update packages and requirements

app.py

@@ -1,10 +1,269 @@
 
+# python gradio_demo/barc_demo_v3.py
+
+import numpy as np
+import os
+import glob
+import torch
+from torch.utils.data import DataLoader
+import torchvision
+from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
+import torchvision.transforms as T
+import cv2
+from matplotlib import pyplot as plt
+from PIL import Image
 
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
 
+import sys
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../', 'src'))
+from stacked_hourglass.datasets.imgcropslist import ImgCrops
+from combined_model.train_main_image_to_3d_withbreedrel import do_visual_epoch
+from combined_model.model_shape_v7 import ModelImageTo3d_withshape_withproj
+
+from configs.barc_cfg_defaults import get_cfg_global_updated
+
+
+
+def get_prediction(model, img_path_or_img, confidence=0.5):
+    """
+    see https://haochen23.github.io/2020/04/object-detection-faster-rcnn.html#.YsMCm4TP3-g
+    get_prediction
+        parameters:
+        - img_path - path of the input image
+        - confidence - threshold value for prediction score
+        method:
+        - Image is obtained from the image path
+        - the image is converted to image tensor using PyTorch's Transforms
+        - image is passed through the model to get the predictions
+        - class, box coordinates are obtained, but only prediction score > threshold
+            are chosen.
+        
+    """
+    if isinstance(img_path_or_img, str):
+        img = Image.open(img_path_or_img).convert('RGB')
+    else:
+        img = img_path_or_img
+    transform = T.Compose([T.ToTensor()])
+    img = transform(img)
+    pred = model([img])
+    # pred_class = [COCO_INSTANCE_CATEGORY_NAMES[i] for i in list(pred[0]['labels'].numpy())]
+    pred_class = list(pred[0]['labels'].numpy())
+    pred_boxes = [[(int(i[0]), int(i[1])), (int(i[2]), int(i[3]))] for i in list(pred[0]['boxes'].detach().numpy())]
+    pred_score = list(pred[0]['scores'].detach().numpy())
+    try:
+        pred_t = [pred_score.index(x) for x in pred_score if x>confidence][-1]
+        pred_boxes = pred_boxes[:pred_t+1]
+        pred_class = pred_class[:pred_t+1]
+        return pred_boxes, pred_class, pred_score
+    except:
+        print('no bounding box with a score that is high enough found! -> work on full image')
+        return None, None, None
+
+def detect_object(model, img_path_or_img, confidence=0.5, rect_th=2, text_size=0.5, text_th=1):
+    """
+    see https://haochen23.github.io/2020/04/object-detection-faster-rcnn.html#.YsMCm4TP3-g
+    object_detection_api
+        parameters:
+        - img_path_or_img - path of the input image
+        - confidence - threshold value for prediction score
+        - rect_th - thickness of bounding box
+        - text_size - size of the class label text
+        - text_th - thichness of the text
+        method:
+        - prediction is obtained from get_prediction method
+        - for each prediction, bounding box is drawn and text is written 
+            with opencv
+        - the final image is displayed
+    """
+    boxes, pred_cls, pred_scores = get_prediction(model, img_path_or_img, confidence)
+    if isinstance(img_path_or_img, str):
+        img = cv2.imread(img_path_or_img)
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    else:
+        img = img_path_or_img
+    is_first = True
+    bbox = None
+    if boxes is not None:
+        for i in range(len(boxes)):
+            cls = pred_cls[i]
+            if cls == 18 and bbox is None:
