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vision.py
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vision.py
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#!/usr/bin/env python3
# Author: Steve Lawrence - Ozoid Robotics 2020 - Ozoid Ltd.
#
import os
import time
import picamera
import numpy as np
import cv2
import argparse
import io
import time
import imutils
import itertools
import rospy
from threading import Thread, Lock
from flask import Flask, Response
from robot5.msg import vision_detect
from edgetpu.classification.engine import ClassificationEngine
from edgetpu.detection.engine import DetectionEngine
from edgetpu.utils import dataset_utils
from PIL import Image
from PIL import ImageDraw
from imutils.video import VideoStream
from dt_apriltags import Detector
class Vision:
coral_model_file = "/home/pi/catkin_ws/src/robot5/models/mobilenet_ssd_v2/mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite"
coral_labels_file = "/home/pi/catkin_ws/src/robot5/models/mobilenet_ssd_v2/coco_labels.txt"
coral_confidence = 0.3
caffe_model_file = "/home/pi/catkin_ws/src/robot5/models/res10_300x300_ssd_iter_140000.caffemodel"
caffe_confidence = 0.5
caffe_prototxt = "/home/pi/catkin_ws/src/robot5/models/deploy.prototxt.txt"
face_cascade = cv2.CascadeClassifier('/home/pi/opencv/data/haarcascades/haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('/home/pi/opencv/data/haarcascades/haarcascade_eye.xml')
APRILWIDTH = 172
FOCALLENGTH = 0.304
def __init__(self):
self.coral_model = {}
self.coral_labels = {}
self.caffe_model = {}
self.at_detector = {}
self.videoStream = {}
self.status = None
self.captureFrame = None
self.visionFrame = None
self.thread = Thread(target=self.frameUpdate,args=())
self.thread.daemon = True
self.flaskThread = Thread(target=self.runFlask)
self.flaskThread.daemon = True
self.frameLock = Lock()
print("[INFO] Initialising ROS...")
#self.pub = rospy.Publisher(name='vision_detect',subscriber_listener=vision_detect,queue_size=5,data_class=vision_detect)
self.pub = rospy.Publisher('/vision_detect',vision_detect)
rospy.init_node('robot5_vision',anonymous=False)
for row in open(self.coral_labels_file):
(classID, label) = row.strip().split(maxsplit=1)
self.coral_labels[int(classID)] = label.strip()
print("[INFO] loading Coral model...")
self.coral_model = DetectionEngine(self.coral_model_file)
#print("[INFO] loading Caffe model...")
#self.caffe_model = cv2.dnn.readNetFromCaffe(self.caffe_prototxt, self.caffe_model_file)
self.at_detector = Detector(families='tag36h11',
nthreads=1,
quad_decimate=1.0,
quad_sigma=0.0,
refine_edges=1,
decode_sharpening=0.25,
debug=0)
print("[INFO] Running Flask...")
self.app = Flask(__name__)
self.add_routes()
self.flaskThread.start()
print("[INFO] starting video stream...")
self.videoStream = VideoStream(src=0,usePiCamera=True).start()
time.sleep(2.0) # warmup
self.captureFrame = self.videoStream.read()
self.visionFrame = self.captureFrame
self.thread.start()
time.sleep(0.5) # get first few
srun = True
print("[INFO] running frames...")
while srun:
srun = self.doFrame()
cv2.destroyAllWindows()
self.videoStream.stop()
def frameUpdate(self):
while True:
self.captureFrame = self.videoStream.read()
time.sleep(0.03)
def gen(self):
while True:
if self.visionFrame is not None:
bout = b"".join([b'--frame\r\nContent-Type: image/jpeg\r\n\r\n', self.visionFrame,b'\r\n'])
yield (bout)
else:
return ""
def add_routes(self):
@self.app.route("/word/<word>")
def some_route(word):
self.testout()
return "At some route:"+word
@self.app.route('/video_feed')
def video_feed():
return Response(self.gen(),mimetype='multipart/x-mixed-replace; boundary=frame')
def testout(self):
print("tested")
pass
def runFlask(self):
self.app.run(debug=False, use_reloader=False,host='0.0.0.0', port=8000)
def coralDetect(self,frame,orig):
start1 = time.time()
results = self.coral_model.detect_with_image(frame, threshold=self.coral_confidence,keep_aspect_ratio=True, relative_coord=False)
fcount = 0
points = []
for r in results:
box = r.bounding_box.flatten().astype("int")
(startX, startY, endX, endY) = box
label = self.coral_labels[r.label_id]
cv2.rectangle(orig, (startX, startY), (endX, endY), (0, 255, 0), 2)
y = startY - 15 if startY - 15 > 15 else startY + 15
