def detect_image(class_names, net, meta, im, thresh=.5, hier_thresh=.5, nms=.45, debug=False):num = c_int(0)if debug: print("Assigned num")pnum = pointer(num)if debug: print("Assigned pnum")predict_image(net, im)if debug: print("did prediction")dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum, 0)if debug: print("Got dets")num = pnum[0]if debug: print("got zeroth index of pnum")if nms:do_nms_sort(dets, num, meta.classes, nms)if debug: print("did sort")res = []if debug: print("about to range")for j in range(num):if debug: print("Ranging on " + str(j) + " of " + str(num))if debug: print("Classes: " + str(meta), meta.classes, meta.names)for i in range(meta.classes):if debug: print("Class-ranging on " + str(i) + " of " + str(meta.classes) + "= " + str(dets[j].prob[i]))if dets[j].prob[i] > 0.0:b = dets[j].bboxif altNames is None:# nameTag = meta.names[i] 该步骤会导致段错误，初步判断应该是和c++程序有关，所以直接传入类别列表参数，以绕过该问题。nameTag = class_names[i]print(nameTag)else:nameTag = altNames[i]print(nameTag)if debug:print("Got bbox", b)print(nameTag)print(dets[j].prob[i])print((b.x, b.y, b.w, b.h))res.append((nameTag, dets[j].prob[i], (b.x, b.y, b.w, b.h)))if debug: print("did range")res = sorted(res, key=lambda x: -x[1])if debug: print("did sort")free_detections(dets, num)if debug: print("freed detections")return res

# -*- coding: utf-8 -*-
"""
本模块使用yolov3模型探测目标在图片或视频中的位置
"""
__author__ = '程序员一一涤生'import colorsys
import os
from timeit import default_timer as timer
import cv2
import numpy as np
from PIL import ImageDraw, ImageFont, Image
import darknetdef _convertBack(x, y, w, h):xmin = int(round(x - (w / 2)))xmax = int(round(x + (w / 2)))ymin = int(round(y - (h / 2)))ymax = int(round(y + (h / 2)))return xmin, ymin, xmax, ymaxdef letterbox_image(image, size):'''resize image with unchanged aspect ratio using padding'''iw, ih = image.sizew, h = sizescale = min(w / iw, h / ih)nw = int(iw * scale)nh = int(ih * scale)image = image.resize((nw, nh), Image.BICUBIC)new_image = Image.new('RGB', size, (128, 128, 128))new_image.paste(image, ((w - nw) // 2, (h - nh) // 2))return new_imageclass YOLO(object):_defaults = {"configPath": "names-data/yolo-obj.cfg","weightPath": "names-data/backup/yolo-obj_3000.weights","metaPath": "names-data/voc.data","classes_path": "names-data/voc.names","thresh": 0.3,"iou_thresh": 0.5,# "model_image_size": (416, 416),# "model_image_size": (608, 608),"model_image_size": (800, 800),"gpu_num": 1,}def __init__(self, **kwargs):self.__dict__.update(self._defaults)  # set up default valuesself.__dict__.update(kwargs)  # and update with user overridesself.class_names = self._get_class()self.colors = self._get_colors()self.netMain = darknet.load_net_custom(self.configPath.encode("ascii"), self.weightPath.encode("ascii"), 0,1)  # batch size = 1self.metaMain = darknet.load_meta(self.metaPath.encode("ascii"))self.altNames = self._get_alt_names()def _get_class(self):classes_path = os.path.expanduser(self.classes_path)with open(classes_path, encoding="utf-8") as f:class_names = f.readlines()class_names = [c.strip() for c in class_names]return class_namesdef _get_colors(self):class_names = self._get_class()# Generate colors for drawing bounding boxes.hsv_tuples = [(x / len(class_names), 1., 1.)for x in range(len(class_names))]colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), colors))np.random.seed(10101)  # Fixed seed for consistent colors across runs.np.random.shuffle(colors)  # Shuffle colors to decorrelate adjacent classes.np.random.seed(None)  # Reset seed to default.return colorsdef _get_alt_names(self):try:with open(self.metaPath) as metaFH:metaContents = metaFH.read()import rematch = re.search("names *= *(.