re add missing files

monstereo/network/pifpaf.py

@@ -0,0 +1,102 @@
+
+import glob
+
+import numpy as np
+import torchvision
+import torch
+from PIL import Image, ImageFile
+from openpifpaf.network import nets
+from openpifpaf import decoder
+
+from .process import image_transform
+
+
+class ImageList(torch.utils.data.Dataset):
+    """It defines transformations to apply to images and outputs of the dataloader"""
+    def __init__(self, image_paths, scale):
+        self.image_paths = image_paths
+        self.image_paths.sort()
+        self.scale = scale
+
+    def __getitem__(self, index):
+        image_path = self.image_paths[index]
+        ImageFile.LOAD_TRUNCATED_IMAGES = True
+        with open(image_path, 'rb') as f:
+            image = Image.open(f).convert('RGB')
+
+        if self.scale > 1.01 or self.scale < 0.99:
+            image = torchvision.transforms.functional.resize(image,
+                                                             (round(self.scale * image.size[1]),
+                                                              round(self.scale * image.size[0])),
+                                                             interpolation=Image.BICUBIC)
+        # PIL images are not iterables
+        original_image = torchvision.transforms.functional.to_tensor(image)  # 0-255 --> 0-1
+        image = image_transform(image)
+
+        return image_path, original_image, image
+
+    def __len__(self):
+        return len(self.image_paths)
+
+
+def factory_from_args(args):
+
+    # Merge the model_pifpaf argument
+    if not args.checkpoint:
+        args.checkpoint = 'resnet152'  # Default model Resnet 152
+    # glob
+    if args.glob:
+        args.images += glob.glob(args.glob)
+    if not args.images:
+        raise Exception("no image files given")
+
+    # add args.device
+    args.device = torch.device('cpu')
+    args.pin_memory = False
+    if torch.cuda.is_available():
+        args.device = torch.device('cuda')
+        args.pin_memory = True
+
+    # Add num_workers
+    args.loader_workers = 8
+
+    # Add visualization defaults
+    args.figure_width = 10
+    args.dpi_factor = 1.0
+
+    return args
+
+
+class PifPaf:
+    def __init__(self, args):
+        """Instanciate the mdodel"""
+        factory_from_args(args)
+        model_pifpaf, _ = nets.factory_from_args(args)
+        model_pifpaf = model_pifpaf.to(args.device)
+        self.processor = decoder.factory_from_args(args, model_pifpaf)
+        self.keypoints_whole = []
+
+        # Scale the keypoints to the original image size for printing (if not webcam)
+        self.scale_np = np.array([args.scale, args.scale, 1] * 17).reshape(17, 3)
+
+    def fields(self, processed_images):
+        """Encoder for pif and paf fields"""
+        fields_batch = self.processor.fields(processed_images)
+        return fields_batch
+
+    def forward(self, image, processed_image_cpu, fields):
+        """Decoder, from pif and paf fields to keypoints"""
+        self.processor.set_cpu_image(image, processed_image_cpu)
+        keypoint_sets, scores = self.processor.keypoint_sets(fields)
+
+        if keypoint_sets.size > 0:
+            self.keypoints_whole.append(np.around((keypoint_sets / self.scale_np), 1)
+                                        .reshape(keypoint_sets.shape[0], -1).tolist())
+
+        pifpaf_out = [
+            {'keypoints': np.around(kps / self.scale_np, 1).reshape(-1).tolist(),
+             'bbox': [np.min(kps[:, 0]) / self.scale_np[0, 0], np.min(kps[:, 1]) / self.scale_np[0, 0],
+                      np.max(kps[:, 0]) / self.scale_np[0, 0], np.max(kps[:, 1]) / self.scale_np[0, 0]]}
+            for kps in keypoint_sets
+        ]
+        return keypoint_sets, scores, pifpaf_out

