Tensorflow深度学习之三十五:tf.image.draw_bounding_boxes

本文详细介绍了如何使用TensorFlow的tf.image.draw_bounding_boxes函数在图像上绘制边界框,包括函数定义、参数说明及代码示例。边界框坐标采用相对比例而非像素值,适用于图像目标检测任务。

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一、函数定义

tf.image.draw_bounding_boxes(
    images,
    boxes,
    name=None
)

  Draw bounding boxes on a batch of images.
  在一个batch的每一张图片上绘制边界框。
   Outputs a copy of images but draws on top of the pixels zero or more bounding boxes specified by the locations in boxes. The coordinates of the each bounding box in boxes are encoded as [y_min, x_min, y_max, x_max]. The bounding box coordinates are floats in [0.0, 1.0] relative to the width and height of the underlying image.
  输出images的一个副本,在图片上像素上绘制boxes中由位置指定的零个或多个边界框的。 框中每个边界框的坐标编码为[y_min,x_min,y_max,x_max]。 边界框坐标相对于底层图像的宽度和高度浮动在[0.0,1.0]中。
  For example, if an image is 100 x 200 pixels (height x width) and the bounding box is [0.1, 0.2, 0.5, 0.9], the upper-left and bottom-right coordinates of the bounding box will be (40, 10) to (180, 50) (in (x,y) coordinates).
  例如,如果图像为100 x 200像素(高x宽)并且边界框为“[0.1,0.2,0.5,0.9]”,则边界框的左上角和右下角坐标为(40, 10)(180, 50)(在(x,y)坐标中)。
  Parts of the bounding box may fall outside the image.
  边界框的某些部分可能会落在图像之外。

  需要注意的是边界框的坐标信息是相对于长宽的小数,而不是实际的像素坐标。

二、参数

参数
imagesA Tensor. Must be one of the following types: float32, half. 4-D with shape [batch, height, width, depth]. A batch of images.一个Tensor,数据类型必须为float32, half。4-D形状[batch, height, width, depth]。 表示一个batch的图片。
boxesA Tensor of type float32. 3-D with shape [batch, num_bounding_boxes, 4] containing bounding boxes.一个Tensor,数据类型为float32,shape为 [batch, num_bounding_boxes, 4]的包含边界框的秩为3的矩阵。
nameA name for the operation (optional).名称(可选参数)

三、代码
  原图如下:
在这里插入图片描述

import tensorflow as tf
import cv2
import numpy as np

tf.enable_eager_execution()

img = cv2.imread("timg.jpeg") / 255.0

bbox = [[0, 0.1, 0.5, 0.5], [0.6, 0.7, 0.9, 1.0]]

bbox_img = tf.image.draw_bounding_boxes([img], [bbox])

cv2.imwrite('bbox.jpg', np.asarray(bbox_img.numpy()[0] * 255, np.uint8))

