koartifact / tracycle_ai Goto Github PK

userId = request.form.get('userId')
if request.method == 'POST':
    areaId = int(request.form.get('areaId'))
    if "mainFile" not in request.files:
        return redirect(request.url)
    file = request.files["mainFile"]

    img_bytes = file.read()
    img = Image.open(io.BytesIO(img_bytes))
    results = model(img, size=640)
    
    results.render()  
    for img in results.imgs:
        img_base64 = Image.fromarray(img)
        img_base64.save("static/"+userId+".jpg", format="JPEG")

    data = results.pandas().xyxy[0].to_json(orient="records")

    info_list = list()   
    list_data = json.loads(data)
    class_id=set()
    for x in list_data:
        class_id.add(x['class'])
    class_id = list(class_id)

    if not class_id:
        print("Can't find object")
        infos = "Can't find object"

    else:
        for c in class_id:
            categoryId = c
            infos = get_result('tracycle', areaId, categoryId)
            for info in infos:
                info_list.append(info)

return jsonify(info_list)

    parser = argparse.ArgumentParser(
        description="Flask app exposing yolov5 models")
    parser.add_argument("--port", default=8085, type=int, help="port number")
    args = parser.parse_args()

    model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt').autoshape()  # force_reload = recache latest code
    model.eval()
    # debug=True causes Restarting with stat
    app.run(host="0.0.0.0", port=args.port)

!git clone https://github.com/ultralytics/yolov5
!cd yolov5;pip install -qr requirements.txt

import os, sys 
from google.colab import drive 

drive.mount('/content/gdrive')

!wget -O /content/gdrive/MyDrive/Project_Data/Tracycle_Train.yaml https://raw.github.com/Koartifact/Tracycle_Ai/master/data/util/Tracycle_Train.yaml

!cd /content/yolov5; python train.py --img 600 --batch 8 --epochs 130 --data /content/gdrive/MyDrive/Project_Data/Tracycle_Train.yaml --weights yolov5m.pt \
                                     --project=/mydrive/Project_Data --name summary --exist-ok 

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license

# Parameters
nc: 80  # number of classes
depth_multiple: 0.67  # model depth multiple
width_multiple: 0.75  # layer channel multiple
anchors:
  - [10,13, 16,30, 33,23]  # P3/8
  - [30,61, 62,45, 59,119]  # P4/16
  - [116,90, 156,198, 373,326]  # P5/32

# YOLOv5 backbone
backbone:
  # [from, number, module, args]
  [[-1, 1, Focus, [64, 3]],  # 0-P1/2
   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
   [-1, 3, C3, [128]],
   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
   [-1, 9, C3, [256]],
   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
   [-1, 9, C3, [512]],
   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
   [-1, 1, SPP, [1024, [5, 9, 13]]],
   [-1, 3, C3, [1024, False]],  # 9
  ]

# YOLOv5 head
head:
  [[-1, 1, Conv, [512, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
   [-1, 3, C3, [512, False]],  # 13

   [-1, 1, Conv, [256, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
   [-1, 3, C3, [256, False]],  # 17 (P3/8-small)

   [-1, 1, Conv, [256, 3, 2]],
   [[-1, 14], 1, Concat, [1]],  # cat head P4
   [-1, 3, C3, [512, False]],  # 20 (P4/16-medium)

   [-1, 1, Conv, [512, 3, 2]],
   [[-1, 10], 1, Concat, [1]],  # cat head P5
   [-1, 3, C3, [1024, False]],  # 23 (P5/32-large)

   [[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]