~speedprog/mtg/mtg_card_detector.git

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			@@ -0,0 +1,186 @@
			import cv2
			import numpy as np
			import os
			import sys


			# Disclaimer: majority of the basic framework in this file is modified from the following tutorial:
			# https://www.learnopencv.com/deep-learning-based-object-detection-using-yolov3-with-opencv-python-c/


			# Get the names of the output layers
			def get_outputs_names(net):
			# Get the names of all the layers in the network
			layers_names = net.getLayerNames()
			# Get the names of the output layers, i.e. the layers with unconnected outputs
			return [layers_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]


			# Remove the bounding boxes with low confidence using non-maxima suppression
			def postprocess(frame, outs, classes, thresh_conf, thresh_nms):
			frame_height = frame.shape[0]
			frame_width = frame.shape[1]

			# Scan through all the bounding boxes output from the network and keep only the
			# ones with high confidence scores. Assign the box's class label as the class with the highest score.
			class_ids = []
			confidences = []
			boxes = []
			for out in outs:
			for detection in out:
			scores = detection[5:]
			class_id = np.argmax(scores)
			confidence = scores[class_id]
			if confidence > thresh_conf:
			center_x = int(detection[0] * frame_width)
			center_y = int(detection[1] * frame_height)
			width = int(detection[2] * frame_width)
			height = int(detection[3] * frame_height)
			left = int(center_x - width / 2)
			top = int(center_y - height / 2)
			class_ids.append(class_id)
			confidences.append(float(confidence))
			boxes.append([left, top, width, height])

			# Perform non maximum suppression to eliminate redundant overlapping boxes with
			# lower confidences.
			indices = cv2.dnn.NMSBoxes(boxes, confidences, thresh_conf, thresh_nms)
			for i in indices:
			i = i[0]
			box = boxes[i]
			left = box[0]
			top = box[1]
			width = box[2]
			height = box[3]
			draw_pred(frame, class_ids[i], classes, confidences[i], left, top, left + width, top + height)


			# Draw the predicted bounding box
			def draw_pred(frame, class_id, classes, conf, left, top, right, bottom):
			# Draw a bounding box.
			cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255))

			label = '%.2f' % conf

			# Get the label for the class name and its confidence
			if classes:
			assert (class_id < len(classes))
			label = '%s:%s' % (classes[class_id], label)

			# Display the label at the top of the bounding box
			label_size, base_line = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
			top = max(top, label_size[1])
			cv2.putText(frame, label, (left, top), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255))


			def detect_frame(net, classes, img, thresh_conf=0.5, thresh_nms=0.4, in_dim=(416, 416), out_path=None):
			# Create a 4D blob from a frame.
			blob = cv2.dnn.blobFromImage(img, 1 / 255, in_dim, [0, 0, 0], 1, crop=False)

			# Sets the input to the network
			net.setInput(blob)

			# Runs the forward pass to get output of the output layers
			outs = net.forward(get_outputs_names(net))

			# Remove the bounding boxes with low confidence
			postprocess(img, outs, classes, thresh_conf, thresh_nms)

			# Put efficiency information. The function getPerfProfile returns the
			# overall time for inference(t) and the timings for each of the layers(in layersTimes)
			t, _ = net.getPerfProfile()
			label = 'Inference time: %.2f ms' % (t * 1000.0 / cv2.getTickFrequency())
			cv2.putText(img, label, (0, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))

			if out_path is not None:
			cv2.imwrite(out_path, img.astype(np.uint8))


			def detect_video(net, classes, capture, thresh_conf=0.5, thresh_nms=0.4, in_dim=(416, 416), out_path=None):
			if out_path is not None:
			vid_writer = cv2.VideoWriter(out_path, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), 30,
			(round(capture.get(cv2.CAP_PROP_FRAME_WIDTH)),
			round(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))))
			while True:
			ret, frame = capture.read()
			if not ret:
			# End of video
			print("End of video. Press any key to exit")
			cv2.waitKey(0)
			break
			'''
			# Create a 4D blob from a frame.
			blob = cv2.dnn.blobFromImage(frame, 1 / 255, in_dim, [0, 0, 0], 1, crop=False)