  结果如下:
在这里插入图片描述

CONF_THRESH = 0.5 IOU_THRESHOLD = 0.4 POSE_NUM = 17 * 3 DET_NUM = 6 SEG_NUM = 32 OBB_NUM = 1 def get_img_path_batches(batch_size, img_dir): ret = [] batch = [] for root, dirs, files in os.walk(img_dir): for name in files: if len(batch) == batch_size: ret.append(batch) batch = [] batch.append(os.path.join(root, name)) if len(batch) > 0: ret.append(batch) return ret def plot_one_box(x, img, color=None, label=None, line_thickness=None): """ description: Plots one bounding box on image img, this function comes from YoLov8 project. param: x: a box likes [x1,y1,x2,y2] img: a opencv image object color: color to draw rectangle, such as (0,255,0) label: str line_thickness: int return: no return """ tl = ( line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 ) # line/font thickness color = color or [random.randint(0, 255) for _ in range(3)] c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3])) cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA) if label: tf = max(tl - 1, 1) # font thickness t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled cv2.putText( img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA, ) class YoLov8TRT(object): """ description: A YOLOv8 class that warps TensorRT ops, preprocess and postprocess ops. """ def __init__(self, engine_file_path): # Create a Context on this device, self.ctx = cuda.Device(0).make_context() stream = cuda.Stream() TRT_LOGGER = trt.Logger(trt.Logger.INFO) runtime = trt.Runtime(TRT_LOGGER) # Deserialize the engine from file with open(engine_file_path, "rb") as f: engine = runtime.deserialize_cuda_engine(f.read()) context = engine.create_execution_context() host_inputs = [] cuda_inputs = [] host_outputs = [] cuda_outputs = [] bindings = [] for binding in engine: print('bingding:', binding, engine.get_binding_shape(binding)) size = trt.volume(engine.get_binding_shape(binding)) * engine.max_batch_size dtype = trt.nptype(engine.get_binding_dtype(binding)) # Allocate host and device buffers host_mem = cuda.pagelocked_empty(size, dtype) cuda_mem = cuda.mem_alloc(host_mem.nbytes) # Append the device buffer to device bindings. bindings.append(int(cuda_mem)) # Append to the appropriate list. if engine.binding_is_input(binding): self.input_w = engine.get_binding_shape(binding)[-1] self.input_h = engine.get_binding_shape(binding)[-2] host_inputs.append(host_mem) cuda_inputs.append(cuda_mem) else: host_outputs.append(host_mem) cuda_outputs.append(cuda_mem) # Store self.stream = stream self.context = context self.engine = engine self.host_inputs = host_inputs self.cuda_inputs = cuda_inputs self.host_outputs = host_outputs self.cuda_outputs = cuda_outputs self.bindings = bindings self.batch_size = engine.max_batch_size self.det_output_length = host_outputs[0].shape[0] def infer(self, raw_image_generator): threading.Thread.__init__(self) # Make self the active context, pushing it on top of the context stack. self.ctx.push() # Restore stream = self.stream context = self.context host_inputs = self.host_inputs cuda_inputs = self.cuda_inputs host_outputs = self.host_outputs cuda_outputs = self.cuda_outputs bindings = self.bindings # Do image preprocess batch_image_raw = [] batch_origin_h = [] batch_origin_w = [] batch_input_image = np.empty(shape=[self.batch_size, 3, self.input_h, self.input_w]) for i, image_raw in enumerate(raw_image_generator): input_image, image_raw, origin_h, origin_w = self.preprocess_image(image_raw) batch_image_raw.append(image_raw) batch_origin_h.append(origin_h) batch_origin_w.append(origin_w) np.copyto(batch_input_image[i], input_image) batch_input_image = np.ascontiguousarray(batch_input_image) # Copy input image to host buffer np.copyto(host_inputs[0], batch_input_image.ravel()) start = time.time() # Transfer input data to the GPU. cuda.memcpy_htod_async(cuda_inputs[0], host_inputs[0], stream) # Run inference. context.execute_async(batch_size=self.batch_size, bindings=bindings, stream_handle=stream.handle) # Transfer predictions back from the GPU. cuda.memcpy_dtoh_async(host_outputs[0], cuda_outputs[0], stream) # Synchronize the stream stream.synchronize() end = time.time() # Remove any context from the top of the context stack, deactivating