monstereo/predict.py

@@ -0,0 +1,146 @@
+
+# pylint: disable=too-many-statements, too-many-branches, undefined-loop-variable
+
+import os
+import json
+from collections import defaultdict
+
+
+import torch
+from PIL import Image
+
+from .visuals.printer import Printer
+from .visuals.pifpaf_show import KeypointPainter, image_canvas
+from .network import PifPaf, ImageList, Loco
+from .network.process import factory_for_gt, preprocess_pifpaf
+
+
+def predict(args):
+
+    cnt = 0
+
+    # Load Models
+    pifpaf = PifPaf(args)
+    assert args.mode in ('mono', 'stereo', 'pifpaf')
+
+    if 'mono' in args.mode:
+        monoloco = Loco(model=args.model, net='monoloco_pp',
+                        device=args.device, n_dropout=args.n_dropout, p_dropout=args.dropout)
+
+    if 'stereo' in args.mode:
+        monstereo = Loco(model=args.model, net='monstereo',
+                         device=args.device, n_dropout=args.n_dropout, p_dropout=args.dropout)
+
+    # data
+    data = ImageList(args.images, scale=args.scale)
+    if args.mode == 'stereo':
+        assert len(data.image_paths) % 2 == 0, "Odd number of images in a stereo setting"
+        bs = 2
+    else:
+        bs = 1
+    data_loader = torch.utils.data.DataLoader(
+        data, batch_size=bs, shuffle=False,
+        pin_memory=args.pin_memory, num_workers=args.loader_workers)
+
+    for idx, (image_paths, image_tensors, processed_images_cpu) in enumerate(data_loader):
+        images = image_tensors.permute(0, 2, 3, 1)
+
+        processed_images = processed_images_cpu.to(args.device, non_blocking=True)
+        fields_batch = pifpaf.fields(processed_images)
+
+        # unbatch stereo pair
+        for ii, (image_path, image, processed_image_cpu, fields) in enumerate(zip(
+                image_paths, images, processed_images_cpu, fields_batch)):
+
+            if args.output_directory is None:
+                splits = os.path.split(image_paths[0])
+                output_path = os.path.join(splits[0], 'out_' + splits[1])
+            else:
+                file_name = os.path.basename(image_paths[0])
+                output_path = os.path.join(args.output_directory, 'out_' + file_name)
+            print('image', idx, image_path, output_path)
+            keypoint_sets, scores, pifpaf_out = pifpaf.forward(image, processed_image_cpu, fields)
+
+            if ii == 0:
+                pifpaf_outputs = [keypoint_sets, scores, pifpaf_out]  # keypoints_sets and scores for pifpaf printing
+                images_outputs = [image]  # List of 1 or 2 elements with pifpaf tensor and monoloco original image
+                pifpaf_outs = {'left': pifpaf_out}
+                image_path_l = image_path
+            else:
+                pifpaf_outs['right'] = pifpaf_out
+
+        if args.mode in ('stereo', 'mono'):
+            # Extract calibration matrix and ground truth file if present
+            with open(image_path_l, 'rb') as f:
+                pil_image = Image.open(f).convert('RGB')
+                images_outputs.append(pil_image)
+
+            im_name = os.path.basename(image_path_l)
+            im_size = (float(image.size()[1] / args.scale), float(image.size()[0] / args.scale))  # Original
+            kk, dic_gt = factory_for_gt(im_size, name=im_name, path_gt=args.path_gt)
+
+            # Preprocess pifpaf outputs and run monoloco
+            boxes, keypoints = preprocess_pifpaf(pifpaf_outs['left'], im_size, enlarge_boxes=False)
+
+            if args.mode == 'mono':
+                print("Prediction with MonoLoco++")
+                dic_out = monoloco.forward(keypoints, kk)
+                dic_out = monoloco.post_process(dic_out, boxes, keypoints, kk, dic_gt)
+
+            else:
+                print("Prediction with MonStereo")
+                boxes_r, keypoints_r = preprocess_pifpaf(pifpaf_outs['right'], im_size)
+                dic_out = monstereo.forward(keypoints, kk, keypoints_r=keypoints_r)
+                dic_out = monstereo.post_process(dic_out, boxes, keypoints, kk, dic_gt)
+
+        else:
+            dic_out = defaultdict(list)
+            kk = None
+
+        factory_outputs(args, images_outputs, output_path, pifpaf_outputs, dic_out=dic_out, kk=kk)
+        print('Image {}\n'.format(cnt) + '-' * 120)
+        cnt += 1
+
+
+def factory_outputs(args, images_outputs, output_path, pifpaf_outputs, dic_out=None, kk=None):
+    """Output json files or images according to the choice"""
+
+    # Save json file
+    if args.mode == 'pifpaf':
+        keypoint_sets, scores, pifpaf_out = pifpaf_outputs[:]
+
+        # Visualizer
+        keypoint_painter = KeypointPainter(show_box=False)
+        skeleton_painter = KeypointPainter(show_box=False, color_connections=True, markersize=1, linewidth=4)
+
+        if 'json' in args.output_types and keypoint_sets.size > 0:
+            with open(output_path + '.pifpaf.json', 'w') as f:
+                json.dump(pifpaf_out, f)
+
+        if 'keypoints' in args.output_types:
+            with image_canvas(images_outputs[0],
+                              output_path + '.keypoints.png',
+                              show=args.show,
+                              fig_width=args.figure_width,
+                              dpi_factor=args.dpi_factor) as ax:
+                keypoint_painter.keypoints(ax, keypoint_sets)
+
+        if 'skeleton' in args.output_types:
+            with image_canvas(images_outputs[0],
+                              output_path + '.skeleton.png',
+                              show=args.show,
+                              fig_width=args.figure_width,
+                              dpi_factor=args.dpi_factor) as ax:
+                skeleton_painter.keypoints(ax, keypoint_sets, scores=scores)
+
+    else:
+        if any((xx in args.output_types for xx in ['front', 'bird', 'multi'])):
+            print(output_path)
+            if dic_out['boxes']:  # Only print in case of detections
+                printer = Printer(images_outputs[1], output_path, kk, args)
+                figures, axes = printer.factory_axes()
+                printer.draw(figures, axes, dic_out, images_outputs[1])
+
+        if 'json' in args.output_types:
+            with open(os.path.join(output_path + '.monoloco.json'), 'w') as ff:
+                json.dump(dic_out, ff)