+                cv2.rectangle(img, boxes[i][0], boxes[i][1],color=(0, 255, 0), thickness=rect_th)
+                # cv2.putText(img, pred_cls[i], boxes[i][0], cv2.FONT_HERSHEY_SIMPLEX, text_size, (0,255,0),thickness=text_th)
+                cv2.putText(img, str(pred_scores[i]), boxes[i][0], cv2.FONT_HERSHEY_SIMPLEX, text_size, (0,255,0),thickness=text_th)
+                bbox = boxes[i]
+    return img, bbox
+
+
+
+def run_bbox_inference(input_image):
+    model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
+    model.eval()
+    out_path = os.path.join(cfg.paths.ROOT_OUT_PATH, 'gradio_examples', 'test2.png')
+    img, bbox = detect_object(model=model, img_path_or_img=input_image, confidence=0.5)
+    fig = plt.figure()   #  plt.figure(figsize=(20,30))
+    plt.imsave(out_path, img)
+    return img, bbox
+
+
+
+
+
+def run_barc_inference(input_image, bbox=None):
+
+    # load configs
+    cfg = get_cfg_global_updated()
+
+    model_file_complete = os.path.join(cfg.paths.ROOT_CHECKPOINT_PATH, 'barc_complete', 'model_best.pth.tar')  
+
+
+
+    # Select the hardware device to use for inference.
+    if torch.cuda.is_available() and cfg.device=='cuda':
+        device = torch.device('cuda', torch.cuda.current_device())
+        # torch.backends.cudnn.benchmark = True
+    else:
+        device = torch.device('cpu')
+
+    path_model_file_complete = os.path.join(cfg.paths.ROOT_CHECKPOINT_PATH, model_file_complete) 
+
+    # Disable gradient calculations.
+    torch.set_grad_enabled(False)
+
+    # prepare complete model
+    complete_model = ModelImageTo3d_withshape_withproj(
+        num_stage_comb=cfg.params.NUM_STAGE_COMB, num_stage_heads=cfg.params.NUM_STAGE_HEADS, \
+        num_stage_heads_pose=cfg.params.NUM_STAGE_HEADS_POSE, trans_sep=cfg.params.TRANS_SEP, \
+        arch=cfg.params.ARCH, n_joints=cfg.params.N_JOINTS, n_classes=cfg.params.N_CLASSES, \
+        n_keyp=cfg.params.N_KEYP, n_bones=cfg.params.N_BONES, n_betas=cfg.params.N_BETAS, n_betas_limbs=cfg.params.N_BETAS_LIMBS, \
+        n_breeds=cfg.params.N_BREEDS, n_z=cfg.params.N_Z, image_size=cfg.params.IMG_SIZE, \
+        silh_no_tail=cfg.params.SILH_NO_TAIL, thr_keyp_sc=cfg.params.KP_THRESHOLD, add_z_to_3d_input=cfg.params.ADD_Z_TO_3D_INPUT,
+        n_segbps=cfg.params.N_SEGBPS, add_segbps_to_3d_input=cfg.params.ADD_SEGBPS_TO_3D_INPUT, add_partseg=cfg.params.ADD_PARTSEG, n_partseg=cfg.params.N_PARTSEG, \
+        fix_flength=cfg.params.FIX_FLENGTH, structure_z_to_betas=cfg.params.STRUCTURE_Z_TO_B, structure_pose_net=cfg.params.STRUCTURE_POSE_NET,
+        nf_version=cfg.params.NF_VERSION) 
+
+    # load trained model
+    print(path_model_file_complete)
+    assert os.path.isfile(path_model_file_complete)
+    print('Loading model weights from file: {}'.format(path_model_file_complete))
+    checkpoint_complete = torch.load(path_model_file_complete)
+    state_dict_complete = checkpoint_complete['state_dict']
+    complete_model.load_state_dict(state_dict_complete, strict=False)        
+    complete_model = complete_model.to(device)
+
+    save_imgs_path = os.path.join(cfg.paths.ROOT_OUT_PATH, 'gradio_examples')
+    if not os.path.exists(save_imgs_path):
+        os.makedirs(save_imgs_path)
+
+    input_image_list = [input_image]
+    if bbox is not None:
+        input_bbox_list = [bbox]
+    else:
+        input_bbox_list = None
+    val_dataset = ImgCrops(image_list=input_image_list, bbox_list=input_bbox_list, dataset_mode='complete')
+    test_name_list = val_dataset.test_name_list
+    val_loader = DataLoader(val_dataset, batch_size=1, shuffle=False,
