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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "8d41befc-7e65-4110-9118-35dce6e6ab0c",
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"metadata": {},
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"outputs": [],
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"source": [
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"from openvino.runtime import Core\n",
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"import openvino.runtime as ov\n",
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"import cv2 as cv\n",
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"import numpy as np\n",
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"from PIL import Image\n",
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"from ultralytics.yolo.utils import ops\n",
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"import torch\n",
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"from ultralytics.yolo.utils.plotting import colors"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "cd01038a-fe1a-4b47-ad49-b0641afdaee5",
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"metadata": {},
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"outputs": [],
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"source": [
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"def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=False, scale_fill=False, scaleup=False, stride=32):\n",
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" \"\"\"\n",
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" Resize image and padding for detection. Takes image as input,\n",
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" resizes image to fit into new shape with saving original aspect ratio and pads it to meet stride-multiple constraints\n",
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"\n",
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" Parameters:\n",
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" img (np.ndarray): image for preprocessing\n",
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" new_shape (Tuple(int, int)): image size after preprocessing in format [height, width]\n",
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" color (Tuple(int, int, int)): color for filling padded area\n",
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" auto (bool): use dynamic input size, only padding for stride constrins applied\n",
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" scale_fill (bool): scale image to fill new_shape\n",
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" scaleup (bool): allow scale image if it is lower then desired input size, can affect model accuracy\n",
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" stride (int): input padding stride\n",
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" Returns:\n",
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" img (np.ndarray): image after preprocessing\n",
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" ratio (Tuple(float, float)): hight and width scaling ratio\n",
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" padding_size (Tuple(int, int)): height and width padding size\n",
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"\n",
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"\n",
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" \"\"\"\n",
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" # Resize and pad image while meeting stride-multiple constraints\n",
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" shape = img.shape[:2] # current shape [height, width]\n",
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" if isinstance(new_shape, int):\n",
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" new_shape = (new_shape, new_shape)\n",
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"\n",
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" # Scale ratio (new / old)\n",
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" r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])\n",
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" if not scaleup: # only scale down, do not scale up (for better test mAP)\n",
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" r = min(r, 1.0)\n",
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"\n",
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" # Compute padding\n",
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" ratio = r, r # width, height ratios\n",
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" new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))\n",
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" dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding\n",
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" if auto: # minimum rectangle\n",
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" dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding\n",
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" elif scale_fill: # stretch\n",
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" dw, dh = 0.0, 0.0\n",
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" new_unpad = (new_shape[1], new_shape[0])\n",
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" ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios\n",
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"\n",
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" dw /= 2 # divide padding into 2 sides\n",
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" dh /= 2\n",
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"\n",
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" if shape[::-1] != new_unpad: # resize\n",
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" img = cv.resize(img, new_unpad, interpolation=cv.INTER_LINEAR)\n",
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" top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))\n",
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" left, right = int(round(dw - 0.1)), int(round(dw + 0.1))\n",
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" img = cv.copyMakeBorder(img, top, bottom, left, right, cv.BORDER_CONSTANT, value=color) # add border\n",
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" return img, ratio, (dw, dh)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "5d01c15e-7dcc-4cec-87b0-a338e41051e4",
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"metadata": {},
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"outputs": [],
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"source": [
