suvrashaw / yolo-flask

A Flask web app that detects humans in a video using the YOLOv3 algorithm.

YOLOv3 Human Detection

There is an object detection algorithm called YOLO (You Only Live Once). In this case, the entire image is processed by a single neural network. Image regions are divided up by this network. It then forecast boxes and probabilities for each region. A probability distribution is applied to these bounding boxes in order to determine their weighting.

• Firstly, we import the essential libraries.

  import cv2 #for image processing
  import numpy as np #for numeric calculations
  # from google.colab.patches import cv2_imshow

• Then, we import the datasets. Download the full dataset archive from here. Also, we store the final or output layer of the architecture because we are concerned with the outputs.

  net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
  layer_names = net.getLayerNames()
  output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
  with open("coco.names", "r") as f: classes = [line.strip() for line in f.readlines()]
  colors = np.random.uniform(0, 255, size=(len(classes), 3))

• Now, provide an input video file. This project's sample video can be downloaded from here.

  cap = cv2.VideoCapture("sample.mp4")

  

• Set the label font for YOLO detction.

  font = cv2.FONT_HERSHEY_PLAIN

• Set the time, using the time library.

  import time
  starting_time = time.time()

• Set the output file configurations.

  result = cv2.VideoWriter('output.mp4', cv2.VideoWriter_fourcc(*'MJPG'),25, (640, 352))

• The loop starts to ork on the input video, frame by frame.

  frame_id = 0
  while True: #infinite loop
      _, frame = cap.read() #reading frame by frame
      frame_id += 1 #interator to calculate the frames
      height, width, channels = frame.shape #configuration of each frame
      blob = cv2.dnn.blobFromImage(frame, 0.00392, (416, 416), (0, 0, 0), True, crop=False) #416x416 resizing + channel reorder
      net.setInput(blob) #fed to YOLO network
      outs = net.forward(output_layers) #output from YOLO network, detected images
      class_ids = [] #coco.name object
      confidences = [] #detection accuracy
      boxes = [] #boxes around a person
      for out in outs: #object detection
          for detection in out: #detection = outs[i]
              scores = detection[5:]
  						#The actual confidence intervals are a percentage value between 0 and 1 for all 80 classes.
              class_id = np.argmax(scores)
              confidence = scores[class_id]
              if confidence > 0.2:
                  center_x = int(detection[0] * width)
                  center_y = int(detection[1] * height)
                  w = int(detection[3] * width)
                  h = int(detection[3] * height)
                  x = int(center_x - w / 1.8)
                  y = int(center_y - h / 1.8)
                  boxes.append([x, y, w, h]) #setting calculated vertices
                  confidences.append(float(confidence))
                  class_ids.append(class_id)
      indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.4, 0.3) #remove overlapping boxes
      for i in range(len(boxes)): #drawing the box
          if i in indexes:
              x, y, w, h = boxes[i] #getting vertices for the box
              label = str(classes[class_ids[i]]) #getting detected labels
              confidence = confidences[i] #getting detection percents
              color = colors[class_ids[i]]
              cv2.rectangle(frame, (x+10, y+10), (x + w, y + h), color, 2) #setting the box
              cv2.putText(frame, label + " " + str(round(confidence, 2)), (x, y + 30), font, 2, color, 2) #editing the box into the frame(s) image
      elapsed_time = time.time() - starting_time #calculating the time passed
      fps = frame_id / elapsed_time #calculating the preset frame
      cv2.putText(frame, "FPS: " + str(round(fps, 2)), (10, 50), font, 2, (0, 0, 0), 3) #editing the frame number on the image(s)
      result.write(frame) #rendering the frame(s) into the output video 
      # cv2_imshow(frame)
      if cv2.waitKey(1) ==ord('q'):
          break
  cap.release()
  cv2.destroyAllWindows()