189 lines
7.0 KiB
Python
189 lines
7.0 KiB
Python
import torch
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import cv2
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import numpy as np
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import torchvision
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from pathlib import Path
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from yolo11_standalone import YOLO11E, YOLOPostProcessor, YOLOPostProcessorNumpy
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from mobile_clip_standalone import MobileCLIP
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DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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YOLO_WEIGHTS = "yoloe-11l-seg.pth"
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CLIP_WEIGHTS = "mobileclip_blt.ts"
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CLIP_SIZE = "blt"
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IMAGE_PATH = "1.jpg"
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CUSTOM_CLASSES = ["girl", "red balloon"]
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COLORS = np.random.uniform(0, 255, size=(len(CUSTOM_CLASSES), 3))
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def letterbox(im, new_shape=(640, 640), color=(114, 114, 114)):
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shape = im.shape[:2]
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if isinstance(new_shape, int): new_shape = (new_shape, new_shape)
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r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
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new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
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dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]
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dw, dh = dw / 2, dh / 2
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if shape[::-1] != new_unpad:
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im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
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top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
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left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
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im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)
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return im, r, (dw, dh)
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def xywh2xyxy(x):
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y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
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y[..., 0] = x[..., 0] - x[..., 2] / 2
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y[..., 1] = x[..., 1] - x[..., 3] / 2
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y[..., 2] = x[..., 0] + x[..., 2] / 2
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y[..., 3] = x[..., 1] + x[..., 3] / 2
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return y
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def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.7, max_det=300):
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prediction = prediction.transpose(1, 2)
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bs = prediction.shape[0]
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xc = prediction[..., 4:].max(-1)[0] > conf_thres
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output = [torch.zeros((0, 6), device=prediction.device)] * bs
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for xi, x in enumerate(prediction):
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x = x[xc[xi]]
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if not x.shape[0]: continue
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box = xywh2xyxy(x[:, :4])
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conf, j = x[:, 4:].max(1, keepdim=True)
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x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]
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n = x.shape[0]
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if not n: continue
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elif n > max_det: x = x[x[:, 4].argsort(descending=True)[:max_det]]
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c = x[:, 5:6] * 7680
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boxes, scores = x[:, :4] + c, x[:, 4]
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i = torchvision.ops.nms(boxes, scores, iou_thres)
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output[xi] = x[i]
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return output
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def non_max_suppression_numpy(prediction, conf_thres=0.25, iou_thres=0.45, max_det=300):
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bs = prediction.shape[0]
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output = [np.zeros((0, 6), dtype=np.float32)] * bs
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for xi, x in enumerate(prediction):
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bbox_xywh = x[:, :4]
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class_probs = x[:, 4:]
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class_ids = np.argmax(class_probs, axis=1)
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confidences = np.max(class_probs, axis=1)
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mask = confidences > conf_thres
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bbox_xywh = bbox_xywh[mask]
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confidences = confidences[mask]
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class_ids = class_ids[mask]
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if len(confidences) == 0:
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continue
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bbox_tlwh = np.copy(bbox_xywh)
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bbox_tlwh[:, 0] = bbox_xywh[:, 0] - bbox_xywh[:, 2] / 2
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bbox_tlwh[:, 1] = bbox_xywh[:, 1] - bbox_xywh[:, 3] / 2
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indices = cv2.dnn.NMSBoxes(
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bboxes=bbox_tlwh.tolist(),
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scores=confidences.tolist(),
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score_threshold=conf_thres,
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nms_threshold=iou_thres
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)
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if len(indices) > 0:
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indices = indices.flatten()
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if len(indices) > max_det:
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indices = indices[:max_det]
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final_boxes_xywh = bbox_xywh[indices]
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final_boxes_xyxy = xywh2xyxy(final_boxes_xywh)
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final_scores = confidences[indices]
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final_classes = class_ids[indices]
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out_tensor = np.concatenate([
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final_boxes_xyxy,
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final_scores[:, None],
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final_classes[:, None]
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], axis=1)
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output[xi] = out_tensor
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return output
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def main():
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print(f"Using device: {DEVICE}")
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print(f"Loading MobileCLIP from {CLIP_WEIGHTS}...")
