PySimpleGUI/YoloObjectDetection/yolo_video.py

# USAGE
# python yolo_video.py --input videos/airport.mp4 --output output/airport_output.avi --yolo yolo-coco

# import the necessary packages
import numpy as np
# import argparse
import imutils
import time
import cv2
import os
import PySimpleGUI as sg

i_vid = r'videos\car_chase_01.mp4'
# o_vid = r'videos\car_chase_01_out.mp4'
y_path = r'yolo-coco'
layout = 	[
		[sg.Text('YOLO Video Player', size=(18,1), font=('Any',18),text_color='#1c86ee' ,justification='left')],
		[sg.Text('Path to input video'), sg.In(i_vid,size=(40,1), key='input'), sg.FileBrowse()],
		# [sg.Text('Path to output video'), sg.In(o_vid,size=(40,1), key='output'), sg.FileSaveAs()],
		[sg.Text('Yolo base path'), sg.In(y_path,size=(40,1), key='yolo'), sg.FolderBrowse()],
		[sg.Text('Confidence'), sg.Slider(range=(0,1),orientation='h', resolution=.1, default_value=.5, size=(15,15), key='confidence')],
		[sg.Text('Threshold'), sg.Slider(range=(0,1), orientation='h', resolution=.1, default_value=.3, size=(15,15), key='threshold')],
		[sg.OK(), sg.Cancel()]
			]

win = sg.Window('YOLO Video',
				default_element_size=(14,1),
				text_justification='right',
				auto_size_text=False).Layout(layout)
event, values = win.Read()
if event is None or event =='Cancel':
	exit()
args = values

win.Close()


# imgbytes = cv2.imencode('.png', image)[1].tobytes()  # ditto

# load the COCO class labels our YOLO model was trained on
labelsPath = os.path.sep.join([args["yolo"], "coco.names"])
LABELS = open(labelsPath).read().strip().split("\n")

# initialize a list of colors to represent each possible class label
np.random.seed(42)
COLORS = np.random.randint(0, 255, size=(len(LABELS), 3),
	dtype="uint8")

# derive the paths to the YOLO weights and model configuration
weightsPath = os.path.sep.join([args["yolo"], "yolov3.weights"])
configPath = os.path.sep.join([args["yolo"], "yolov3.cfg"])

# load our YOLO object detector trained on COCO dataset (80 classes)
# and determine only the *output* layer names that we need from YOLO
print("[INFO] loading YOLO from disk...")
net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)
ln = net.getLayerNames()
ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]

# initialize the video stream, pointer to output video file, and
# frame dimensions
vs = cv2.VideoCapture(args["input"])
writer = None
(W, H) = (None, None)

# try to determine the total number of frames in the video file
try:
	prop = cv2.cv.CV_CAP_PROP_FRAME_COUNT if imutils.is_cv2() \
		else cv2.CAP_PROP_FRAME_COUNT
	total = int(vs.get(prop))
	print("[INFO] {} total frames in video".format(total))

# an error occurred while trying to determine the total
# number of frames in the video file
except:
	print("[INFO] could not determine # of frames in video")
	print("[INFO] no approx. completion time can be provided")
	total = -1

# loop over frames from the video file stream
win_started = False
while True:
	# read the next frame from the file
	(grabbed, frame) = vs.read()

	# if the frame was not grabbed, then we have reached the end
	# of the stream
	if not grabbed:
		break

	# if the frame dimensions are empty, grab them
	if W is None or H is None:
		(H, W) = frame.shape[:2]

	# construct a blob from the input frame and then perform a forward
	# pass of the YOLO object detector, giving us our bounding boxes
	# and associated probabilities
	blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416),
		swapRB=True, crop=False)
	net.setInput(blob)
	start = time.time()
	layerOutputs = net.forward(ln)
	end = time.time()

	# 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 > args["confidence"]:
				# 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, args["confidence"],
		args["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])

			# draw a bounding box rectangle and label on the frame
			color = [int(c) for c in COLORS[classIDs[i]]]
			cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
			text = "{}: {:.4f}".format(LABELS[classIDs[i]],
				confidences[i])
			cv2.putText(frame, text, (x, y - 5),
				cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)

	# check if the video writer is None
	# if writer is None:
	# 	# initialize our video writer
	# 	fourcc = cv2.VideoWriter_fourcc(*"MJPG")
	# 	writer = cv2.VideoWriter(args["output"], fourcc, 30,
	# 		(frame.shape[1], frame.shape[0]), True)
    #
	# 	# some information on processing single frame
	# 	if total > 0:
	# 		elap = (end - start)
	# 		print("[INFO] single frame took {:.4f} seconds".format(elap))
	# 		print("[INFO] estimated total time to finish: {:.4f}".format(
	# 			elap * total))

	# write the output frame to disk
	# writer.write(frame)
	imgbytes = cv2.imencode('.png', frame)[1].tobytes()  # ditto

	if not win_started:
		win_started = True
		layout = [
			[sg.Text('Yolo Output')],
			[sg.Image(data=imgbytes, key='_IMAGE_')],
			[sg.Exit()]
		]
		win = sg.Window('YOLO Output',
						default_element_size=(14, 1),
						text_justification='right',
						auto_size_text=False).Layout(layout).Finalize()
		image_elem = win.FindElement('_IMAGE_')
	else:
		image_elem.Update(data=imgbytes)

	event, values = win.Read(timeout=0)
	if event is None or event == 'Exit':
		break


win.Close()

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