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| import cv2 as cv | |
| import numpy as np | |
| from tts import * | |
| # Distance constants | |
| KNOWN_DISTANCE = 45 #INCHES | |
| PERSON_WIDTH = 16 #INCHES | |
| MOBILE_WIDTH = 3.0 #INCHES | |
| CHAIR_WIDTH = 20.0 | |
| text1 = "" | |
| text2 = "" | |
| # Object detector constant | |
| CONFIDENCE_THRESHOLD = 0.4 | |
| NMS_THRESHOLD = 0.3 | |
| # colors for object detected | |
| COLORS = [(255,0,0),(255,0,255),(0, 255, 255), (255, 255, 0), (0, 255, 0), (255, 0, 0)] | |
| GREEN =(0,255,0) | |
| BLACK =(0,0,0) | |
| # defining fonts | |
| FONTS = cv.FONT_HERSHEY_COMPLEX | |
| # getting class names from classes.txt file | |
| class_names = [] | |
| with open("classes.txt", "r") as f: | |
| class_names = [cname.strip() for cname in f.readlines()] | |
| # setttng up opencv net | |
| yoloNet = cv.dnn.readNet('yolov4-tiny.weights', 'yolov4-tiny.cfg') | |
| yoloNet.setPreferableBackend(cv.dnn.DNN_BACKEND_CUDA) | |
| yoloNet.setPreferableTarget(cv.dnn.DNN_TARGET_CUDA_FP16) | |
| model = cv.dnn_DetectionModel(yoloNet) | |
| model.setInputParams(size=(416, 416), scale=1/255, swapRB=True) | |
| # object detector funciton /method | |
| def object_detector(image): | |
| classes, scores, boxes = model.detect(image, CONFIDENCE_THRESHOLD, NMS_THRESHOLD) | |
| # creating empty list to add objects data | |
| data_list =[] | |
| for (classid, score, box) in zip(classes, scores, boxes): | |
| x1,y1,x2,y2 = box | |
| center_x, center_y = ( x1 + x2 ) / 2, ( y1 + y2 ) / 2 | |
| height, width, channels = image.shape | |
| # print(x1,y1,x2,y2) | |
| # define color of each, object based on its class id | |
| if center_x <= width/3: | |
| W_pos = "left" | |
| elif center_x <= (width/3 * 2): | |
| W_pos = "center" | |
| else: | |
| W_pos = "right" | |
| if center_y <= height/3: | |
| H_pos = "top" | |
| elif center_y <= (height/3 * 2): | |
| H_pos = "mid" | |
| else: | |
| H_pos = "bottom" | |
| text1 = W_pos | |
| text2 = H_pos | |
| color= COLORS[int(classid) % len(COLORS)] | |
| label = "%s : %f" % (class_names[classid], score) | |
| # draw rectangle on and label on object | |
| cv.rectangle(image, box, color, 2) | |
| cv.putText(image, label, (box[0], box[1]-14), FONTS, 0.5, color, 2) | |
| # getting the data | |
| # 1: class name | |
| # 2: object width in pixels, | |
| # 3: position where have to draw text(distance) | |
| print("objects identified status") | |
| print("person identified : ",classid == 0) | |
| print("mobile identified : ",classid == 67) | |
| print("chair identified : ",classid == 56) | |
| # getting the data | |
| # 1: class name 2: object width in pixels, 3: position where have to draw text(distance) | |
| if classid == 0: # person class id | |
| data_list.append([class_names[classid], box[2], (box[0], box[1]-2),text1,text2]) | |
| elif classid == 67: | |
| data_list.append([class_names[classid], box[2], (box[0], box[1]-2),text1,text2]) | |
| elif classid == 56: | |
| data_list.append([class_names[classid], box[2], (box[0], box[1]-2),text1,text2]) | |
| # if you want inclulde more classes then you have to simply add more [elif] statements here | |
| # returning list containing the object data. | |
| return data_list | |
| def focal_length_finder (measured_distance, real_width, width_in_rf): | |
| focal_length = (width_in_rf * measured_distance) / real_width | |
| return focal_length | |
| # distance finder function | |
| def distance_finder(focal_length, real_object_width, width_in_frmae): | |
| distance = (real_object_width * focal_length) / width_in_frmae | |
| return distance | |
| # reading the reference image from dir | |
| ref_person = cv.imread('ReferenceImages/image14.png') | |
| ref_mobile = cv.imread('ReferenceImages/image4.png') | |
| ref_chair = cv.imread('ReferenceImages/image22.png') | |
| mobile_data = object_detector(ref_mobile) | |
| mobile_width_in_rf = mobile_data[1][1] | |
| person_data = object_detector(ref_person) | |
| person_width_in_rf = person_data[0][1] | |
| chair_data = object_detector(ref_person) | |
| chair_width_in_rf = chair_data[0][1] | |
| # print(f"Person width in pixels : {person_width_in_rf} mobile width in pixel: {mobile_width_in_rf}") | |
| # finding focal length | |
| focal_person = focal_length_finder(KNOWN_DISTANCE, PERSON_WIDTH, person_width_in_rf) | |
| focal_mobile = focal_length_finder(KNOWN_DISTANCE, MOBILE_WIDTH, mobile_width_in_rf) | |
| focal_chair = focal_length_finder(KNOWN_DISTANCE, CHAIR_WIDTH, chair_width_in_rf) | |
| #d[] | |
| def get_frame_output(frame, frame_cnt): | |
| output_text_file = open('output_text.txt','w') | |
| data = object_detector(frame) | |
| for d in data: | |
| if d[0] =='person': | |
| distance = distance_finder(focal_person, PERSON_WIDTH, d[1]) | |
| x, y = d[2] | |
| elif d[0] =='cell phone': | |
| distance = distance_finder (focal_mobile, MOBILE_WIDTH, d[1]) | |
| x, y = d[2] | |
| elif d[0] == 'chair': | |
| distance = distance_finder (focal_chair, CHAIR_WIDTH, d[1]) | |
| x, y = d[2] | |
| text1,text2=d[3],d[4] | |
| cv.rectangle(frame, (x, y-3), (x+150, y+23),BLACK,-1 ) | |
| cv.putText(frame, f'Dis: {round(distance,2)} inch', (x+5,y+13), FONTS, 0.48, GREEN, 2) | |
| OUTPUTtEXT="" | |
| if distance > 100: | |
| OUTPUTtEXT = "Get closer" | |
| elif (round(distance) > 50) and (text2 == "mid"): | |
| OUTPUTtEXT="Go straight" | |
| else: | |
| OUTPUTtEXT = (str(d[0]) + " " + str(int(round(distance,1))) +" inches"+" take left or right") | |
| output_text_file.write(OUTPUTtEXT) | |
| output_text_file.write("\n") | |
| output_text_file.close() | |
| return frame |