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How to use drawBboxes method in fMBT

Best Python code snippet using fMBT_python

yolov3-app.py

Source:yolov3-app.py

...111        return inference_duration, detected_objects112    except Exception as e:113        print('EXCEPTION:', str(e))114        return 'Error processing image', 500115def drawBboxes(image, detected_objects):116    objects_identified = len(detected_objects)117    118    iw, ih = image.size119    draw = ImageDraw.Draw(image)    120    textfont = ImageFont.load_default()121    122    for pos in range(objects_identified):       123        entity = detected_objects[pos]['entity'] 124        box = entity["box"]125        x1 = box["l"]126        y1 = box["t"]127        x2 = box["w"]128        y2 = box["h"]129        130        x1 = x1 * iw131        y1 = y1 * ih132        x2 = (x2 * iw) + x1133        y2 = (y2 * ih) + y1134        tag = entity['tag']135        objClass = tag['value']        136        draw.rectangle((x1, y1, x2, y2), outline = 'blue', width = 1)137        print('rectangle drawn')138        draw.text((x1, y1), str(objClass), fill = "white", font = textfont)139     140    return image141app = Flask(__name__)142# / routes to the default function which returns 'Hello World'143@app.route('/', methods=['GET'])144def defaultPage():145    return Response(response='Hello from Yolov3 inferencing based on ONNX', status=200)146@app.route('/stream/<id>')147def stream(id):148    respBody = ("<html>"149                "<h1>Stream with inferencing overlays</h1>"150                "<img src=\"/mjpeg/" + id + "\"/>"151                "</html>")152    return Response(respBody, status= 200)153    #return render_template('mjpeg.html')154# /score routes to scoring function 155# This function returns a JSON object with inference duration and detected objects156@app.route("/score", methods=['POST'])157def score():158    try:159        objectType = None160        confidenceThreshold = 0.0161        if (request.args):162            try:163                objectType = request.args.get('object')164                stream = request.args.get('stream')165                confidence = request.args.get('confidence')166                if confidence is not None:167                    confidenceThreshold = float(confidence)                168            except Exception as ex:169                print('EXCEPTION:', str(ex))                                170        imageData = io.BytesIO(request.get_data())171        # load the image172        img = Image.open(imageData)173        inference_duration, detected_objects = processImage(img, objectType, confidenceThreshold)        174        try:        175            if stream is not None:176                output_img = drawBboxes(img, detected_objects)177                imgBuf = io.BytesIO()178                output_img.save(imgBuf, format='JPEG')179                # post the image with bounding boxes so that it can be viewed as an MJPEG stream180                postData = b'--boundary\r\n' + b'Content-Type: image/jpeg\r\n\r\n' + imgBuf.getvalue() + b'\r\n'181                requests.post('http://127.0.0.1:80/mjpeg_pub/' + stream, data = postData)182        except Exception as ex:183            print('EXCEPTION:', str(ex))184        if len(detected_objects) > 0:185            respBody = {                    186                        "inferences" : detected_objects187                    }188            respBody = json.dumps(respBody)189            return Response(respBody, status= 200, mimetype ='application/json')190        else:191            return Response(status= 204)            192    except Exception as e:193        print('EXCEPTION:', str(e))194        return Response(response='Error processing image ' + str(e), status= 500)195# /score-debug routes to score_debug196# This function scores the image and stores an annotated image for debugging purposes197@app.route('/score-debug', methods=['POST'])198def score_debug():199    try:200        imageData = io.BytesIO(request.get_data())201        # load the image202        img = Image.open(imageData)203        inference_duration, detected_objects = processImage(img)204        print('Inference duration was ', str(inference_duration))205        output_img = drawBboxes(img, detected_objects)206        # datetime object containing current date and time207        now = datetime.now()208        209        output_img_file = now.strftime("%d_%m_%Y_%H_%M_%S.jpeg")210        output_img.save(output_dir + "/" + output_img_file)211        respBody = {                    212                    "inferences" : detected_objects213                    }                   214        215        return respBody216    except Exception as e:217        print('EXCEPTION:', str(e))218        return Response(response='Error processing image', status= 500)219# /annotate routes to annotation function 220# This function returns an image with bounding boxes drawn around detected objects221@app.route('/annotate', methods=['POST'])222def annotate():223    try:224        imageData = io.BytesIO(request.get_data())225        # load the image226        img = Image.open(imageData)227        inference_duration, detected_objects = processImage(img)228        print('Inference duration was ', str(inference_duration))229        img = drawBboxes(img, detected_objects)230        231        imgByteArr = io.BytesIO()        232        img.save(imgByteArr, format = 'JPEG')        233        imgByteArr = imgByteArr.getvalue()                234        return Response(response = imgByteArr, status = 200, mimetype = "image/jpeg")235    except Exception as e:236        print('EXCEPTION:', str(e))237        return Response(response='Error processing image', status= 500)238# Load and initialize the model239init()240if __name__ == '__main__':241    # Run the server...

