Test for working layout

app.py

@@ -1,16 +1,16 @@
 import gradio as gr
-import spaces
+# import spaces
 from huggingface_hub import hf_hub_download
 import os
 
 # make sure you have the following dependencies
-import torch
+# import torch
 import numpy as np
-from models.common import DetectMultiBackend
-from utils.general import non_max_suppression, scale_boxes
-from utils.torch_utils import select_device, smart_inference_mode
-from utils.augmentations import letterbox
-import PIL.Image
+# from models.common import DetectMultiBackend
+# from utils.general import non_max_suppression, scale_boxes
+# from utils.torch_utils import select_device, smart_inference_mode
+# from utils.augmentations import letterbox
+# import PIL.Image
 
 #@smart_inference_mode()
 @spaces.GPU
@@ -27,33 +27,35 @@ def yolov9_inference(img_path, model_id='YOLOv9-S_X_LOCO-converted.pt', img_size
     :return: A tuple containing the detections (boxes, scores, categories) and the results object for further actions like displaying.
     """
     
-    # Load the model
-    model_path = download_models(model_id)
+    return np.array(PIL.Image.open(img_path))
 
-    # Initialize
-    device = select_device('0')
-    model = DetectMultiBackend(model_path, device="0", fp16=False, data='data/coco.yaml')
-    stride, names, pt = model.stride, model.names, model.pt
+    # # Load the model
+    # model_path = download_models(model_id)
 
-    # Load image
-    img = np.array(PIL.Image.open(img_path))
-    img = letterbox(img0, img_size, stride=stride, auto=True)[0]
-    img = img[:, :, ::-1].transpose(2, 0, 1)
-    img = np.ascontiguousarray(img)
-    img = torch.from_numpy(img).to(device).float()
-    img /= 255.0
-    if img.ndimension() == 3:
-        img = img.unsqueeze(0)
+    # # Initialize
+    # device = select_device('0')
+    # model = DetectMultiBackend(model_path, device="0", fp16=False, data='data/coco.yaml')
+    # stride, names, pt = model.stride, model.names, model.pt
 
-    # Inference
-    results = model(img, augment=False, visualize=False)
+    # # Load image
+    # img = np.array(PIL.Image.open(img_path))
+    # img = letterbox(img0, img_size, stride=stride, auto=True)[0]
+    # img = img[:, :, ::-1].transpose(2, 0, 1)
+    # img = np.ascontiguousarray(img)
+    # img = torch.from_numpy(img).to(device).float()
+    # img /= 255.0
+    # if img.ndimension() == 3:
+    #     img = img.unsqueeze(0)
 
-    # Apply NMS
-    results = non_max_suppression(results[0][0], conf_thres, iou_thres, classes=None, max_det=1000)
+    # # Inference
+    # results = model(img, augment=False, visualize=False)
 
-    output = results.render()
+    # # Apply NMS
+    # results = non_max_suppression(results[0][0], conf_thres, iou_thres, classes=None, max_det=1000)
 
-    return output[0]
+    # output = results.render()
+
+    # return output[0]
 
 
 

requirements.txt

@@ -1,49 +1,6 @@
 # requirements
 # Usage: pip install -r requirements.txt
 
-# Base ------------------------------------------------------------------------
-gitpython
-ipython
-matplotlib>=3.2.2
-numpy>=1.18.5
-opencv-python>=4.1.1
-Pillow>=7.1.2, <10.0.0
-psutil
-PyYAML>=5.3.1
-requests>=2.23.0
-scipy>=1.4.1
-thop>=0.1.1
-torch>=1.7.0
-torchvision>=0.8.1
-tqdm>=4.64.0
-# protobuf<=3.20.1
-
-# Logging ---------------------------------------------------------------------
-tensorboard>=2.4.1
-# clearml>=1.2.0
-# comet
-
-# Plotting --------------------------------------------------------------------
-pandas>=1.1.4
-seaborn>=0.11.0
-
-# Export ----------------------------------------------------------------------
-# coremltools>=6.0
-# onnx>=1.9.0
-# onnx-simplifier>=0.4.1
-# nvidia-pyindex
-# nvidia-tensorrt
-# scikit-learn<=1.1.2
-# tensorflow>=2.4.1
-# tensorflowjs>=3.9.0
-# openvino-dev
-
-# Deploy ----------------------------------------------------------------------
-# tritonclient[all]~=2.24.0
-git+https://github.com/KoniHD/yolov9.git@main#egg=yolov9
 huggingface_hub
-
-# Extras ----------------------------------------------------------------------
-# mss
-albumentations>=1.0.3
-pycocotools>=2.0
+ultralytics
+numpy>=1.18.5