My code detects nothing

Python
1

I intend to detect social distance violation(uses YoLoV3). But my code below just opens the live video feed and detects nothing and neither does it makes the bounding boxes accordingly. Need help to make things work

# import the necessary packages

import numpy as np

import cv2

import math

import time

# Load Yolo Model

net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
# net = cv2.dnn.readNet("other\yolov3.weights", "other\yolov3.cfg")

classes = ["person"]

with open("coco.names", "r") as f:
# with open("other\coco.names", "r") as f:

    classes = [line.strip() for line in f.readlines()]

layer_names = net.getLayerNames()

# output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]

np.random.uniform(0, 255, size=(len(classes),3))

#Start video or live camera

# cap = cv2.VideoCapture('video.mp4')
# cap = cv2.VideoCapture('videos\pedestrians.mp4')

cap = cv2.VideoCapture(0)  

#cap = cv2.VideoCapture('http://192.168.43.1:8080//video')

#Euclidean distance for each video

def E_dist(p1, p2):

    return ((p1[0] - p2[0]) ** 2 +  (p1[1] - p2[1]) ** 2) ** 0.5

def isclose(p1, p2):

    c_d = E_dist(p1, p2)

    calib = (p1[1] + p2[1]) / 2 

    if 0 < c_d < 0.15 * calib:

        return 1

    elif 0 < c_d < 0.2 * calib:

        return 2

    else:

        return 0

    

height,width=(None,None)

q=0       

#Start working on video or camera

while(cap.isOpened()):

    # Capture frame-by-frame

    ret, img = cap.read()  

    # print(ret)  //commented out change 1

    if not ret:

        break

    if width is None or height is None: 

        height,width=img.shape[:2]

        q=width

    #height, width, channels = img.   

    img =img[0:height, 0:q]

    height,width=img.shape[:2]

    # Detecting objects 0.00392

    blob = cv2.dnn.blobFromImage(img,0.00392, (416, 416), (0,0,0), True, crop=False)

    net.setInput(blob)

    start = time.time()

    outs = net.forward(output_layers)

    end=time.time()

     # Showing informations on the screen

    class_ids = []

    confidences = []

    boxes = []

    for out in outs:

        for detection in out:

            scores = detection[5:]

            class_id = np.argmax(scores)

            confidence = scores[class_id]

            #0.5 is the threshold for confidence

            if confidence > 0.5:

                # Object detected

                #Purpose : Converts center coordinates to rectangle coordinates

                # x, y = midpoint of box

                center_x = int(detection[0] * width)

                center_y = int(detection[1] * height)

                 # w, h = width, height of the box

                w = int(detection[2] * width)

                h = int(detection[3] * height)

                # Rectangle coordinates

                x = int(center_x - w / 2)

                y = int(center_y - h / 2)

                boxes.append([x, y, w, h])

                confidences.append(float(confidence))

                class_ids.append(class_id)

    indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.5)

    #print(indexes)

    font = cv2.FONT_HERSHEY_SIMPLEX

    if len(indexes)>0:

        status=list()        

        idf = indexes.flatten()        

        close_pair = list()        

        s_close_pair = list()        

        center = list()        

        dist = list()        

        for i in idf:            

            (x, y) = (boxes[i][0], boxes[i][1])            

            (w, h) = (boxes[i][2], boxes[i][3])            

            center.append([int(x + w / 2), int(y + h / 2)])            

            status.append(0)            

        for i in range(len(center)):            

            for j in range(len(center)):                

                #compare the closeness of two values

                g=isclose(center[i], center[j])                

                if g ==1:                    

                    close_pair.append([center[i],center[j]])                    

                    status[i] = 1                    

                    status[j] = 1                    

                elif g == 2:                    

                    s_close_pair.append([center[i], center[j]])                    

                    if status[i] != 1:                        

                        status[i] = 2                        

                    if status[j] != 1:                        

                        status[j] = 2

        total_p = len(center)        

        low_risk_p = status.count(2)        

        high_risk_p = status.count(1)        

        safe_p = status.count(0)        

        kk = 0        

        for i in idf:            

            sub_img = img[10:170, 10:width - 10]            

            black_rect = np.ones(sub_img.shape, dtype=np.uint8)*0            

            res = cv2.addWeighted(sub_img, 0.77, black_rect,0.23, 1.0)

            img[10:170, 10:width - 10] = res           

            