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"def preprocess_image(img0: np.ndarray):\n",
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" \"\"\"\n",
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" Preprocess image according to YOLOv8 input requirements.\n",
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" Takes image in np.array format, resizes it to specific size using letterbox resize and changes data layout from HWC to CHW.\n",
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"\n",
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" Parameters:\n",
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" img0 (np.ndarray): image for preprocessing\n",
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" Returns:\n",
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" img (np.ndarray): image after preprocessing\n",
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" \"\"\"\n",
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" # resize\n",
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" img = letterbox(img0)[0]\n",
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"\n",
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" # Convert HWC to CHW\n",
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" img = img.transpose(2, 0, 1)\n",
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" img = np.ascontiguousarray(img)\n",
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" return img"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "5d99be3c-8c3d-4c4c-b82e-17b6724d2258",
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"metadata": {},
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"outputs": [],
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"source": [
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"def image_to_tensor(image:np.ndarray):\n",
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" \"\"\"\n",
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" Preprocess image according to YOLOv8 input requirements.\n",
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" Takes image in np.array format, resizes it to specific size using letterbox resize and changes data layout from HWC to CHW.\n",
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"\n",
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" Parameters:\n",
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" img (np.ndarray): image for preprocessing\n",
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" Returns:\n",
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" input_tensor (np.ndarray): input tensor in NCHW format with float32 values in [0, 1] range\n",
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" \"\"\"\n",
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" input_tensor = image.astype(np.float32) # uint8 to fp32\n",
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" input_tensor /= 255.0 # 0 - 255 to 0.0 - 1.0\n",
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"\n",
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" # add batch dimension\n",
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" if input_tensor.ndim == 3:\n",
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" input_tensor = np.expand_dims(input_tensor, 0)\n",
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" return input_tensor"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "c1867e4e-1b99-431a-9d07-0389ae47d6db",
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"metadata": {},
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"outputs": [],
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"source": [
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"def postprocess(\n",
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" pred_boxes,\n",
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" input_hw,\n",
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" orig_img,\n",
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" min_conf_threshold=0.25,\n",
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" nms_iou_threshold=0.7,\n",
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" agnosting_nms=False,\n",
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" max_detections=300,\n",
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"):\n",
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" \"\"\"\n",
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" YOLOv8 model postprocessing function. Applied non maximum supression algorithm to detections and rescale boxes to original image size\n",
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" Parameters:\n",
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" pred_boxes (np.ndarray): model output prediction boxes\n",
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" input_hw (np.ndarray): preprocessed image\n",
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" orig_image (np.ndarray): image before preprocessing\n",
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" min_conf_threshold (float, *optional*, 0.25): minimal accepted confidence for object filtering\n",
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" nms_iou_threshold (float, *optional*, 0.45): minimal overlap score for removing objects duplicates in NMS\n",
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" agnostic_nms (bool, *optiona*, False): apply class agnostinc NMS approach or not\n",
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" max_detections (int, *optional*, 300): maximum detections after NMS\n",
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" Returns:\n",
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" pred (List[Dict[str, np.ndarray]]): list of dictionary with det - detected boxes in format [x1, y1, x2, y2, score, label]\n",
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" \"\"\"\n",
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" nms_kwargs = {\"agnostic\": agnosting_nms, \"max_det\":max_detections}\n",
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" preds = ops.non_max_suppression(\n",
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" torch.from_numpy(pred_boxes),\n",
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" min_conf_threshold,\n",
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" nms_iou_threshold,\n",
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" nc=3,\n",
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" **nms_kwargs\n",
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" )\n",
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"\n",
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" results = []\n",
