# -*- encoding: utf-8 -*- # ---------------------------- Food Classifier --------------------------- # This code is written to test the food classifier using customized YOLO model # It supports YOLO v3 and v4 as of 20Dec. 11, 2020 # For options in detail, please refer to /config/confi.py # Usage example: python3 food_classifier_yolo.py --video=run.mp4 # python3 food_classifier_yolo.py --image=bird.jpg # ------------------------------------------------------------------------- # modified by speedpointer, 05 Aug, 2025 import os.path import cv2 as cv import argparse import sys import numpy as np import json from PIL import ImageFont, ImageDraw, Image from config import config parser = argparse.ArgumentParser(description='Food Classification and Localization ver. 0.9') parser.add_argument('--image', help='Full path to image file.') parser.add_argument('--video', help='Full path to video file.') parser.add_argument('--showText', type=int, default=1, help='show text in the output.') parser.add_argument('--ps', type=int, default=1, help='stop each image in the screen.') args = parser.parse_args() # Initialize the parameters args.image = config.TEST_IMAGE_PATH # image path args.video = config.TEST_VIDEO_PATH # video path args.showText = config.SHOW_TEXT_FLAG #1 args.ps = config.PS_FLAG # 1 # refine the inferences confThreshold = config.CONF_THRES # 0.1 #0.5 # Confidence threshold nmsThreshold = config.NMS_THRES #0.1 #0.4 # Non-maximum suppression threshold # modes inference size regardless of input image size inpWidth = config.INPWIDTH # 32*10 # 608 #Width of network's input image # 320(32*10) inpHeight = config.INPHEIGHT # 32*9 # 608 #Height of network's input image # 288(32*9) best # model base directory modelBaseDir = config.ModelBaseDir # "C:/Users/mmc/workspace/yolo" # Load names of classes from a file classesFile = os.path.sep.join([modelBaseDir, config.CLASSES_FILE]) classes = None with open(classesFile, 'rt', encoding='utf-8') as f: classes = f.read().rstrip('\n').split('\n') # Load codes of classes from a file classes_File_Codes = os.path.sep.join([modelBaseDir, config.CLASSES_FILE_CODE]) classes_codes = None with open(classes_File_Codes, 'rt', encoding='utf-8') as f: classes_codes = f.read().rstrip('\n').split('\n') assert (len(classes) == len(classes_codes)) # model configuration and weights paths modelConfiguration = os.path.sep.join([modelBaseDir, config.Model_Configuration]) modelWeights = os.path.sep.join([modelBaseDir, config.Model_Weights]) # load a given model net = cv.dnn.readNetFromDarknet(modelConfiguration, modelWeights) net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV) net.setPreferableTarget(cv.dnn.DNN_TARGET_OPENCL_FP16) # Get the names of the output layers def getOutputsNames(net): # Get the names of all the layers in the network layersNames = net.getLayerNames() # Get the names of the output layers, i.e. the layers with unconnected outputs # Fix for OpenCV 4.x compatibility unconnected = net.getUnconnectedOutLayers() if len(unconnected.shape) == 1: return [layersNames[i - 1] for i in unconnected] else: return [layersNames[i[0] - 1] for i in unconnected] # Draw the predicted bounding box def drawPred(frame, classId, conf, left, top, right, bottom): # Draw a bounding box. # cv.rectangle(frame, (left, top), (right, bottom), (255, 178, 50), 3) cv.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 3) label = '%.2f' % conf # Get the label for the class name and its confidence if classes: assert (classId < len(classes)) #label = '%s:%s' % (classes[classId], label) label = u'%s' % (classes[classId]) #label = u'%s' % (classId) print('label:{}, class_id:{}'.format(label, classId)) # Display the label at the top of the bounding box labelSize, baseLine = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, 1) top = max(top, labelSize[1]) if args.showText: #cv.rectangle(frame, (left, top - round(1.5 * labelSize[1])), (left + round(1.5 * labelSize[0]), top + baseLine), # (0, 255, 255), cv.FILLED) cv.rectangle(frame, (left, top - round(1.5*labelSize[1])), (left + round(1.5*labelSize[0]), top + baseLine), (0, 255, 255), cv.FILLED) cv.putText(frame, label, (left, top), cv.