            # adding text to image            

                      #(image,text,org( X coordinate value, Y coordinate value),font,fontScale,color,thikness)

            cv2.putText(img, "Social Distancing Detection - During COVID19 ", (255, 45),font, 1, (255, 255, 255), 2)

            cv2.putText(img, "Praveen's Work", (450, 200),font, 1, (0, 255, 255), 2)            

            #image = cv2.rectangle(image, start_point, end_point, color, thickness)

            cv2.rectangle(img, (20, 60), (625, 160), (170, 170, 170), 2)            

            cv2.putText(img, "Connecting lines shows closeness among people. ", (45, 80),font, 0.6, (255, 255, 0), 1)            

            cv2.putText(img, "YELLOW: CLOSE", (45, 110),font, 0.5, (0, 255, 255), 1)            

            cv2.putText(img, "RED: VERY CLOSE", (45, 130),font, 0.5, (0, 0, 255), 1)

            cv2.rectangle(img, (675, 60), (width -20, 160), (170, 170, 170), 2)            

            cv2.putText(img, "Bounding box shows the level of risk to the person.",(685, 80),font, 0.6, (255, 255, 0), 1)           

            

            cv2.putText(img, "DARK RED: HIGH RISK", (685, 110),font, 0.5, (0, 0, 150), 1)      

            cv2.putText(img, "ORANGE: LOW RISK", (685, 130),font, 0.5, (0, 120, 255), 1)

            cv2.putText(img, "GREEN: CONGRATULATIONS YOU ARE SAFE", (685, 150),font, 0.5, (0, 255, 0), 1)

            tot_str = "NUMBER OF PEOPLE: " + str(total_p)            

            high_str = "RED ZONE: " + str(high_risk_p)            

            low_str = "ORANGE ZONE: " + str(low_risk_p)            

            safe_str = "GREEN ZONE: " + str(safe_p)            

            #image ROI

            sub_img = img[height - 120:height-20, 0:500]

            #cv2.imshow("sub_img",sub_img)            

            black_rect = np.ones(sub_img.shape, dtype=np.uint8) * 0

            res = cv2.addWeighted(sub_img, 0.8, black_rect, 0.2, 1.0)

            img[height - 120:height-20, 0:500] = res

            cv2.putText(img, tot_str, (10, height - 75),font, 0.6, (255, 255, 255), 1)            

            cv2.putText(img, safe_str, (300, height - 75),font, 0.6, (0, 255, 0), 1)            

            cv2.putText(img, low_str, (10, height - 50),font, 0.6, (0, 120, 255), 1)            

            cv2.putText(img, high_str, (300, height - 50),font, 0.6, (0, 0, 150), 1)

            (x, y) = (boxes[i][0], boxes[i][1])            

            (w, h) = (boxes[i][2], boxes[i][3])        

            #color of the ractangle when is too close 

            if status[kk] == 1:                

                cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 150), 2)

            elif status[kk] == 0:                

                cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)

            else:

                cv2.rectangle(img, (x, y), (x + w, y + h), (0, 120, 255), 2)

            kk += 1

        for h in close_pair:            

            cv2.line(img, tuple(h[0]), tuple(h[1]), (0, 0, 255), 2)         

        for b in s_close_pair:

            cv2.line(img, tuple(b[0]), tuple(b[1]), (0, 255, 255), 2)

    cv2.imshow('image',img)

    if cv2.waitKey(1) & 0xFF == ord('q'):

        break

    cv2.waitKey(1)

    #FourCC code is passed as

    # fourcc = cv2.VideoWriter_fourcc(*'MJPG')
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    # fourcc = cv2.VideoWriter_fourcc(*'XVID')
    

    output = cv2.VideoWriter('output4.mp4',fourcc, 20.0, (img.shape[1], img.shape[0]))

    #cv2.imwrite("output1.mp4",img)

    #img = cv2.flip(img,0)

    output.write(img)

cap.release()

output.release()

cv2.destroyAllWindows()

# press 'q' to release the window.
2

first, i’ve no idea, why it doesnt show anything
(use python -m pdb my.py to debug it, line by line !)

then, some objections:

  • pinhole camera model !! (lookitup, gdam !) 10 pixels in 50 m distance (to camera) are something completely different than 10 pix at 50cm distance
    how do you even expect to measure anything sane w/o knowing or estimating the camera distance ?

  • please explain this:
    calib = (p1[1] + p2[1]) / 2
    if 0 < c_d < 0.15 * calib:
    …
    why correlate the avg center.y to distance ? makes no sense to me !

  • should’t you restrict it to special class_id’s (e.g. human, face) here ?
    distance between a bus & a stop sign is simply irrelevant here !