Upload hybridnets_test_videos.py

hybridnets_test_videos.py

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+import time
+import torch
+from torch.backends import cudnn
+from backbone import HybridNetsBackbone
+import cv2
+import numpy as np
+from glob import glob
+from utils.utils import letterbox, scale_coords, postprocess, BBoxTransform, ClipBoxes, restricted_float, boolean_string
+from utils.plot import STANDARD_COLORS, standard_to_bgr, get_index_label, plot_one_box
+import os
+from torchvision import transforms
+import argparse
+
+parser = argparse.ArgumentParser('HybridNets: End-to-End Perception Network - DatVu')
+parser.add_argument('-c', '--compound_coef', type=int, default=3, help='Coefficient of efficientnet backbone')
+parser.add_argument('--source', type=str, default='demo/video', help='The demo video folder')
+parser.add_argument('--output', type=str, default='demo_result', help='Output folder')
+parser.add_argument('-w', '--load_weights', type=str, default='weights/hybridnets.pth')
+parser.add_argument('--nms_thresh', type=restricted_float, default='0.25')
+parser.add_argument('--iou_thresh', type=restricted_float, default='0.3')
+parser.add_argument('--cuda', type=boolean_string, default=True)
+parser.add_argument('--float16', type=boolean_string, default=True, help="Use float16 for faster inference")
+args = parser.parse_args()
+
+compound_coef = args.compound_coef
+source = args.source
+if source.endswith("/"):
+    source = source[:-1]
+output = args.output
+if output.endswith("/"):
+    output = output[:-1]
+weight = args.load_weights
+video_src = glob(f'{source}/*.mp4')[0]
+os.makedirs(output, exist_ok=True)
+video_out = f'{output}/output.mp4'
+input_imgs = []
+shapes = []
+
+# replace this part with your project's anchor config
+anchor_ratios = [(0.62, 1.58), (1.0, 1.0), (1.58, 0.62)]
+anchor_scales = [2 ** 0, 2 ** 0.70, 2 ** 1.32]
+
+threshold = args.nms_thresh
+iou_threshold = args.iou_thresh
+
+use_cuda = args.cuda
+use_float16 = args.float16
+cudnn.fastest = True
+cudnn.benchmark = True
+
+obj_list = ['car']
+
+color_list = standard_to_bgr(STANDARD_COLORS)
+resized_shape = 640
+normalize = transforms.Normalize(
+    mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+)
+transform = transforms.Compose([
+    transforms.ToTensor(),
+    normalize,
+])
+# print(x.shape)
+
+model = HybridNetsBackbone(compound_coef=compound_coef, num_classes=len(obj_list),
+                           ratios=anchor_ratios, scales=anchor_scales, seg_classes=2)
+try:
+    model.load_state_dict(torch.load(weight, map_location='cuda' if use_cuda else 'cpu'))
+except:
+    model.load_state_dict(torch.load(weight, map_location='cuda' if use_cuda else 'cpu')['model'])
+model.requires_grad_(False)
+model.eval()
+
+if use_cuda:
+    model = model.cuda()
+if use_float16:
+    model = model.half()
+cap = cv2.VideoCapture(video_src)
+# Define the codec and create VideoWriter object
+fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+out_stream = cv2.VideoWriter(video_out, fourcc, 30.0,
+                             (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))))
+t1 = time.time()
+frame_count = 0
+while True:
+    ret, frame = cap.read()
+    if not ret:
+        break
+
+    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    h0, w0 = frame.shape[:2]  # orig hw
+    r = resized_shape / max(h0, w0)  # resize image to img_size
+    input_img = cv2.resize(frame, (int(w0 * r), int(h0 * r)), interpolation=cv2.INTER_AREA)
+    h, w = input_img.shape[:2]
+
+    (input_img, _, _), ratio, pad = letterbox((input_img, input_img.copy(), input_img.copy()), resized_shape, auto=True,
+                                              scaleup=False)
+
+    shapes = ((h0, w0), ((h / h0, w / w0), pad))
+
+    if use_cuda:
+        x = transform(input_img).cuda()
+    else:
+        x = transform(input_img)
+
+    x = x.to(torch.float32 if not use_float16 else torch.float16)
+    x.unsqueeze_(0)
+    with torch.no_grad():
+        features, regression, classification, anchors, seg = model(x)
+
+        seg = seg[:, :, 12:372, :]
+        da_seg_mask = torch.nn.functional.interpolate(seg, size=[h0, w0], mode='nearest')
+        _, da_seg_mask = torch.max(da_seg_mask, 1)
+        da_seg_mask_ = da_seg_mask[0].squeeze().cpu().numpy().round()
+
+        color_area = np.zeros((da_seg_mask_.shape[0], da_seg_mask_.shape[1], 3), dtype=np.uint8)
+        color_area[da_seg_mask_ == 1] = [0, 255, 0]
+        color_area[da_seg_mask_ == 2] = [0, 0, 255]
+        color_seg = color_area[..., ::-1]
+
+        # cv2.imwrite('seg_only_{}.jpg'.format(i), color_seg)
+
+        color_mask = np.mean(color_seg, 2)
+        frame[color_mask != 0] = frame[color_mask != 0] * 0.5 + color_seg[color_mask != 0] * 0.5
+        frame = frame.astype(np.uint8)
+        # cv2.imwrite('seg_{}.jpg'.format(i), ori_img)
+
+        regressBoxes = BBoxTransform()
+        clipBoxes = ClipBoxes()
+        out = postprocess(x,
+                          anchors, regression, classification,
+                          regressBoxes, clipBoxes,
+                          threshold, iou_threshold)
+        out = out[0]
+        out['rois'] = scale_coords(frame[:2], out['rois'], shapes[0], shapes[1])
+        for j in range(len(out['rois'])):
+            x1, y1, x2, y2 = out['rois'][j].astype(int)
+            obj = obj_list[out['class_ids'][j]]
+            score = float(out['scores'][j])
+            plot_one_box(frame, [x1, y1, x2, y2], label=obj, score=score,
+                         color=color_list[get_index_label(obj, obj_list)])
+        out_stream.write(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+        frame_count += 1
+
+t2 = time.time()
+print("frame: {}".format(frame_count))
+print("second: {}".format(t2-t1))
+print("fps: {}".format((t2-t1)/frame_count))
+
+cap.release()
+out_stream.release()