*)$", metaContents, re.IGNORECASE | re.MULTILINE)if match:result = match.group(1)else:result = Nonetry:if os.path.exists(result):with open(result) as namesFH:namesList = namesFH.read().strip().split("\n")altNames = [x.strip() for x in namesList]except TypeError:passexcept Exception:passreturn altNamesdef cvDrawBoxes(self, detections, image):# 字体相关设置，包括字体文件路径、字体大小font = ImageFont.truetype(font='font/simfang.ttf',size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))# 检测框的边框厚度，该公式使得厚度可以根据图片的大小来自动调整thickness = (image.size[0] + image.size[1]) // 300  #
        # 遍历每个检测到的目标detection:(classname,probaility,(x,y,w,h))for c, detection in enumerate(detections):# 获取当前目标的类别和置信度分数classname = detection[0]# score = round(detection[1] * 100, 2)score = round(detection[1], 2)label = '{} {:.2f}'.format(classname, score)# 计算检测框左上角(xmin, ymin)和右下角的坐标(xmax, ymax)x, y, w, h = detection[2][0], \detection[2][1], \detection[2][2], \detection[2][3]xmin, ymin, xmax, ymax = _convertBack(float(x), float(y), float(w), float(h))# 获取绘制实例draw = ImageDraw.Draw(image)# 获取将显示的文本的大小label_size = draw.textsize(label, font)# 将坐标对应到top, left, bottom, right，注意不要对应错了top, left, bottom, right = (ymin, xmin, ymax, xmax)top = max(0, np.floor(top + 0.5).astype('int32'))left = max(0, np.floor(left + 0.5).astype('int32'))bottom = min(image.size[1], np.floor(bottom + 0.5).astype('int32'))right = min(image.size[0], np.floor(right + 0.5).astype('int32'))print(label, (left, top), (right, bottom))if top - label_size[1] >= 0:text_origin = np.array([left, top - label_size[1]])else:text_origin = np.array([left, top + 1])if c > len(self.class_names) - 1:c = 1# 绘制边框厚度for i in range(thickness):draw.rectangle([left + i, top + i, right - i, bottom - i],outline=self.colors[c])# 绘制检测框的文本边界
            draw.rectangle([tuple(text_origin), tuple(text_origin + label_size)],fill=self.colors[c])# 绘制文本draw.text(text_origin, label, fill=(0, 0, 0), font=font)del drawreturn imagedef detect_video(self, video_path, output_path="",show=True):nw = self.model_image_size[0]nh = self.model_image_size[1]assert nw % 32 == 0, 'Multiples of 32 required'assert nh % 32 == 0, 'Multiples of 32 required'vid = cv2.VideoCapture(video_path)if not vid.isOpened():raise IOError("Couldn't open webcam or video")video_FourCC = cv2.VideoWriter_fourcc(*"mp4v")video_fps = vid.get(cv2.CAP_PROP_FPS)video_size = (nw,nh)isOutput = True if output_path != "" else Falseif isOutput:print("!!! TYPE:", type(output_path), type(video_FourCC), type(video_fps), type(video_size))out = cv2.VideoWriter(output_path, video_FourCC, video_fps, video_size)accum_time = 0curr_fps = 0fps = "FPS: ??"prev_time = timer()# Create an image we reuse for each detectdarknet_image = darknet.make_image(nw, nh, 3)while True:return_value, frame = vid.read()if return_value:# 转成RGB格式，因为opencv默认使用BGR格式读取图片，而PIL是用RGBframe_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)image = Image.fromarray(frame_rgb)image_resized = image.resize(video_size, Image.LINEAR)darknet.copy_image_from_bytes(darknet_image, np.asarray(image_resized).tobytes())detections = darknet.detect_image(self.class_names, self.netMain, self.metaMain, darknet_image,thresh=self.thresh, debug=True)image_resized = self.cvDrawBoxes(detections, image_resized)result = np.asarray(image_resized)# 转成BGR格式以便opencv处理result = cv2.cvtColor(result, cv2.COLOR_RGB2BGR)curr_time = timer()exec_time = curr_time - prev_timeprev_time = curr_timeaccum_time = accum_time + exec_timecurr_fps = curr_fps + 1if accum_time > 1:accum_time = accum_time - 1fps = "FPS: " + str(curr_fps)curr_fps = 0cv2.putText(result, text=fps, org=(3, 15), fontFace=cv2.FONT_HERSHEY_SIMPLEX,fontScale=0.50, color=(255, 0, 0), thickness=2)if show:cv2.imshow("Object Detect", result)if isOutput:print("start write...