detect.py

Source:detect.py

1import os2import cv23import time4import mxnet as mx5from . import predict6from .config_farm import configuration_10_320_20L_5scales_v2 as cfg7from threading import Thread8model_file_path = "LFFD/models/train_10_320_20L_5scales_v2_iter_1000000.params" 9symbol_file_path = "LFFD/symbols/symbol_10_320_20L_5scales_v2_deploy.json"10ctx = mx.gpu(0)11### Threshold for Face Matching12cosine_threshold = 0.813proba_threshold = 0.614comparing_num = 515face_predictor = predict.Predict(mxnet=mx,16                    symbol_file_path=symbol_file_path,17                    model_file_path=model_file_path,18                    ctx=ctx,19                    receptive_field_list=cfg.param_receptive_field_list,20                    receptive_field_stride=cfg.param_receptive_field_stride,21                    bbox_small_list=cfg.param_bbox_small_list,22                    bbox_large_list=cfg.param_bbox_large_list,23                    receptive_field_center_start=cfg.param_receptive_field_center_start,24                    num_output_scales=cfg.param_num_output_scales)25class DetectFaces():26    def __init__(self, frame = None):27        ### Loading Face Detection Models28        self.frame = frame29        self.stopped = False30       31    def start(self):32        print("START VIDEO PROCESSSING")33        Thread(target=self.drawBBoxes, args=()).start()34        Thread.daemon = True35        return self36    def drawBBoxes(self):37        while not self.stopped:38            frame = self.frame39            bboxes, infer_time = face_predictor.predict(frame, resize_scale=1, score_threshold=0.6, top_k=10000, \40                                                           NMS_threshold=0.4, NMS_flag=True, skip_scale_branch_list=[]) 41            42            h, w, c = frame.shape43            for bbox in bboxes:44                print(bbox)45                bbox_int = [int(b) for b in bbox[:-1]]46                bbox_int[0] = max(0, min(w - 1, bbox_int[0]))47                bbox_int[1] = max(0, min(h - 1, bbox_int[1]))48                bbox_int[2] = max(0, min(w - 1, bbox_int[2]))49                bbox_int[3] = max(0, min(h - 1, bbox_int[3]))50                cv2.rectangle(frame, tuple(bbox_int[0:2]), tuple(bbox_int[2:4]), (0, 255, 0), 2)51                self.frame = frame52    53    def stop(self):...

objdetect.py

Source:objdetect.py

1import cv2 as cv2import torch3# Load model4model = (5    torch.hub.load("ultralytics/yolov5", "yolov5s", pretrained=True).fuse().autoshape()6)  # for PIL/cv2/np inputs and NMS7# Get camera 0 is my usb cam and 2 is my internal one8cap = cv.VideoCapture(2)9def drawbboxes(img, bboxes, labels):10    """Draw bounding boxed onto the image11    :img: Image to draw the bboxes on12    :bboxes: Bounding boxes to draw which should be given as (top-left, bottom-right)13    :returns: An image with bounding boxes drawn14    """15    thickness = 516    color = (0, 255, 0)17    for bbox in bboxes:18        # top-left is x1, y1; bottom-right is x2,y219        x1, y1, x2, y2, prob, category = (20            int(bbox[0]),21            int(bbox[1]),22            int(bbox[2]),23            int(bbox[3]),24            round(bbox[4], 2),25            labels[int(bbox[5])],26        )27        img = cv.rectangle(img, (x1, y1), (x2, y2), color, thickness)28        img = cv.putText(29            img,30            f"Label: {category} ({prob})",31            (x1, y1 - 10),32            0,33            0.5,34            color,35            thickness // 3,36        )37    return img38while True:39    success, img = cap.read()40    # Predict41    img2 = cv.cvtColor(img, cv.COLOR_BGR2RGB)42    results = model(img2, size=640)  # includes NMS43    results.print()  # print results to screen44    # Visualize45    img = drawbboxes(img, results.pred[0].tolist(), results.names)46    cv.imshow("Camera", img)47    if cv.waitKey(1) and 0xFF == ord("q"):...

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