darknet yolov4 python接口测试图像

darknet yolov4 python(linux gpu)接口测试图像

1.安装教程

2.darknet python目标检测接口（最新版本-持续更新）

3.darknet python目标检测接口（2020-06月darknet版本）

3.可视化效果

1.安装教程

按照github darknet yolov4要求配置即可，会出现lib.so文件

根据最新版本的darknet代码，编写图像-视频预测python代码！

2020-06 darknet.py内容：https://github.com/gengyanlei/fire-detect-yolov4/blob/master/yolov4/darknet.py

最新版本代码详细见：https://github.com/gengyanlei/fire-detect-yolov4/blob/master/latest_darknet_API.py

2.darknet python目标检测接口（最新版本-持续更新）

'''
注释：author is leilei由于最新版本的darknet将images和video预测分开了，并不是很符合自己的额需求，因此参考darknet_images.py修改成属于自己的预测函数
注：darknet.py 核心函数：load_network、detect_image draw_boxes bbox2pointsdarknet_images.py 核心函数： image_detection,此函数需要修改成输入图像darknet官方写的预测图像输出依旧为正方形，而非原图！因此要转换成原图
'''
import os
import cv2
import numpy as np
import darknetclass Detect:def __init__(self, metaPath, configPath, weightPath, gpu_id=2, batch=1):''':param metaPath:   ***.data 存储各种参数:param configPath: ***.cfg  网络结构文件:param weightPath: ***.weights yolo的权重:param batch:      ########此类只支持batch=1############'''assert batch==1, "batch必须为1"# 设置gpu_iddarknet.set_gpu(gpu_id)# 网络network, class_names, class_colors = darknet.load_network(configPath,metaPath,weightPath,batch_size=batch)self.network = networkself.class_names = class_namesself.class_colors = class_colorsdef bbox2point(self, bbox):x, y, w, h = bboxxmin = x - (w / 2)xmax = x + (w / 2)ymin = y - (h / 2)ymax = y + (h / 2)return (xmin, ymin, xmax, ymax)def point2bbox(self, point):x1,y1,x2,y2 = pointx = (x1+x2)/2y = (y1+y2)/2w = (x2-x1)h = (y2-y1)return (x,y,w,h)def image_detection(self, image_bgr, network, class_names, class_colors, thresh=0.25):# 判断输入图像是否为3通道if len(image_bgr.shape) == 2:image_bgr = np.stack([image_bgr]*3, axis=-1)# 获取原始图像大小orig_h, orig_w = image_bgr.shape[:2]width = darknet.network_width(network)height = darknet.network_height(network)darknet_image = darknet.make_image(width, height, 3)# image = cv2.imread(image_path)image_rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)image_resized = cv2.resize(image_rgb, (width, height), interpolation=cv2.INTER_LINEAR)darknet.copy_image_from_bytes(darknet_image, image_resized.tobytes())detections = darknet.detect_image(network, class_names, darknet_image, thresh=thresh)darknet.free_image(darknet_image)'''注意：这里原始代码依旧是608*608，而不是原图大小，因此我们需要转换'''new_detections = []for detection in detections:pred_label, pred_conf, (x, y, w, h) = detectionnew_x = x / width * orig_wnew_y = y / height * orig_hnew_w = w / width * orig_wnew_h = h / height * orig_h# 可以约束一下(x1,y1,x2,y2) = self.bbox2point((new_x, new_y, new_w, new_h))x1 = x1 if x1 > 0 else 0x2 = x2 if x2 < orig_w else orig_wy1 = y1 if y1 > 0 else 0y2 = y2 if y2 < orig_h else orig_h(new_x, new_y, new_w, new_h) = self.point2bbox((x1,y1,x2,y2))new_detections.append((pred_label, pred_conf, (new_x, new_y, new_w, new_h)))image = darknet.draw_boxes(new_detections, image_rgb, class_colors)return cv2.cvtColor(image, cv2.COLOR_RGB2BGR), new_detectionsdef predict_image(self, image_bgr, thresh=0.25, is_show=True, save_path=''):''':param image_bgr:  输入图像:param thresh:     置信度阈值:param is_show:   是否将画框之后的原始图像返回:param save_path: 画框后的保存路径, eg='/home/aaa.jpg':return:'''draw_bbox_image, detections = self.image_detection(image_bgr, self.network, self.class_names, self.class_colors, thresh)if is_show:if save_path:cv2.imwrite(save_path, draw_bbox_image)return draw_bbox_imagereturn detectionsif __name__ == '__main__':# gpu 通过环境变量设置detect = Detect(metaPath=r'/home/cfg/sg.data',configPath=r'/home/cfg/yolov4-sg.cfg',weightPath=r'/home/yolov4-sg_best.weights',gpu_id=1)# 读取单张图像# image_path = r'/home/aa.jpg'# image = cv2.imread(image_path, -1)# draw_bbox_image = detect.predict_image(image, save_path='./pred.jpg')# 读取文件夹image_root = r'/home/Datasets/image/'save_root = r'./output'if not os.path.exists(save_root):os.makedirs(save_root)for name in os.listdir(image_root):print(name)image = cv2.imread(os.path.join(image_root, name), -1)draw_bbox_image = detect.predict_image(image, save_path=os.path.join(save_root, name))# 读取视频# video_path = r'/home/Datasets/SHIJI_Fire/20200915_2.mp4'# video_save_path = r'/home/20200915_3_pred.mp4'# cap = cv2.VideoCapture(video_path)# # 获取视频的fps， width height# fps = int(cap.get(cv2.CAP_PROP_FPS))# width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))# height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))# count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))# print(count)# # 创建视频# fourcc = cv2.VideoWriter_fourcc(*'mp4v')# # fourcc = cv2.VideoWriter_fourcc('I', '4', '2', '0')# video_writer = cv2.VideoWriter(video_save_path, fourcc=fourcc, fps=fps, frameSize=(width,height))# ret, frame = cap.read()  # ret表示下一帧还有没有 有为True# while ret:#     # 预测每一帧#     pred = detect.predict_image(frame)#     video_writer.write(pred)#     cv2.waitKey(fps)#     # 读取下一帧#     ret, frame = cap.read()#     print(ret)# cap.release()# cv2.destroyAllWindows()