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if not Path(CLIP_WEIGHTS).exists(): raise FileNotFoundError(CLIP_WEIGHTS)
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clip_model = MobileCLIP(checkpoint=CLIP_WEIGHTS, size=CLIP_SIZE, device=DEVICE)
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print(f"Encoding classes: {CUSTOM_CLASSES}")
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tokens = clip_model.tokenize([f"{c}" for c in CUSTOM_CLASSES])
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text_embeddings = clip_model.encode_text(tokens).unsqueeze(0)
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print(f"Loading YOLO11E from {YOLO_WEIGHTS}...")
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if not Path(YOLO_WEIGHTS).exists(): raise FileNotFoundError(YOLO_WEIGHTS)
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yolo_model = YOLO11E(nc=80, scale='l')
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yolo_model.load_weights(YOLO_WEIGHTS)
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yolo_model.to(DEVICE).eval()
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head = yolo_model.model[-1]
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post_processor = YOLOPostProcessor(head, use_segmentation=True)
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post_processor.to(DEVICE).eval()
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with torch.no_grad():
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text_pe = head.get_tpe(text_embeddings)
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yolo_model.set_classes(CUSTOM_CLASSES, text_pe)
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img0 = cv2.imread(IMAGE_PATH)
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assert img0 is not None, f"Image Not Found {IMAGE_PATH}"
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img, ratio, (dw, dh) = letterbox(img0, new_shape=(640, 640))
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img = np.ascontiguousarray(img[:, :, ::-1].transpose(2, 0, 1))
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img_tensor = torch.from_numpy(img).to(DEVICE).float() / 255.0
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if img_tensor.ndim == 3: img_tensor = img_tensor.unsqueeze(0)
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print("Running inference...")
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with torch.no_grad():
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raw_outputs = yolo_model(img_tensor)
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# decoded_box, mc, p = post_processor(raw_outputs)
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# pred = non_max_suppression(decoded_box, conf_thres=0.25, iou_thres=0.7)
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# det = pred[0]
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feat_maps, mc, p = raw_outputs
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feat_maps_numpy = [f.detach().cpu().numpy() for f in feat_maps]
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mc_numpy = mc.detach().cpu().numpy()
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p_numpy = p.detach().cpu().numpy()
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raw_outputs_numpy = (feat_maps_numpy, mc_numpy, p_numpy)
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post_processor_numpy = YOLOPostProcessorNumpy(strides=[8, 16, 32], reg_max=16, use_segmentation=True)
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decoded_box_numpy, mc_numpy_out, p_numpy_out = post_processor_numpy(raw_outputs_numpy)
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pred_results = non_max_suppression_numpy(decoded_box_numpy, conf_thres=0.25, iou_thres=0.7)
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det = pred_results[0]
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if len(det):
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det[:, [0, 2]] -= dw
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det[:, [1, 3]] -= dh
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det[:, :4] /= ratio
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# det[:, [0, 2]].clamp_(0, img0.shape[1])
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# det[:, [1, 3]].clamp_(0, img0.shape[0])
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det[:, 0] = np.clip(det[:, 0], 0, img0.shape[1])
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det[:, 1] = np.clip(det[:, 1], 0, img0.shape[0])
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det[:, 2] = np.clip(det[:, 2], 0, img0.shape[1])
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det[:, 3] = np.clip(det[:, 3], 0, img0.shape[0])
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print(f"Detected {len(det)} objects:")
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for *xyxy, conf, cls in det:
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c = int(cls)
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label = f'{CUSTOM_CLASSES[c]} {conf:.2f}'
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p1, p2 = (int(xyxy[0]), int(xyxy[1])), (int(xyxy[2]), int(xyxy[3]))
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color = COLORS[c % len(COLORS)]
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cv2.rectangle(img0, p1, p2, color, 2, cv2.LINE_AA)
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cv2.putText(img0, label, (p1[0], p1[1] - 5), 0, 0.5, color, 1, cv2.LINE_AA)
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print(f" - {label}")
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else:
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print("No objects detected.")
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cv2.imwrite("result_yoloe.jpg", img0)
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print("Result saved.")
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if __name__ == "__main__":
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main() |