+                            num_workers=0, pin_memory=True, drop_last=False)   
+
+    # run visual evaluation
+    #   remark: take ACC_Joints and DATA_INFO from StanExt as this is the training dataset
+    all_results = do_visual_epoch(val_loader, complete_model, device,
+                        ImgCrops.DATA_INFO,
+                        weight_dict=None,
+                        acc_joints=ImgCrops.ACC_JOINTS,
+                        save_imgs_path=None, # save_imgs_path,
+                        metrics='all', 
+                        test_name_list=test_name_list,
+                        render_all=cfg.params.RENDER_ALL,
+                        pck_thresh=cfg.params.PCK_THRESH, 
+                        return_results=True)
+
+    mesh = all_results[0]['mesh_posed']
+    result_path = os.path.join(save_imgs_path, test_name_list[0] + '_z')
+
+    mesh.apply_transform([[-1, 0, 0, 0],
+                            [0, -1, 0, 0],
+                            [0, 0, 1, 1],
+                            [0, 0, 0, 1]])
+    mesh.export(file_obj=result_path + '.glb')
+    result_gltf = result_path + '.glb'
+    return [result_gltf, result_gltf]
+
+
+
+
+
+
+def run_complete_inference(input_image):
+
+    output_interm_image, output_interm_bbox = run_bbox_inference(input_image.copy())
+
+    print(output_interm_bbox)
+
+    # output_image = run_barc_inference(input_image)
+    output_image = run_barc_inference(input_image, output_interm_bbox)
+
+    return output_image
+
+
+
+
+# demo = gr.Interface(run_barc_inference, gr.Image(), "image")
+# demo = gr.Interface(run_complete_inference, gr.Image(), "image")
+
+
+
+# see: https://huggingface.co/spaces/radames/PIFu-Clothed-Human-Digitization/blob/main/PIFu/spaces.py
+
+description = '''
+# BARC
+
+#### Project Page
+* https://barc.is.tue.mpg.de/
+
+#### Description
+This is a demo for BARC. While BARC is trained on image crops, this demo uses a pretrained Faster-RCNN in order to get bounding boxes for the dogs. 
+To see your result you may have to wait a minute or two, please be paitient.
+
+<details>
+
+<summary>More</summary>
+
+#### Citation
+
+```
+@inproceedings{BARC:2022,
+    title = {BARC}: Learning to Regress {3D} Dog Shape from Images by Exploiting Breed Information,
+    author = {Rueegg, Nadine and Zuffi, Silvia and Schindler, Konrad and Black, Michael J.},
+    booktitle = {Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
+    year = {2022}
+}
+```
+
+</details>
+'''
+
+examples = sorted(glob.glob(os.path.join(os.path.dirname(__file__), '../', 'datasets', 'test_image_crops', '*.jpg')) + glob.glob(os.path.join(os.path.dirname(__file__), '../', 'datasets', 'test_image_crops', '*.png')))  
+
+
+demo = gr.Interface(
+    fn=run_complete_inference,
+    description=description,
+    # inputs=gr.Image(type="filepath", label="Input Image"),
+    inputs=gr.Image(label="Input Image"),
+    outputs=[
+        gr.Model3D(
+            clear_color=[0.0, 0.0, 0.0, 0.0],  label="3D Model"),
+        gr.File(label="Download 3D Model")
+    ],
+    examples=examples,
+    thumbnail="barc_thumbnail.png",
+    allow_flagging="never",
+    cache_examples=True
+)
+
+
+
+demo.launch(share=True)

packages.txt

@@ -0,0 +1,8 @@
+libgl1
+unzip
+ffmpeg 
+libsm6 
+libxext6
+libgl1-mesa-dri
+libegl1-mesa
+libgbm1 

requirements.txt

@@ -0,0 +1,15 @@
+torch==1.6.0
+torchvision==0.7.0
+pytorch3d==0.2.5
+kornia==0.4.0 
+matplotlib
+opencv-python
+trimesh
+scipy
+chumpy
+pymp
+importlib-resources
+pycocotools
+openpyxl
+dominate
+git+https://github.com/runa91/FrEIA.git