3.darknet python目标检测接口（2020-06月darknet版本）

代码如下：主要调用darknet.py文件，此外自己写了自适应字体展示代码(与darknet终端命令得到的图像一样优美)

'''
注释：author is leileidarknet python调用接口，参考darknet.py即可！此代码为batch=1的测试代码，逐帧检测。
'''
import os
import cv2
import numpy as np
import random
import darknetclass Detect:def __init__(self, metaPath, configPath, weightPath, namesPath, gpu_id=2):''':param metaPath:   ***.data 存储各种参数:param configPath: ***.cfg  网络结构文件:param weightPath: ***.weights yolo的权重:param namesPath:  ***.data中的names路径，这里是便于读取使用'''# 设置gpu_iddarknet.set_gpu(gpu_id)# 网络self.netMain = darknet.load_net_custom(configPath.encode("ascii"), weightPath.encode("ascii"), 0, 1) # batch=1# 各种参数self.metaMain = darknet.load_meta(metaPath.encode("ascii"))# 读取标签类别名称列表self.names = self.read_names(namesPath)# 每类颜色肯定一致，但是每次执行不一定都一样self.colors = self.color()def read_names(self, namesPath):# 专门读取包含类别标签名的***.names文件with open(namesPath, 'r') as f:lines = f.readlines()altNames = [x.strip() for x in lines]f.close()return altNamesdef color(self):# rgb 格式colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(self.metaMain.classes)]return colorsdef predict_image(self, image, thresh=0.25, is_show=True, save_path=''):''':param image:    cv2.imread 图像, darknet自己会对图像进行预处理:param thresh:   置信度阈值, 其它阈值不变:param is_show:  是否将画框之后的图像返回:param save_path: 画框后的保存路径:return:         返回1个矩阵'''# bgr->rgbrgb_img = image[..., ::-1]# 获取图片大小，网络输入大小height, width = rgb_img.shape[:2]network_width = darknet.network_width(self.netMain)network_height = darknet.network_height(self.netMain)# 将图像resize到输入大小rsz_img = cv2.resize(rgb_img, (network_width, network_height), interpolation=cv2.INTER_LINEAR)# 转成tensor的形式，以及[1,C,H,W]darknet_image, _ = darknet.array_to_image(rsz_img)detections = darknet.detect_image(self.netMain, self.metaMain, darknet_image, thresh=thresh)if is_show:for detection in detections:x, y, w, h = detection[2][0], \detection[2][1], \detection[2][2], \detection[2][3]# 置信度conf = detection[1]# 预测标签label = detection[0].decode()# 获取坐标x *= width / network_widthw *= width / network_widthy *= height / network_heighth *= height / network_height# 转成x1y1x2y2,左上右下坐标; x是w方向xyxy = np.array([x - w / 2, y - h / 2, x + w / 2, y + h / 2])index = self.names.index(label)label_conf = f'{label} {conf:.2f}'self._plot_one_box(xyxy, rgb_img, self.colors[index], label_conf)bgr_img = rgb_img[..., ::-1]# 保存图像if save_path:cv2.imwrite(save_path, bgr_img)return bgr_img  #返回画框的bgr图像return detectionsdef _plot_one_box(self, xyxy, img_rgb, color, label):# 直接对原始图像操作img = img_rgb[..., ::-1]  # bgrpt1 = (int(xyxy[0]), int(xyxy[1]))  # 左上角pt2 = (int(xyxy[2]), int(xyxy[3]))  # 右下角thickness = round(0.001 * max(img.shape[0:2])) + 1  # 必须为整数# if thickness > 1:#     thickness = 1  # 可强制为1cv2.rectangle(img, pt1, pt2, color, thickness)  #画框,thickness线粗细# 获取字体的宽x-高y，实际上此高y应该乘1.5 才是字体的真实高度(bq是占上中、中下3个格)t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, fontScale=thickness / 3, thickness=thickness)[0]# 按照2种方式显示，默认是在框上面显示，当上框仅挨着上边界时，采用框内显示；右边界不管c1 = (pt1[0], pt1[1]-int(t_size[1]*1.5)) if pt1[1]-int(t_size[1]*1.5) >= 0 else (pt1[0], pt1[1])c2 = (pt1[0]+t_size[0], pt1[1]) if pt1[1]-int(t_size[1]*1.5) >= 0 else (pt1[0]+t_size[0], pt1[1]+int(t_size[1]*1.5))# 判断c1 xy坐标是否都大于0if c1[0]<0 or c1[1]<0:x_t = c1[0] if c1[0] >= 0 else 0y_t = c1[1] if c1[1] >= 0 else 0c1 = (x_t, y_t)# 字体框内背景填充与框颜色一致cv2.rectangle(img, c1, c2, color, -1)  # 当thickness=-1时为填充# 绘制文本，文本是在下1/3位置开始text_pos = (c1[0], c1[1]+t_size[1])cv2.putText(img, label, text_pos, cv2.FONT_HERSHEY_SIMPLEX, thickness / 3, [225, 255, 255], thickness=thickness, lineType=cv2.LINE_AA)if __name__ == '__main__':# gpu 通过环境变量设置CUDA_VISBLE_DEVICES=0detect = Detect(metaPath=r'./cfg/helmet.data',configPath=r'./cfg/yolov4-obj.cfg',weightPath=r'./backup/yolov4-obj_best.weights',namesPath=r'./data/helmet.names',gpu_id=2)# coco权重#detect = Detect(metaPath=r'./cfg/coco.data',#                configPath=r'./cfg/yolov4.cfg',#                weightPath=r'./yolov4.weights',#                namesPath=r'./data/coco.names',#                gpu_id=2)image = cv2.imread(r'/home/Datasets/image/200205_3430.jpg', -1)detect.predict_image(image, save_path='./pred.jpg')###############################################################''' 读取视频，保存视频 '''cap = cv2.VideoCapture(r'/home/Datasets/fire1.avi')# 获取视频的fps， width heightfps = int(cap.get(cv2.CAP_PROP_FPS))width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))print(count)# 创建视频fourcc = cv2.VideoWriter_fourcc(*'mp4v')video_writer = cv2.VideoWriter(r'/home/Datasets/fire1.mp4', fourcc=fourcc, fps=fps, frameSize=(width,height))ret, frame = cap.read()  # ret表示下一帧还有没有 有为Truewhile ret:# 预测每一帧pred = detect.predict_image(frame)video_writer.write(pred)cv2.waitKey(fps)# 读取下一帧ret, frame = cap.read()print(ret)cap.release()cv2.destroyAllWindows()

3.可视化效果

(左自己代码产生，右darknet命令产生，下coco权重可视化效果)

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darknet yolov4 python接口测试图像

1.安装教程

2.darknet python目标检测接口（最新版本-持续更新）

3.darknet python目标检测接口（2020-06月darknet版本）

3.可视化效果

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