it. self.ctx.pop() # Here we use the first row of output in that batch_size = 1 output = host_outputs[0] # Do postprocess for i in range(self.batch_size): result_boxes, result_scores, result_classid = self.post_process( output[i * self.det_output_length: (i + 1) * self.det_output_length], batch_origin_h[i], batch_origin_w[i] ) # Draw rectangles and labels on the original image for j in range(len(result_boxes)): box = result_boxes[j] plot_one_box( box, batch_image_raw[i], label="{}:{:.2f}".format( categories[int(result_classid[j])], result_scores[j] ), ) return batch_image_raw, end - start def destroy(self): # Remove any context from the top of the context stack, deactivating it. self.ctx.pop() def get_raw_image(self, image_path_batch): """ description: Read an image from image path """ for img_path in image_path_batch: yield cv2.imread(img_path) def get_raw_image_zeros(self, image_path_batch=None): """ description: Ready data for warmup """ for _ in range(self.batch_size): yield np.zeros([self.input_h, self.input_w, 3], dtype=np.uint8) def preprocess_image(self, raw_bgr_image): """ description: Convert BGR image to RGB, resize and pad it to target size, normalize to [0,1], transform to NCHW format. param: input_image_path: str, image path return: image: the processed image image_raw: the original image h: original height w: original width """
最新发布
07-13
mport ctypes import os import shutil import random import sys import threading import time import cv2 import numpy as np import pycuda.autoinit import pycuda.driver as cuda import tensorrt as trt CONF_THRESH = 0.8 IOU_THRESHOLD = 0.6 def get_img_path_batches(batch_size, img_dir): ret = [] batch = [] for root, dirs, files in os.walk(img_dir): for name in files: if len(batch) == batch_size: ret.append(batch) batch = [] batch.append(os.path.join(root, name)) if len(batch) > 0: ret.append(batch) return ret def plot_one_box(x, img, color=None, label=None, line_thickness=None): """ description: Plots one bounding box on image img, this function comes from YoLov5 project. param: x: a box likes [x1,y1,x2,y2] img: a opencv image object color: color to draw rectangle, such as (0,255,0) label: str line_thickness: int return: no return """ tl = ( line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 ) # line/font thickness color = color or [random.randint(0, 255) for _ in range(3)] c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3])) cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA) if label: tf = max(tl - 1, 1) # font thickness t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled cv2.putText( img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA, ) class YoLov5TRT(object): """ description: A YOLOv5 class that warps TensorRT ops, preprocess and postprocess ops. """ def __init__(self, engine_file_path): # Create a Context on this device, self.ctx = cuda.Device(0).make_context() stream = cuda.Stream() TRT_LOGGER = trt.Logger(trt.Logger.INFO) runtime = trt.Runtime(TRT_LOGGER) # Deserialize the engine from file with open(engine_file_path, "rb") as f: engine = runtime.deserialize_cuda_engine(f.read()) context = engine.create_execution_context() host_inputs = [] cuda_inputs = [] host_outputs = [] cuda_outputs = [] bindings = [] for binding in engine: # print('bingding:', binding, engine.get_binding_shape(binding)) size = trt.volume(engine.get_binding_shape(binding)) * engine.max_batch_size dtype = trt.nptype(engine.get_binding_dtype(binding)) # Allocate host and device buffers host_mem = cuda.pagelocked_empty(size, dtype) cuda_mem = cuda.mem_alloc(host_mem.nbytes) # Append the device buffer to device bindings. bindings.append(int(cuda_mem)) # Append to the appropriate list. if engine.binding_is_input(binding): self.input_w = engine.get_binding_shape(binding)[-1] self.input_h = engine.get_binding_shape(binding)[-2] host_inputs.append(host_mem) cuda_inputs.append(cuda_mem) else: host_outputs.append(host_mem) cuda_outputs.append(cuda_mem) # Store self.stream = stream self.context = context self.engine = engine self.host_inputs = host_inputs self.cuda_inputs = cuda_inputs self.host_outputs = host_outputs self.cuda_outputs = cuda_outputs self.bindings = bindings self.batch_size = 1 def infer(self, raw_image_generator): # threading.Thread.