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" for i, pred in enumerate(preds):\n",
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" shape = orig_img[i].shape if isinstance(orig_img, list) else orig_img.shape\n",
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" if not len(pred):\n",
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" results.append({\"det\": [], \"segment\": []})\n",
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" continue\n",
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" pred[:, :4] = ops.scale_boxes(input_hw, pred[:, :4], shape).round()\n",
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" results.append({\"det\": pred})\n",
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"\n",
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" return results"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "1940d393-1e89-46cd-9f12-b965487e1874",
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"metadata": {},
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"outputs": [],
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"source": [
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"def draw_results(results, source_image, label_map):\n",
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" \"\"\"\n",
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" Helper function for drawing bounding boxes on image\n",
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" Parameters:\n",
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" image_res (np.ndarray): detection predictions in format [x1, y1, x2, y2, score, label_id]\n",
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" source_image (np.ndarray): input image for drawing\n",
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" label_map; (Dict[int, str]): label_id to class name mapping\n",
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" Returns:\n",
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" Image with boxes\n",
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" \"\"\"\n",
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" boxes = results[\"det\"]\n",
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" for idx, (*xyxy, conf, lbl) in enumerate(boxes):\n",
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" label = f'{label_map[int(lbl)]} {conf:.2f}'\n",
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" source_image = plot_one_box(xyxy, source_image, label=label, color=colors(int(lbl)), line_thickness=1)\n",
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" return source_image"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "c599c49c-52af-4d0e-bad7-20c25fa2c851",
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"metadata": {},
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"outputs": [],
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"source": [
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"def plot_one_box(box, img,\n",
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" color,\n",
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" label, \n",
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" line_thickness=5):\n",
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" \"\"\"\n",
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" Helper function for drawing single bounding box on image\n",
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" Parameters:\n",
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" x (np.ndarray): bounding box coordinates in format [x1, y1, x2, y2]\n",
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" img (no.ndarray): input image\n",
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" color (Tuple[int, int, int], *optional*, None): color in BGR format for drawing box, if not specified will be selected randomly\n",
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" label (str, *optonal*, None): box label string, if not provided will not be provided as drowing result\n",
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" line_thickness (int, *optional*, 5): thickness for box drawing lines\n",
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" \"\"\"\n",
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" # Plots one bounding box on image img\n",
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" tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness\n",
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" color = color or [random.randint(0, 255) for _ in range(3)]\n",
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" c1, c2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))\n",
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" cv.rectangle(img, c1, c2, color, thickness=tl, lineType=cv.LINE_AA)\n",
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" if label:\n",
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" tf = max(tl - 1, 1) # font thickness\n",
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" t_size = cv.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]\n",
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" c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3\n",
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" cv.rectangle(img, c1, c2, color, -1, cv.LINE_AA) # filled\n",
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" cv.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv.LINE_AA)\n",
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"\n",
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" return img"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "ed0b7672-e2d1-47e6-8de8-07ffbb793a53",
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"metadata": {},
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"outputs": [],
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"source": [
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"def read_label(label_path):\n",
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" with open(label_path, 'r') as f:\n",
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" labels = f.read().split()\n",
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" return labels"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "531a9cbd-3613-4c41-b3f2-be900a5de3d3",
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"metadata": {},
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"outputs": [],
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"source": [
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"label_path = \"labels.txt\"\n",
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"yoloe_model_path = \"yolov9c/best.xml\"\n",
