yolo.py

import numpy as np
import time
import cv2
import imutils
from imutils.video import VideoStream,FPS


INPUT_FILE = "poker.mp4"
OUTPUT_FILE='output.avi'
LABELS_FILE='cards.names'
CONFIG_FILE='yolo_tiny-cards.cfg'
WEIGHTS_FILE='yolo-tiny-cards_best.weights'
CONFIDENCE_THRESHOLD=0.75

H=None
W=None

fps = FPS().start()

LABELS = open(LABELS_FILE).read().strip().split("\n")

np.random.seed(4)
COLORS = np.random.randint(0, 255, size=(len(LABELS), 3),
  dtype="uint8")


net = cv2.dnn.readNetFromDarknet(CONFIG_FILE, WEIGHTS_FILE)
model = cv2.dnn_DetectionModel(net)

vs = cv2.VideoCapture(INPUT_FILE)
print(vs)


# determine only the *output* layer names that we need from YOLO
ln = net.getLayerNames()
ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]
cnt =0;
while True:
  cnt+=1
  try:
    (grabbed, image) = vs.read()
  except:
    break
  blob = cv2.dnn.blobFromImage(image, 1 / 255.0, (416, 416),
    swapRB=True, crop=False)
  net.setInput(blob)
  if W is None or H is None:
    (H, W) = image.shape[:2]
  layerOutputs = net.forward(ln)

  output1 = layerOutputs[0]
  output2 = layerOutputs[1]

  (output2[np.max(output2[:,5:],axis=1)>CONFIDENCE_THRESHOLD].shape)



  # initialize our lists of detected bounding boxes, confidences, and
  # class IDs, respectively
  boxes = []
  confidences = []
  classIDs = []

  # loop over each of the layer outputs
  for output in layerOutputs:
    # loop over each of the detections
    for detection in output:
      # extract the class ID and confidence (i.e., probability) of
      # the current object detection
      scores = detection[5:]
      classID = np.argmax(scores)
      confidence = scores[classID]

      # filter out weak predictions by ensuring the detected
      # probability is greater than the minimum probability
      if confidence > CONFIDENCE_THRESHOLD:
        # scale the bounding box coordinates back relative to the
        # size of the image, keeping in mind that YOLO actually
        # returns the center (x, y)-coordinates of the bounding
        # box followed by the boxes' width and height
        box = detection[0:4] * np.array([W, H, W, H])
        (centerX, centerY, width, height) = box.astype("int")

        # use the center (x, y)-coordinates to derive the top and
        # and left corner of the bounding box
        x = int(centerX - (width / 2))
        y = int(centerY - (height / 2))

        # update our list of bounding box coordinates, confidences,
        # and class IDs
        boxes.append([x, y, int(width), int(height)])
        confidences.append(float(confidence))
        classIDs.append(classID)

  # apply non-maxima suppression to suppress weak, overlapping bounding
  # boxes
  idxs = cv2.dnn.NMSBoxes(boxes, confidences, CONFIDENCE_THRESHOLD,
    CONFIDENCE_THRESHOLD)

  # ensure at least one detection exists
  if len(idxs) > 0:
    # loop over the indexes we are keeping
    for i in idxs.flatten():
      # extract the bounding box coordinates
      (x, y) = (boxes[i][0], boxes[i][1])
      (w, h) = (boxes[i][2], boxes[i][3])

      color = [int(c) for c in COLORS[classIDs[i]]]

      cv2.rectangle(image, (x, y), (x + w, y + h), color, 2)
      text = "{}: {:.4f}".format(LABELS[classIDs[i]], confidences[i])
      cv2.putText(image, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX,
        0.5, color, 2)

  # show the output image
  cv2.imshow("output", cv2.resize(image,(800, 600)))
  fps.update()
  key = cv2.waitKey(1) & 0xFF
  if key == ord("q"):
    break

fps.stop()

print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))

# do a bit of cleanup
cv2.destroyAllWindows()

# release the file pointers
print("[INFO] cleaning up...")
vs.release()