__init__(self) # Make self the active context, pushing it on top of the context stack. self.ctx.push() # Restore stream = self.stream context = self.context engine = self.engine host_inputs = self.host_inputs cuda_inputs = self.cuda_inputs host_outputs = self.host_outputs cuda_outputs = self.cuda_outputs bindings = self.bindings # Do image preprocess batch_image_raw = [] batch_origin_h = [] batch_origin_w = [] batch_input_image = np.empty(shape=[self.batch_size, 3, self.input_h, self.input_w]) input_image, image_raw, origin_h, origin_w = self.preprocess_image(raw_image_generator) batch_image_raw.append(image_raw) batch_origin_h.append(origin_h) batch_origin_w.append(origin_w) np.copyto(batch_input_image[0], input_image) batch_input_image = np.ascontiguousarray(batch_input_image) # Copy input image to host buffer np.copyto(host_inputs[0], batch_input_image.ravel()) start = time.time() # Transfer input data to the GPU. cuda.memcpy_htod_async(cuda_inputs[0], host_inputs[0], stream) # Run inference. context.execute_async(batch_size=self.batch_size, bindings=bindings, stream_handle=stream.handle) # Transfer predictions back from the GPU. cuda.memcpy_dtoh_async(host_outputs[0], cuda_outputs[0], stream) # Synchronize the stream stream.synchronize() end = time.time() # Remove any context from the top of the context stack, deactivating it. self.ctx.pop() # Here we use the first row of output in that batch_size = 1 output = host_outputs[0] # Do postprocess for i in range(self.batch_size): result_boxes, result_scores, result_classid = self.post_process(output[i * 6001: (i + 1) * 6001], batch_origin_h[i], batch_origin_w[i]) return result_boxes, result_scores, result_classid, end - start # # Draw rectangles and labels on the original image # for j in range(len(result_boxes)): # box = result_boxes[j] # plot_one_box( # box, # batch_image_raw[i], # label="{}:{:.2f}".format( # categories[int(result_classid[j])], result_scores[j] # ), # ) def destroy(self): # Remove any context from the top of the context stack, deactivating it. self.ctx.pop() def get_raw_image(self, image_path_batch): """ description: Read an image from image path """ for img_path in image_path_batch: yield cv2.imread(img_path) def get_raw_image_zeros(self, image_path_batch=None): """ description: Ready data for warmup """ for _ in range(self.batch_size): yield np.zeros([self.input_h, self.input_w, 3], dtype=np.uint8) def preprocess_image(self, raw_bgr_image): """ description: Convert BGR image to RGB, resize and pad it to target size, normalize to [0,1], transform to NCHW format. param: input_image_path: str, image path return: image: the processed image image_raw: the original image h: original height w: original width """ image_raw = raw_bgr_image h, w, c = image_raw.shape image = cv2.cvtColor(image_raw, cv2.COLOR_BGR2RGB) # Calculate widht and height and paddings r_w = self.input_w / w r_h = self.input_h / h if r_h > r_w: tw = self.input_w th = int(r_w * h) tx1 = tx2 = 0 ty1 = int((self.input_h - th) / 2) ty2 = self.input_h - th - ty1 else: tw = int(r_h * w) th = self.input_h tx1 = int((self.input_w - tw) / 2) tx2 = self.input_w - tw - tx1 ty1 = ty2 = 0 # Resize the image with long side while maintaining ratio image = cv2.resize(image, (tw, th)) # Pad the short side with (128,128,128) image = cv2.copyMakeBorder( image, ty1, ty2, tx1, tx2, cv2.BORDER_CONSTANT, None, (128, 128, 128) ) image = image.astype(np.float32) # Normalize to [0,1] image /= 255.0 # HWC to CHW format: image = np.transpose(image, [2, 0, 1]) # CHW to NCHW format image = np.expand_dims(image, axis=0) # Convert the image to row-major order, also known as "C order": image = np.ascontiguousarray(image) return image, image_raw, h, w def xywh2xyxy(self, origin_h, origin_w, x): """ description: Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right param: origin_h: height of original image origin_w: width of original image x: A boxes numpy, each row is a box [center_x, center_y, w, h] return: y: A boxes numpy, each row is a box [x1, y1, x2, y2] """ y = np.zeros_like(x) r_w = self.input_w / origin_w r_h = self.input_h / origin_h if r_h > r_w: y[:, 