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 0), 2) #fontpath = "./font/gulim.ttc" #font_ = ImageFont.truetype(fontpath, 14) #img_pil = Image.fromarray(frame) #draw = ImageDraw.Draw(img_pil) #draw.text((left, top), label, font=font_, fill=(0, 0, 0, 0)) #frame = np.array(img_pil) #cv.imshow('pil', frame) def postprocess(frame, outs, showimg=False): frameHeight = frame.shape[0] frameWidth = frame.shape[1] # Scan through all the bounding boxes output from the network and keep only the # ones with high confidence scores. Assign the box's class label as the class with the highest score. classIds = [] confidences = [] boxes = [] for out in outs: if(args.showText): print("out.shape : ", out.shape) for detection in out: # if detection[4]>0.001: scores = detection[5:] classId = np.argmax(scores) # if scores[classId]>confThreshold: confidence = scores[classId] if detection[4] >= confThreshold: if(args.showText): print('obj score: ', detection[4], " - confidence:", scores[classId], " - thres : ", confThreshold) #print(detection) if confidence >= confThreshold: center_x = int(detection[0] * frameWidth) center_y = int(detection[1] * frameHeight) width = int(detection[2] * frameWidth) height = int(detection[3] * frameHeight) left = int(center_x - width / 2) top = int(center_y - height / 2) classIds.append(classId) confidences.append(float(confidence)) boxes.append([left, top, width, height]) # cv.rectangle(frame, (left, top), (left+width, top+height), (255, 0, 255),2) # cv.imshow('test', frame) # cv.waitKey(1) # Perform non maximum suppression to eliminate redundant overlapping boxes with # lower confidences. indices = cv.dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold) rests =[] for i in indices: # Fix for OpenCV 4.x compatibility idx = i[0] if isinstance(i, (list, tuple, np.ndarray)) and len(i) > 0 else i box = boxes[idx] left = box[0] top = box[1] width = box[2] height = box[3] rests.append([classIds[idx], left, top, width, height, frameWidth, frameHeight]) if(showimg): drawPred(frame, classIds[idx], confidences[idx], left, top, left + width, top + height) return rests def food_classifier_Json(image): # do somthing print(args.showText) locations = food_classifier_pipeline(frame=image) #[(2321, 0, 0, 10, 10)] # list of (id, rect) from classfication jsons = [] for j,location in enumerate(locations): class_id, x, y, width, height, framewidth, frameheight =location res_json = {} res_json["ClassID"] = classes_codes[class_id] # code , class_id (training class) res_json["ClassName"] = classes[class_id] res_json["x"] = int(x) res_json["y"] = int(y) res_json["w"] = int(width) res_json["h"] = int(height) res_json["framewidth"] = int(framewidth) res_json["frameheight"]= int(frameheight) jsons.append(res_json) print(json.dumps(jsons,ensure_ascii=False)) return json.dumps(jsons,ensure_ascii=False) def food_classifier_pipeline(frame): # Create a 4D blob from a frame. blob = cv.dnn.blobFromImage(frame, 1 / 255, (inpWidth, inpHeight), [0, 0, 0], 1, crop=False) # Sets the input to the network net.setInput(blob) # Runs the forward pass to get output of the output layers outs = net.forward(getOutputsNames(net)) final_infos = postprocess(frame, outs) return final_infos # Process inputs def main(main_args): winName = 'Food Classification Results' #cv.namedWindow(winName, cv.WINDOW_AUTOSIZE) m_startFrame = np.maximum(0, config.Video_Start_Frame) outputFile = "yolo_out_py.avi" if (main_args.image): # Open the image file if not os.path.isfile(main_args.image): print("Input image file ", main_args.image, " doesn't exist") sys.exit(1) cap = cv.VideoCapture(main_args.image) outputFile = args.image[:-4] + '_yolo_out_py.jpg' elif (main_args.video): # Open the video file if not os.path.isfile(main_args.video): print("Input video file ", main_args.video, " doesn't exist") sys.exit(1) cap = cv.VideoCapture(main_args.video) cap.set(cv.CAP_PROP_POS_FRAMES, m_startFrame) outputFile = main_args.video[:-4] + '_yolo_out_py.avi' else: # Webcam input cap = cv.VideoCapture(0) # Get the video writer initialized to save the output video if (not main_args.image): vid_writer = cv.VideoWriter(outputFile, cv.VideoWriter_fourcc('M', 'J', 'P', 'G'), 30, (round(cap.get(cv.CAP_PROP_FRAME_WIDTH)), round(cap.get(cv.CAP_PROP_FRAME_HEIGHT)))) pcontinue = True while pcontinue: # get frame from the video hasFrame, frame = cap.read() # Stop the program if reached end of video if not hasFrame: print("Done processing !!!") print("Output file is stored as ", outputFile) # if(main_args.ps): # cv.waitKey(0) #else: # cv.waitKey(1) #break # Create a 4D blob from a frame. blob = cv.dnn.blobFromImage(frame, 1 / 255, (inpWidth, inpHeight), [0, 0, 0], 1, crop=False) # Sets the input to the network net.setInput(blob) # Runs the forward pass to get output of the output layers outs = net.forward(getOutputsNames(net)) if main_args.showText: print(getOutputsNames(net)) postprocess(frame, outs, showimg=True) # Put efficiency information. The function getPerfProfile returns the overall time for inference(t) and the timings for each of the layers(in layersTimes) if main_args.showText: t, _ = net.getPerfProfile() label = 'Inference time: %.2f ms' % (t * 1000.0 / cv.getTickFrequency()) print(label) cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255)) # Write the frame with the detection boxes if (main_args.image): cv.imwrite(outputFile, frame.astype(np.uint8)); else: vid_writer.write(frame.astype(np.uint8)) #cv.imshow(winName, frame) #cv.waitKey(1) pcontinue=False if __name__ == "__main__": main(main_args=args)