==========================================")out.write(result)if cv2.waitKey(1) & 0xFF == ord('q'):breakelse:breakout.release()vid.release()cv2.destroyAllWindows()def detect_image(self, image_path, save_path):nw = self.model_image_size[0]nh = self.model_image_size[1]assert nw % 32 == 0, 'Multiples of 32 required'assert nh % 32 == 0, 'Multiples of 32 required'try:image = Image.open(image_path)except:print('Open Error! Try again!')else:image_resized = image.resize((nw, nh), Image.LINEAR)darknet_image = darknet.make_image(nw, nh, 3)darknet.copy_image_from_bytes(darknet_image, np.asarray(image_resized).tobytes())# 识别图片得到目标的类别、置信度、中心点坐标和检测框的高宽detections = darknet.detect_image(self.class_names, self.netMain, self.metaMain, darknet_image,thresh=0.25, debug=True)# 在图片上将detections信息绘制出来image_resized = self.cvDrawBoxes(detections, image_resized)# 显示绘制后的图片
            image_resized.show()image_resized.save(save_path)if __name__ == "__main__":_yolo = YOLO()_yolo.detect_image("names-data/images/food.JPG", "names-data/images/food_detect.JPG")# _yolo.detect_video("names-data/videos/food.mp4", "names-data/videos/food_detect.mp4",show=False)

void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output){int i, j;if (!show_img) return;static int frame_id = 0;frame_id++;for (i = 0; i < num; ++i) {char labelstr[4096] = { 0 };int class_id = -1;for (j = 0; j < classes; ++j) {int show = strncmp(names[j], "dont_show", 9);if (dets[i].prob[j] > thresh && show) {float score=dets[i].prob[j];//在label标签上加入置信度if (class_id < 0) {strcat(labelstr, names[j]);strcat(labelstr, ", ");sprintf(labelstr + strlen(labelstr), "%0.2f", score);class_id = j;}else {strcat(labelstr, ", ");strcat(labelstr, names[j]);strcat(labelstr, ", ");sprintf(labelstr + strlen(labelstr), "%0.2f", score);}printf("%s: %.0f%% ", names[j], score * 100);}}if (class_id >= 0) {int width = show_img->height * .006;int offset = class_id * 123457 % classes;float red = get_color(2, offset, classes);float green = get_color(1, offset, classes);float blue = get_color(0, offset, classes);float rgb[3];rgb[0] = red;rgb[1] = green;rgb[2] = blue;box b = dets[i].bbox;b.w = (b.w < 1) ? b.w : 1;b.h = (b.h < 1) ? b.h : 1;b.x = (b.x < 1) ? b.x : 1;b.y = (b.y < 1) ? b.y : 1;int left = (b.x - b.w / 2.)*show_img->width;int right = (b.x + b.w / 2.)*show_img->width;int top = (b.y - b.h / 2.)*show_img->height;int bot = (b.y + b.h / 2.)*show_img->height;if (left < 0) left = 0;if (right > show_img->width - 1) right = show_img->width - 1;if (top < 0) top = 0;if (bot > show_img->height - 1) bot = show_img->height - 1;float const font_size = show_img->height / 1000.F;CvPoint pt1, pt2, pt_text, pt_text_bg1, pt_text_bg2;pt1.x = left;pt1.y = top;pt2.x = right;pt2.y = bot;pt_text.x = left;pt_text.y = top - 12;pt_text_bg1.x = left;pt_text_bg1.y = top - (10 + 25 * font_size);pt_text_bg2.x = right;pt_text_bg2.y = top;CvScalar color;color.val[0] = red * 256;color.val[1] = green * 256;color.val[2] = blue * 256;cvRectangle(show_img, pt1, pt2, color, width, 8, 0);if (ext_output)printf("\t(left_x: %4.0f   top_y: %4.0f   width: %4.0f   height: %4.0f)\n",(float)left, (float)top, b.w*show_img->width, b.h*show_img->height);elseprintf("\n");cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, width, 8, 0);cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, CV_FILLED, 8, 0);    // filled
            CvScalar black_color;black_color.val[0] = 0;CvFont font;cvInitFont(&font, CV_FONT_HERSHEY_SIMPLEX, font_size, font_size, 0, font_size * 3, 8);cvPutText(show_img, labelstr, pt_text, &font, black_color);}}if (ext_output) {fflush(stdout);}
}