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].bbox                  if altNames is None:         # nameTag = meta.names[i] 該步驟會導致段錯誤，初步判斷應該是和c++程式有關，所以直接傳入類別列表參數，以繞過該問題。                      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 -*-  """  本模組使用yolov3模型探測目標在圖片或影片中的位置  """  __author__ = '程式設計師一一滌生'import colorsys  import os  from timeit import default_timer as timer  import cv2  import numpy as np  from PIL import ImageDraw, ImageFont, Image  import darknet    def _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, ymax    def letterbox_image(image, size):      '''resize image with unchanged aspect ratio using padding'''      iw, ih = image.size      w, h = size      scale = 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_image    class 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 values          self.__dict__.update(kwargs)  # and update with user overrides          self.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 = 1          self.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_names        def _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 colors        def _get_alt_names(self):          try:              with open(self.metaPath) as metaFH:                  metaContents = metaFH.read()                  import re                  match = re.search("names *= *(.*)$", metaContents, re.IGNORECASE | re.MULTILINE)                  if match:                      result = match.group(1)                  else:                      result = None                  try:                      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:                      pass          except Exception:              pass          return altNames        def cvDrawBoxes(self, detections, image):          # 字體相關設置，包括字體文件路徑、字體大小          font = ImageFont.truetype(font='font/simfang.ttf',                                    size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))          # 檢測框的邊框厚度，該公式使得厚度可以根據圖片的大小來自動調整          thickness = (image.size[0] + image.size[1]) // 300  #          # 遍歷每個檢測到的目標detection:(classname,probaility,(x,y,w,h))          for c, detection in enumerate(detections):              # 獲取當前目標的類別和置信度分數              classname = detection[0]              # score = round(detection[1] * 100, 2)              score = round(detection[1], 2)              label = '{} {:.2f}'.format(classname, score)              # 計算檢測框左上角(xmin, ymin)和右下角的坐標(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))              # 獲取繪製實例              draw = ImageDraw.Draw(image)              # 獲取將顯示的文本的大小              label_size = draw.textsize(label, font)              # 將坐標對應到top, left, bottom, right，注意不要對應錯了              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              # 繪製邊框厚度              for i in range(thickness):                  draw.rectangle(                      [left + i, top + i, right - i, bottom - i],                      outline=self.colors[c])              # 繪製檢測框的文本邊界              draw.rectangle(                  [tuple(text_origin), tuple(text_origin + label_size)],                  fill=self.colors[c])              # 繪製文本              draw.text(text_origin, label, fill=(0, 0, 0), font=font)              del draw          return image        def 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 False          if 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 = 0          curr_fps = 0          fps = "FPS: ??"          prev_time = timer()            # Create an image we reuse for each detect          darknet_image = darknet.make_image(nw, nh, 3)          while True:              return_value, frame = vid.read()              if return_value:                  # 轉成RGB格式，因為opencv默認使用BGR格式讀取圖片，而PIL是用RGB                  frame_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)                  # 轉成BGR格式以便opencv處理                  result = cv2.cvtColor(result, cv2.COLOR_RGB2BGR)                  curr_time = timer()                  exec_time = curr_time - prev_time                  prev_time = curr_time                  accum_time = accum_time + exec_time                  curr_fps = curr_fps + 1                  if accum_time > 1:                      accum_time = accum_time - 1                      fps = "FPS: " + str(curr_fps)                      curr_fps = 0                  cv2.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'):                      break              else:                  break          out.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())              # 識別圖片得到目標的類別、置信度、中心點坐標和檢測框的高寬              detections = darknet.detect_image(self.class_names, self.netMain, self.metaMain, darknet_image,                                                thresh=0.25, debug=True)              # 在圖片上將detections資訊繪製出來              image_resized = self.cvDrawBoxes(detections, image_resized)              # 顯示繪製後的圖片              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標籤上加入置信度                  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);              else                  printf("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);      }  }