cx = startX + (endX - startX/2)
cy = startY + (endY - startY/2)
#points.append({"type":"coral","num":fcount,"x":cx,"y":cy,"label":label,"score":r.score,"time":time.time() })
points.append(["coral",fcount,int(cx),int(cy),label,int(r.score*100),rospy.Time.now()])
fcount +=1
text = "{}: {:.2f}%".format(label, r.score * 100)
cv2.putText(orig, text, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
end1 = time.time()
#print("#1:",end1-start1)
return orig,points
def caffeDetect(self,frame,orig):
start2 = time.time()
(h, w) = frame.shape[:2]
blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)), 1.0,(300, 300), (104.0, 177.0, 123.0))
self.caffe_model.setInput(blob)
detections = self.caffe_model.forward()
fcount = 0
points = []
for i in range(0, detections.shape[2]):
confidence = detections[0, 0, i, 2]
if confidence < self.caffe_confidence:
continue
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
text = "{:.2f}%".format(confidence * 100)
y = startY - 10 if startY - 10 > 10 else startY + 10
cx = startX + (endX - startX/2)
cy = startY + (endY - startY/2)
#points.append({"type":"caffe","num":fcount,"x":cx,"y":cy,"score":confidence,"time":time.time()})
points.append(["caffe",fcount,int(cx),int(cy),"",int(confidence*10),rospy.Time.now()])
fcount +=1
cv2.rectangle(orig, (startX, startY), (endX, endY),(0, 0, 255), 2)
cv2.putText(orig, text, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
end2 = time.time()
#print("#2:",end2-start2)
return orig,points
def aprilDetect(self,grey,orig):
start3 = time.time()
Xarray = [338.563277422543, 0.0, 336.45495347495824, 0.0, 338.939280638548, 230.486982216255, 0.0, 0.0, 1.0]
camMatrix = np.array(Xarray).reshape((3,3))
params = (camMatrix[0,0],camMatrix[1,1],camMatrix[0,2],camMatrix[1,2])
tags = self.at_detector.detect(grey,True,params,0.065)
fcount = 0
points =[]
for tag in tags:
pwb = tag.corners[2][1] - tag.corners[0][1]
pwt = tag.corners[3][1] - tag.corners[1][1]
pwy = (pwb+pwt)/2
pwl = tag.corners[3][1] - tag.corners[0][1]
pwr = tag.corners[2][1] - tag.corners[1][1]
pwx = (pwl+pwr)/2
dist = self.distanceToCamera(self.APRILWIDTH,(pwx))
#print(dist)
#points.append({"type":"april","num":fcount,"x":pwx,"y":pwy,"label":tag.id,"score":dist,"time":time.time()})
points.append(["april",fcount,int(pwl + (pwx/2)),int(pwb + (pwy/2)),str(tag.tag_id)+"|"+str(dist),0,rospy.Time.now()])
fcount += 1
cv2.putText(orig, str(dist), (int(pwx), int(pwy)), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
for idx in range(len(tag.corners)):
cv2.line(orig,tuple(tag.corners[idx-1,:].astype(int)),tuple(tag.corners[idx,:].astype(int)),(0,255,0))
end3 = time.time()
#print("#3:",end3-start3)
return orig,points
def haarDetect(self,grey,orig):
start4 = time.time()
faces = self.face_cascade.detectMultiScale(grey,1.3,5)
fcount=0
points =[]
for(x,y,w,h) in faces:
#points.append({"type":"haar","num":fcount,"x":x+(w/2),"y":y+(h/2),"time":time.time()})
points.append(["haar",fcount,int(x+(w/2)),int(y+(h/2)),"",0,rospy.Time.now()])
orig = cv2.rectangle(orig,(x,y),(x+w,y+h),(255,255,0),2)
roi_gray = grey[y:y+h, x:x+w]
roi_color = orig[y:y+h, x:x+w]
fcount += 1
eyes = self.eye_cascade.detectMultiScale(roi_gray)
for (ex,ey,ew,eh) in eyes:
cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)
end4 = time.time()
#print("#4:",end4-start4)
return orig,points
def doFrame(self):
frame = self.captureFrame
if frame is None:
return False
frame = imutils.resize(frame, width=500)
orig = frame.copy()
grey = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame = Image.fromarray(frame)
outframe,cpoints = self.coralDetect(frame,orig)
outframe,apoints = self.aprilDetect(grey,outframe)
outframe,hpoints = self.haarDetect(grey,outframe)
#outframe,fpoints = self.caffeDetect(orig,outframe)
points = list(itertools.chain(cpoints,apoints,hpoints))
for p in points:
self.pub.publish(p[0],p[1],p[2],p[3],p[4],p[5],p[6])
pass
ret, self.visionFrame = cv2.imencode('.jpg', outframe)
#self.visionFrame = outframe
#cv2.imshow("Frame", outframe)
#key = cv2.waitKey(1) & 0xFF
#if key == ord("q"):
# return False
return True
def distanceToCamera(self,actWidth,perWidth):
return ((actWidth*self.FOCALLENGTH)/perWidth)*2
if __name__ == '__main__':
v = Vision()