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"label_map = read_label(label_path)\n",
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"core = ov.Core()\n",
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"compiled_model = core.compile_model(yoloe_model_path, \"CPU\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "321e9911-30a5-44dd-810b-4b41ed5154b8",
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"metadata": {},
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"source": [
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"label_path = \"labels.txt\"\n",
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"yoloe_model_path = \"yolov9c/best.xml\"\n",
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"label_map = read_label(label_path)\n",
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"core = ov.Core()\n",
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"compiled_model = core.compile_model(yoloe_model_path, \"GPU\")\n",
|
|
|
|
|
"cap = cv.VideoCapture(0)\n",
|
|
|
|
|
"while cap.isOpened():\n",
|
|
|
|
|
" ret, frame = cap.read()\n",
|
|
|
|
|
" frame = cv.flip(frame, 180)\n",
|
|
|
|
|
" cv.namedWindow(\"MaskDetection\", 0) # 0可调大小，注意：窗口名必须imshow里面的一窗口名一直\n",
|
|
|
|
|
" cv.resizeWindow(\"MaskDetection\", 640, 480) # 设置长和宽\n",
|
|
|
|
|
" preprocessed_image = preprocess_image(frame)\n",
|
|
|
|
|
" input_tensor = image_to_tensor(preprocessed_image)\n",
|
|
|
|
|
" result = compiled_model(input_tensor)\n",
|
|
|
|
|
" detections = postprocess(pred_boxes=result[compiled_model.output(0)], input_hw=input_tensor.shape[2:], orig_img=frame)[0]\n",
|
|
|
|
|
" image_with_boxes = draw_results(detections, frame, label_map)\n",
|
|
|
|
|
" cv.imshow('MaskDetection', image_with_boxes)\n",
|
|
|
|
|
" key = cv.waitKey(1)\n",
|
|
|
|
|
" if key == 27: #esc退出\n",
|
|
|
|
|
" break\n",
|
|
|
|
|
"cap.release()\n",
|
|
|
|
|
"cv.destroyAllWindows()"
|
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|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
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|
|
|
"execution_count": 10,
|
|
|
|
|
"id": "7e2a8aab-cf82-4bf8-80d3-0cded72f9493",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"ename": "RuntimeError",
|
|
|
|
|
"evalue": "Exception from src\\inference\\src\\cpp\\infer_request.cpp:66:\nException from src\\plugins\\intel_cpu\\src\\infer_request.cpp:378:\nParameterMismatch: Failed to set tensor for input with precision: u8, since the model input tensor precision is: f32\n\n",
|
|
|
|
|
"output_type": "error",
|
|
|
|
|
"traceback": [
|
|
|
|
|
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
|
|
|
|
"\u001b[1;31mRuntimeError\u001b[0m Traceback (most recent call last)",
|
|
|
|
|
"Cell \u001b[1;32mIn[10], line 9\u001b[0m\n\u001b[0;32m 7\u001b[0m input_tensor \u001b[38;5;241m=\u001b[39m compiled_model\u001b[38;5;241m.\u001b[39minput(\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m 8\u001b[0m input_tensor\u001b[38;5;241m.\u001b[39mprecision \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFP32\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m----> 9\u001b[0m \u001b[43mcurr_request\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_tensor\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_tensor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mov\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTensor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcurr_frame\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 10\u001b[0m curr_request\u001b[38;5;241m.\u001b[39mstart_async()\n\u001b[0;32m 11\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m cap\u001b[38;5;241m.\u001b[39misOpened():\n",
|
|
|
|
|
"\u001b[1;31mRuntimeError\u001b[0m: Exception from src\\inference\\src\\cpp\\infer_request.cpp:66:\nException from src\\plugins\\intel_cpu\\src\\infer_request.cpp:378:\nParameterMismatch: Failed to set tensor for input with precision: u8, since the model input tensor precision is: f32\n\n"
|
|
|
|
|
]
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"cap = cv.VideoCapture(0)\n",
|
|
|
|
|
"ret, frame = cap.read()\n",
|
|
|
|
|
"curr_frame = preprocess_image(frame)\n",
|
|
|
|
|
"curr_fram = image_to_tensor(curr_frame)\n",
|
|
|
|
|
"curr_request = compiled_model.create_infer_request()\n",
|
|
|
|
|
"next_request = compiled_model.create_infer_request()\n",
|
|
|
|
|
"input_tensor = compiled_model.input(0)\n",
|
|
|
|
|
"input_tensor.precision = \"FP32\"\n",
|
|
|
|
|
"curr_request.set_tensor(input_tensor, ov.Tensor(curr_frame))\n",
|
|
|
|
|
"curr_request.start_async()\n",
|
|
|
|
|
"while cap.isOpened():\n",
|
|
|
|
|
" ret, next_frame = cap.read()\n",
|
|
|
|
|
" next_frame = cv.flip(next_frame, 180)\n",
|
|
|
|
|
" cv.namedWindow(\"MaskDetection\", 0) # 0可调大小，注意：窗口名必须imshow里面的一窗口名一直\n",
|
|
|
|
|
" cv.resizeWindow(\"MaskDetection\", 640, 480) # 设置长和宽\n",
|
|
|
|
|
" in_frame = preprocess_image(next_frame)\n",
|
|
|
|
|
" in_frame = image_to_tensor(in_frame)\n",
|
|
|
|
|
" input_tensor = compiled_model.input(0)\n",
|
|
|
|
|
" input_tensor.precision = \"FP32\"\n",
|
|
|
|
|
" next_request.set_tensor(input_tensor, ov.Tensor(in_frame))\n",
|
|
|
|
|
" next_request.start_async()\n",
|
|
|
|
|
" if curr_request.wait_for(-1) == 1:\n",
|
|
|
|
|
" result = curr_request.get_output_tensor(0).data\n",
|
|
|
|
|
" detections = postprocess(pred_boxes=result[compiled_model.output(0)], input_hw=in_frame.shape[2:], orig_img=next_frame)[0]\n",
|
|
|
|
|
" image_with_boxes = draw_results(detections, next_frame, label_map)\n",
|
|
|
|
|
" cv.imshow('MaskDetection', image_with_boxes)\n",
|
|
|
|
|
" frame = next_frame\n",
|
|
|
|
|
" curr_request, next_request = next_request, curr_request\n",
|
|
|
|
|
" key = cv.waitKey(1)\n",
|
|
|
|
|
" if key == 27: #esc退出\n",
|
|
|
|
|
" break\n",
|
|
|
|
|
"cap.release()\n",
|
|
|
|
|
"cv.destroyAllWindows()"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
"id": "4cb86c95-195f-4c01-ba22-f569d1b3b964",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": []
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"metadata": {
|
|
|
|
|
"kernelspec": {
|
|
|
|
|
"display_name": "Python 3 (ipykernel)",
|
|
|
|
|
"language": "python",
|
|
|
|
|
"name": "python3"
|
|
|
|
|
},
|
|
|
|
|
"language_info": {
|
|
|
|
|
"codemirror_mode": {
|
|
|
|
|
"name": "ipython",
|
|
|
|
|
"version": 3
|
|
|
|
|
},
|
|
|
|
|
"file_extension": ".py",
|
|
|
|
|
"mimetype": "text/x-python",
|
|
|
|
|
"name": "python",
|
|
|
|
|
"nbconvert_exporter": "python",
|
|
|
|
|
"pygments_lexer": "ipython3",
|
|
|
|
|
"version": "3.8.0"
|
|
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
"nbformat": 4,
|
|
|
|
|
"nbformat_minor": 5
|
|
|
|
|
}
|
