0), 2)
 
     cv2.imshow("result", image)
     cv2.waitKey(0)
-        cv2.destroyWindow("result")
+    cv2.destroyAllWindows()
 
-        #cv2.imwrite("result.png", np.hstack((clone, image)))
+    cv2.imwrite("result.png", np.hstack((clone, image)))"""
     return result#(result, image)
 
 
-def videoDetect(cap):
+def cameraDetect(video_path="", sample_time=5):
-    while True:
+
+    if video_path:
+        cap = cv2.VideoCapture(video_path)
+    else:
+        cap = cv2.VideoCapture(0)
+    
+    #init = time.time()
+
+    while(True):
         # Capture frame-by-frame
         _, frame = cap.read()
 
@@ -110,16 +97,15 @@ def videoDetect(cap):
         # 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 result:
+        if len(result):
             print("{} people detected.".format(len(result)))
         #init = time.time()
         
+
         if cv2.waitKey(1) & 0xFF == ord('q'):
             break
 
@@ -127,50 +113,38 @@ def videoDetect(cap):
     cap.release()
     cv2.destroyAllWindows()
 
-def remoteDetect(image_hub):
-    while True:
-        rpi_name, image = image_hub.recv_image()
-        cv2.imshow(rpi_name, image) # 1 window for each RPi
-        cv2.waitKey(1)
-        image_hub.send_reply(b'OK')
 
+def convert_to_base64(image):
+    image = imutils.resize(image, width=400)
+    img_str = cv2.imencode('.png', image)[1].tostring()
+    b64 = base64.b64encode(img_str)
+
+    return b64.decode('utf-8')
 
 
 def detectPeople(args):
     image_path = args["image"]
     video_path = args["video"]
-    camera = True if args["camera"] else False
+    camera = True if str(args["camera"]) == 'true' else False
-    remote = True if args["remote"] else False
 
     # Routine to read local image
-    if image_path is not None:
+    if image_path != None and not camera and video_path == None:
         print("[INFO] Image path provided, attempting to read image")
         (result, image) = localDetect(image_path)
         print(str(len(result)) + " People detected.")
 
-    elif video_path is not None:
+    if video_path != None and not camera:
-        print("[INFO] Video path provided, reading video")
+        print("[INFO] reading video")
-        cap = cv2.VideoCapture(video_path)
+        cameraDetect(video_path)
-        videoDetect(cap)
 
     # Routine to read images from webcam
-    elif camera:
+    if camera:
-        print("[INFO] Reading images from local camera")
+        print("[INFO] reading camera images")
-        cap = cv2.VideoCapture(0)
+        cameraDetect()
-        videoDetect(cap)
-
-    elif remote:
-        print("[INFO] Reading images from remote stream")
-        image_hub = imagezmq.ImageHub()
-        remoteDetect(image_hub)
-
-    else:
-        usage()
-
 
 
 def main():
-    args = args_parser()
+    args = argsParser()
     detectPeople(args)
 
 

camera/person_detection.py

@@ -1,232 +0,0 @@
-#!/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
-
-frame_timer = None
-contour_timer = None
-detection_timer = None
-
-frame_time = []
-contour_time = []
-detection_time = []
-
-VISUAL_DEBUG = True
-
-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():
-    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()
-        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)
-    while True:
-        timer.start_frame_timer()
-        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
-        if detector.currentFrame is None:
-            break
-
-        # resize the frame to 500
-        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)
-
-                prott1 = r'ML-Models/MobileNetSSD_deploy.prototxt'
-                prott2 = r'ML-Models/MobileNetSSD_deploy.caffemodel'
-                net = cv2.dnn.readNetFromCaffe(prott1, prott2)
-
-                #trackbox = detector.currentFrame[y:y+h, x:x+w]boundRect[1]
-                trackbox = detector.currentFrame[bound_rect[1]:bound_rect[1]+bound_rect[3], 
-                                                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]):
-                    
-                    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
-
-        # if the `q` key is pressed, break from the lop
-        if key == ord("q"):
-            break
-        if key == ord("d"):
-            detector.firstFrame = None
-        #detector.lastFrame = detector.currentFrame
-
-        timer.print_time()
-
-    # finally, stop the camera/stream and close any open windows
-    vs.stop() if args.get("video", None) is None else vs.release()
-    cv2.destroyAllWindows()
-
-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_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):
-        self.min_size = min_size
-        self.confidence_level = confidence
-
-        self.firstFrame = None
-        self.currentFrame = None
-        
-        self.initBB2 = None
-        self.fps = None
-        self.differ = None
-        self.now = ''
-        self.framecounter = 0
-        self.people_count_total = 0
-
-
-    def prepareFrame(self):
-        gray = cv2.cvtColor(self.currentFrame, cv2.COLOR_BGR2GRAY)
-        gray = cv2.GaussianBlur(gray, (21, 21), 0)
-
-        # if the first frame is None, initialize it
-        if self.firstFrame is None:
-            self.firstFrame = gray
-            return []
-
-        # compute the absolute difference between the current frame and first frame
-        frameDelta = cv2.absdiff(self.firstFrame, gray)
-        thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
-        
-        #debug
-        """if VISUAL_DEBUG: 
-            cv2.imshow("debug image", thresh)
-            cv2.waitKey(0)
-            cv2.destroyWindow("debug image")
-            #cv2.destroyWindow("threshhold image")"""
-        
-        # dilate the thresholded image to fill in holes
-        thresh = cv2.dilate(thresh, None, iterations=2)
-
-        # find contours on thresholded image
-        thresh = np.uint8(thresh)
-        cnts, _  = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-
-        return cnts
-
-    def detectConfidentiallyPeople(self, i, detections, bound_rect):
-        #CLASSES = ["person"]
-
-        detected_color = (0, 255, 0)
-        #COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))
-
-        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
-            # 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
-
-            #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")
-
-
-
-if __name__ == "__main__":
-    main()

camera/video_stream/zmq_video_server.py

@@ -8,4 +8,3 @@ while True:  # show streamed images until Ctrl-C
     cv2.imshow(rpi_name, image) # 1 window for each RPi
     cv2.waitKey(1)
     image_hub.send_reply(b'OK')
-