			# Sets the input to the network
			net.setInput(blob)

			# Runs the forward pass to get output of the output layers
			outs = net.forward(get_outputs_names(net))

			# Remove the bounding boxes with low confidence
			postprocess(frame, outs, classes, thresh_conf, thresh_nms)

			# Put efficiency information. The function getPerfProfile returns the
			# overall time for inference(t) and the timings for each of the layers(in layersTimes)
			t, _ = net.getPerfProfile()
			label = 'Inference time: %.2f ms' % (t * 1000.0 / cv2.getTickFrequency())
			cv2.putText(frame, label, (0, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))
			'''
			detect_frame(net, classes, frame,
			thresh_conf=thresh_conf, thresh_nms=thresh_nms, in_dim=in_dim, out_path=None)
			cv2.imshow('result', frame)
			if out_path is not None:
			vid_writer.write(frame.astype(np.uint8))
			cv2.waitKey(1)

			if out_path is not None:
			vid_writer.release()
			cv2.destroyAllWindows()
			pass


			def main():
			# Specify paths for all necessary files
			test_path = '../data/test1.mp4'
			weight_path = 'weights/second_general/tiny_yolo_final.weights'
			cfg_path = 'cfg/tiny_yolo.cfg'
			class_path = "data/obj.names"
			out_dir = 'out'
			if not os.path.isfile(test_path):
			print('The test file %s doesn\'t exist!' % os.path.abspath(test_path))
			if not os.path.isfile(weight_path):
			print('The weight file %s doesn\'t exist!' % os.path.abspath(test_path))
			if not os.path.isfile(cfg_path):
			print('The config file %s doesn\'t exist!' % os.path.abspath(test_path))
			if not os.path.isfile(class_path):
			print('The class file %s doesn\'t exist!' % os.path.abspath(test_path))

			# Setup
			# Read class names from text file
			with open(class_path, 'r') as f:
			classes = [line.strip() for line in f.readlines()]
			# Load up the neural net using the config and weights
			net = cv2.dnn.readNetFromDarknet(cfg_path, weight_path)
			#net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
			#net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)

			# Save the detection result if out_dir is provided
			if out_dir is None or out_dir == '':
			out_path = None
			else:
			out_path = out_dir + '/' + os.path.split(test_path)[1]
			# Check if test file is image or video
			test_ext = test_path[test_path.find('.') + 1:]
			if test_ext in ['jpg', 'jpeg', 'bmp', 'png', 'tiff']:
			img = cv2.imread(test_path)
			detect_frame(net, classes, img, out_path=out_path)
			else:
			capture = cv2.VideoCapture(test_path)
			detect_video(net, classes, capture, out_path=out_path)
			capture.release()
			pass


			if __name__ == '__main__':
			main()

	README.md	8 ●●●●● patch \| view \| raw \| blame \| history
	cfg/tiny_yolo.cfg	4 ●●●●● patch \| view \| raw \| blame \| history
	data/obj.data	2 ●●●●● patch \| view \| raw \| blame \| history
	opencv_dnn.py	186 ●●●●● patch \| view \| raw \| blame \| history

			@@ -73,4 +73,10 @@

			Bad news, I couldn't find any repo that has python wrapper for darknet to pursue this project further. There is a [python example](https://github.com/AlexeyAB/darknet/blob/master/darknet.py) in the original repo of this fork, but [it doesn't support video input](https://github.com/AlexeyAB/darknet/issues/955). Other darknet repos are in the same situation.

			I suppose there is a poor man's alternative - feed individual frames from the video into the detection script for image. I'll have to give it a shot.
			I suppose there is a poor man's alternative - feed individual frames from the video into the detection script for image. I'll have to give it a shot.


			## Sept 14th, 2018
			--------------------

			Thankfully, OpenCV had an implementation for DNN, which supports YOLO as well. They have done quite an amazing job, and the speed isn't too bad, either. I can score about 20~25fps on my tiny YOLO, without using GPU.

			@@ -115,11 +115,11 @@
			activation=linear

			[region]
			anchors = 118.3429,137.0897, 95.8160,181.9724, 140.4955,166.7423, 112.7262,220.6808, 129.2741,198.9876, 159.0679,197.4912, 138.1861,243.0256, 167.4683,229.0091, 165.0264,255.0887
			anchors = 118.3429,137.0897, 95.8160,181.9724, 140.4955,166.7423, 112.7262,220.6808, 129.2741,198.9876
			bias_match=1
			classes=1
			coords=4
			num=9
			num=5
			softmax=1
			jitter=.2
			rescore=1

			@@ -1,5 +1,5 @@
			classes= 1
			train = train.txt
			valid = test.txt
			names = obj.names
			names = data/obj.names
			backup = backup/