0] = x[:, 0] - x[:, 2] / 2 y[:, 2] = x[:, 0] + x[:, 2] / 2 y[:, 1] = x[:, 1] - x[:, 3] / 2 - (self.input_h - r_w * origin_h) / 2 y[:, 3] = x[:, 1] + x[:, 3] / 2 - (self.input_h - r_w * origin_h) / 2 y /= r_w else: y[:, 0] = x[:, 0] - x[:, 2] / 2 - (self.input_w - r_h * origin_w) / 2 y[:, 2] = x[:, 0] + x[:, 2] / 2 - (self.input_w - r_h * origin_w) / 2 y[:, 1] = x[:, 1] - x[:, 3] / 2 y[:, 3] = x[:, 1] + x[:, 3] / 2 y /= r_h return y def post_process(self, output, origin_h, origin_w): """ description: postprocess the prediction param: output: A numpy likes [num_boxes,cx,cy,w,h,conf,cls_id, cx,cy,w,h,conf,cls_id, ...] origin_h: height of original image origin_w: width of original image return: result_boxes: finally boxes, a boxes numpy, each row is a box [x1, y1, x2, y2] result_scores: finally scores, a numpy, each element is the score correspoing to box result_classid: finally classid, a numpy, each element is the classid correspoing to box """ # Get the num of boxes detected num = int(output[0]) # Reshape to a two dimentional ndarray pred = np.reshape(output[1:], (-1, 6))[:num, :] # Do nms boxes = self.non_max_suppression(pred, origin_h, origin_w, conf_thres=CONF_THRESH, nms_thres=IOU_THRESHOLD) result_boxes = boxes[:, :4] if len(boxes) else np.array([]) result_scores = boxes[:, 4] if len(boxes) else np.array([]) result_classid = boxes[:, 5] if len(boxes) else np.array([]) return result_boxes, result_scores, result_classid def bbox_iou(self, box1, box2, x1y1x2y2=True): """ description: compute the IoU of two bounding boxes param: box1: A box coordinate (can be (x1, y1, x2, y2) or (x, y, w, h)) box2: A box coordinate (can be (x1, y1, x2, y2) or (x, y, w, h)) x1y1x2y2: select the coordinate format return: iou: computed iou """ if not x1y1x2y2: # Transform from center and width to exact coordinates b1_x1, b1_x2 = box1[:, 0] - box1[:, 2] / 2, box1[:, 0] + box1[:, 2] / 2 b1_y1, b1_y2 = box1[:, 1] - box1[:, 3] / 2, box1[:, 1] + box1[:, 3] / 2 b2_x1, b2_x2 = box2[:, 0] - box2[:, 2] / 2, box2[:, 0] + box2[:, 2] / 2 b2_y1, b2_y2 = box2[:, 1] - box2[:, 3] / 2, box2[:, 1] + box2[:, 3] / 2 else: # Get the coordinates of bounding boxes b1_x1, b1_y1, b1_x2, b1_y2 = box1[:, 0], box1[:, 1], box1[:, 2], box1[:, 3] b2_x1, b2_y1, b2_x2, b2_y2 = box2[:, 0], box2[:, 1], box2[:, 2], box2[:, 3] # Get the coordinates of the intersection rectangle inter_rect_x1 = np.maximum(b1_x1, b2_x1) inter_rect_y1 = np.maximum(b1_y1, b2_y1) inter_rect_x2 = np.minimum(b1_x2, b2_x2) inter_rect_y2 = np.minimum(b1_y2, b2_y2) # Intersection area inter_area = np.clip(inter_rect_x2 - inter_rect_x1 + 1, 0, None) * \ np.clip(inter_rect_y2 - inter_rect_y1 + 1, 0, None) # Union Area b1_area = (b1_x2 - b1_x1 + 1) * (b1_y2 - b1_y1 + 1) b2_area = (b2_x2 - b2_x1 + 1) * (b2_y2 - b2_y1 + 1) iou = inter_area / (b1_area + b2_area - inter_area + 1e-16) return iou def non_max_suppression(self, prediction, origin_h, origin_w, conf_thres=0.5, nms_thres=0.4): """ description: Removes detections with lower object confidence score than 'conf_thres' and performs Non-Maximum Suppression to further filter detections. param: prediction: detections, (x1, y1, x2, y2, conf, cls_id) origin_h: original image height origin_w: original image width conf_thres: a confidence threshold to filter detections nms_thres: a iou threshold to filter detections return: boxes: output after nms with the shape (x1, y1, x2, y2, conf, cls_id) """ # Get the boxes that score > CONF_THRESH boxes = prediction[prediction[:, 4] >= conf_thres] # Trandform bbox from [center_x, center_y, w, h] to [x1, y1, x2, y2] boxes[:, :4] = self.xywh2xyxy(origin_h, origin_w, boxes[:, :4]) # clip the coordinates boxes[:, 0] = np.clip(boxes[:, 0], 0, origin_w -1) boxes[:, 2] = np.clip(boxes[:, 2], 0, origin_w -1) boxes[:, 1] = np.clip(boxes[:, 1], 0, origin_h -1) boxes[:, 3] = np.clip(boxes[:, 3], 0, origin_h -1) # Object confidence confs = boxes[:, 4] # Sort by the confs boxes = boxes[np.argsort(-confs)] # Perform non-maximum suppression keep_boxes = [] while boxes.shape[0]: large_overlap = self.bbox_iou(np.expand_dims(boxes[0, :4], 0), boxes[:, :4]) > nms_thres label_match = boxes[0, -1] == boxes[:, -1] # Indices of boxes with lower confidence scores, large IOUs and matching labels invalid = large_overlap & label_match keep_boxes += [boxes[0]] boxes = boxes[~invalid] boxes = np.stack(keep_boxes, 0) if len(keep_boxes) else np.array([]) return boxes # class inferThread(threading.Thread): # def __init__(self, yolov5_wrapper, image_path_batch): # threading.Thread.