color = (0, 179, 0)
cv2.putText(
frame, object_name + ": " +
str(int(object_probability)) + "%", org, font,
fontscale, color, thickness)
return ret, cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
else:
return ret, None
else:
return ret, None
# Release the video source when the object is destroyed
def __del__(self):
if self.vid.isOpened():
self.vid.release()
# Create a window and pass it to the Application object
# Load dataset/model
detector = CustomObjectDetection()
detector.setModelTypeAsYOLOv3()
detector.setModelPath("manafv6.h5")
detector.setJsonPath("detection_config.json")
detector.loadModel()
window = Tk()
thread_gui = Gui(window, "Techrecycle")
thread_gui.start()
def __init__(self, detectorModel, setJson):
self.detector = CustomObjectDetection()
self.detector.setModelPath(detectorModel)
self.detector.setJsonPath(setJson)
self.detector.loadModel()
import io
import cv2
import sys
from PIL import Image
import numpy as np
from flask import Flask, request, jsonify
from imageai.Detection.Custom import CustomObjectDetection
MODEL_PATH = "../models/" + sys.argv[1]
JSON_PATH = "../json/" + sys.argv[2]
app = Flask(__name__)
dtc = CustomObjectDetection()
dtc.setModelTypeAsYOLOv3()
dtc.setModelPath(MODEL_PATH)
dtc.setJsonPath(JSON_PATH)
dtc.loadModel()
def detect_objects(img, min_prob=30):
"""Processes and transforms product images to detect objects learned
by the model loaded in global.
Arguments:
img {FileStorage} -- Uploaded image file though api request.
Keyword Arguments:
min_prob {int} -- Threshold probability for objects detected in
images. Low probability levels show more detections
(default: {30}).
def detectImage(queue):
detector = CustomObjectDetection()
detector.setModelTypeAsYOLOv3()
detector.setModelPath(modelFilePath)
detector.setJsonPath("detection_config.json")
detector.loadModel()
while True:
if queue.qsize() > 0:
try:
fileNameIn = queue.get()
logging.info("[detectImage] Processing %s" % fileNameIn)
filePathIn = os.path.join(imageFolderIn, fileNameIn)
filePathOut = os.path.join(imageFolderOut, fileNameIn)
filePathStorage = os.path.join(imageStorage, fileNameIn)
logging.info(filePathOut)
filePathSpare = os.path.join(spareImageFolder, fileNameIn)
filepathBorder = os.path.join(borderImageFolder, fileNameIn)
logging.info("\n---------------------------------------")
logging.info("moving to next image")
if borderTopBtm:
logging.info("filepathBorder: " + filepathBorder)
else:
logging.info("filePathIn: " + filePathIn)
logging.info("---------------------------------------")
if borderTopBtm:
borderImage = cv2.imread(filePathIn)
#border bottom
cv2.rectangle(borderImage, (0, 525), (1280, 720),
(0, 0, 0), -1)
##border top
cv2.rectangle(borderImage, (0, 0), (1280, 140), (0, 0, 0),
-1)
cv2.imwrite(filepathBorder, borderImage)
if borderTopBtm:
logging.info("[YOLO] Processing %s..." % filepathBorder)
detections = detector.detectObjectsFromImage(
imageIn=filepathBorder,
imageOut=filePathSpare,
minProbPerc=imageMinProb)
else:
logging.info("[YOLO] Processing %s..." % filePathIn)
detections = detector.detectObjectsFromImage(
imageIn=filePathIn,
imageOut=filePathSpare,
minProbPerc=imageMinProb)
probPercBest = 0
closestName = None
bestBB = None
img = cv2.imread(filePathIn)
for detection in detections:
name = detection["name"]
probPerc = detection["probPerc"]
bbPoints = detection["bbPoints"]
logging.info("name=%s, probPerc=%s, bbPoints=%s" %
(name, probPerc, bbPoints))
if probPerc > probPercBest:
probPercBest = probPerc
closestName = name
bestBB = bbPoints
if bestBB is None:
logging.info("Nothing is detected, skipping image...")
shutil.copy2(filePathIn, filePathOut)
shutil.copy2(filePathIn, filePathStorage)
continue
if probPercBest >= imageMaxProb:
logging.info("Model is sure that closestName is " +
closestName)
logging.info("fileNameIn: %s" % fileNameIn)
logging.info("fileNameIn.split('_'): %s" %
fileNameIn.split("_"))
robotCurrentX, robotCurrentY, robotCurrentDirection = fileNameIn.split(
"-")[1].split("_")[0], fileNameIn.split("-")[1].split(
"_")[1], fileNameIn.split("-")[1].split(
"_")[2].split('.')[0]
exportImageAbs(img, filePathOut, filePathStorage, bestBB,
closestName, robotCurrentX, robotCurrentY,
robotCurrentDirection)
continue
else:
logging.warn("Model is not confident")
shutil.copy2(filePathIn, filePathOut)
shutil.copy2(filePathIn, filePathStorage)
continue
except Exception as e:
logging.error("\nException %s\n" % e)
traceback.print_exc()
if "!_img.empty()" in str(e):
logging.error("Model does not detect images ")
if os.path.exists(filePathIn):
shutil.copy2(filePathIn, filePathOut)
shutil.copy2(filePathIn, filePathStorage)
continue