__init__(self) # self.yolov5_wrapper = yolov5_wrapper # self.image_path_batch = image_path_batch # def run(self): # batch_image_raw, use_time = self.yolov5_wrapper.infer(self.yolov5_wrapper.get_raw_image(self.image_path_batch)) # for i, img_path in enumerate(self.image_path_batch): # parent, filename = os.path.split(img_path) # save_name = os.path.join('output', filename) # # Save image # cv2.imwrite(save_name, batch_image_raw[i]) # print('input->{}, time->{:.2f}ms, saving into output/'.format(self.image_path_batch, use_time * 1000)) class warmUpThread(threading.Thread): def __init__(self, yolov5_wrapper): threading.Thread.__init__(self) self.yolov5_wrapper = yolov5_wrapper def run(self): batch_image_raw, use_time = self.yolov5_wrapper.infer(self.yolov5_wrapper.get_raw_image_zeros()) print('warm_up->{}, time->{:.2f}ms'.format(batch_image_raw[0].shape, use_time * 1000)) # def get_max_area(img , boxs, result_classid, result_scores): # max_area = 0 # cls_id = 10 # score_1=0 # for box, class_id, score in zip(boxs, result_classid, result_scores): # x1, y1, x2, y2 = box # area = (x2-x1)*(y2-y1) # if area > 100: # cv2.rectangle(img, (int(x1), int(y1)), (int(x2), int(y2)), (255, 0, 0), # thickness=3, lineType=cv2.LINE_AA) # if area > max_area: # max_area = int(area) # cls_id = int(class_id) # score_1=score # return cls_id, max_area, score_1 def get_max_area(boxs, result_classid , result_scores): max_area = 0 cls_id = 0 _score = 0 max_box=[] for box, class_id ,score in zip(boxs, result_classid,result_scores): x1, y1, x2, y2 = box area = (y2-y1)*(y2-y1) if area > max_area: max_area = int(area) cls_id = int(class_id) max_box = box _score = score return cls_id, max_area, max_box ,_score def draw_rerectangle(frame,box,cls,score,area): x1, y1, x2, y2 = box cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), (255, 0, 0), thickness=3, lineType=cv2.LINE_AA) cv2.putText(frame, str("cls_id:%s" % cls), (20, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) cv2.putText(frame, str("area:%d" % area), (20, 120), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) cv2.putText(frame, str("score:%f" % score), (20, 90), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) if __name__ == "__main__": # load custom plugin and engine PLUGIN_LIBRARY = "/media/jian/My Passport/资料/car/ai_control-FULL-3.0(14)/ai_control-FULL-3.0/src/opencv_detection/scripts/small_yolo/libmyplugins.so" engine_file_path = "/media/jian/My Passport/资料/car/ai_control-FULL-3.0(14)/ai_control-FULL-3.0/src/opencv_detection/scripts/small_yolo/small.engine" if len(sys.argv) > 1: engine_file_path = sys.argv[1] if len(sys.argv) > 2: PLUGIN_LIBRARY = sys.argv[2] ctypes.CDLL(PLUGIN_LIBRARY) # load coco labels categories = ["danger","side_walk","speed","speed_limit","turn_left","turn_right","lane_change"] # a YoLov5TRT instance yolov5_wrapper = YoLov5TRT(engine_file_path) cap = cv2.VideoCapture(0,cv2.CAP_V4L2) try: while 1: ret, img = cap.read() if not ret: continue image = cv2.resize(img, (320, 240), interpolation=cv2.INTER_AREA) #w*h # print(image.shape) # frame = image[:224, 96:320, :] #h*w result_boxes, result_scores, result_classid, use_time = yolov5_wrapper.infer( image) cls_id = 10 area = 0 if (len(result_classid) != 0): cls_id, area, score = get_max_area(image,result_boxes, result_classid,result_scores) cv2.putText(image, str("cls_id:%s" % categories[cls_id]), (20, 100), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) cv2.putText(image, str("area:%d" % area), (20, 160), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) cv2.putText(image, str("score:%f" % score), (20, 130), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) cv2.imshow("frame", image) if cv2.waitKey(1) & 0xFF == ord('q'): break finally: # destroy the instance cap.release() cv2.destroyAllWindows() yolov5_wrapper.destroy()
07-07
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