~speedprog/mtg/mtg_card_detector.git

			@@ -1,13 +1,176 @@
			import ast
			import collections
			import cv2
			import imagehash as ih
			import numpy as np
			from operator import itemgetter
			import os
			import sys
			import pandas as pd
			from PIL import Image
			import time

			from config import Config
			import fetch_data


			# 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/
			"""
			As of the current version, the YOLO network has been removed from this code during optimization.
			It was found out that YOLO was adding too much processing delay, and the benefits from using it couldn't justify
			such heavy cost.
			If you're interested to see the implementation using YOLO, please check out the previous commit:
			https://github.com/hj3yoo/mtg_card_detector/tree/dea64611730c84a59c711c61f7f80948f82bcd31
			"""


			def calc_image_hashes(card_pool, save_to=None, hash_size=32, highfreq_factor=4):
			"""
			Calculate perceptual hash (pHash) value for each cards in the database, then store them if needed
			:param card_pool: pandas dataframe containing all card information
			:param save_to: path for the pickle file to be saved
			:param hash_size: param for pHash algorithm
			:param highfreq_factor: param for pHash algorithm
			:return: pandas dataframe
			"""
			# Since some double-faced cards may result in two different cards, create a new dataframe to store the result
			new_pool = pd.DataFrame(columns=list(card_pool.columns.values))
			new_pool['card_hash'] = np.NaN
			#new_pool['art_hash'] = np.NaN
			for ind, card_info in card_pool.iterrows():
			if ind % 100 == 0:
			print('Calculating hashes: %dth card' % ind)

			card_names = []
			# Double-faced cards have a different json format than normal cards
			if card_info['layout'] in ['transform', 'double_faced_token']:
			if isinstance(card_info['card_faces'], str):
			card_faces = ast.literal_eval(card_info['card_faces'])
			else:
			card_faces = card_info['card_faces']
			for i in range(len(card_faces)):
			card_names.append(card_faces[i]['name'])
			else: # if card_info['layout'] == 'normal':
			card_names.append(card_info['name'])

			for card_name in card_names:
			# Fetch the image - name can be found based on the card's information
			card_info['name'] = card_name
			img_name = '%s/card_img/png/%s/%s_%s.png' % (Config.data_dir, card_info['set'],
			card_info['collector_number'],
			fetch_data.get_valid_filename(card_info['name']))
			card_img = cv2.imread(img_name)

			# If the image doesn't exist, download it from the URL
			if card_img is None:
			fetch_data.fetch_card_image(card_info,
			out_dir='%s/card_img/png/%s' % (Config.data_dir, card_info['set']))
			card_img = cv2.imread(img_name)
			if card_img is None:
			print('WARNING: card %s is not found!' % img_name)

			# Compute value of the card's perceptual hash, then store it to the database
			'''
			img_art = Image.fromarray(card_img[121:580, 63:685]) # For 745*1040 size card image
			art_hash = ih.phash(img_art, hash_size=hash_size, highfreq_factor=highfreq_factor)
			card_info['art_hash'] = art_hash
			'''
			img_card = Image.fromarray(card_img)
			card_hash = ih.phash(img_card, hash_size=hash_size, highfreq_factor=highfreq_factor)
			card_info['card_hash'] = card_hash
			new_pool.loc[0 if new_pool.empty else new_pool.index.max() + 1] = card_info

			# Remove uselesss fields, then pickle it if needed
			new_pool = new_pool[['artist', 'border_color', 'collector_number', 'color_identity', 'colors', 'flavor_text',
			'image_uris', 'mana_cost', 'legalities', 'name', 'oracle_text', 'rarity', 'type_line',
			'set', 'set_name', 'power', 'toughness', 'art_hash', 'card_hash']]
			if save_to is not None:
			new_pool.to_pickle(save_to)
			return new_pool


			# www.pyimagesearch.com/2014/08/25/4-point-opencv-getperspective-transform-example/
			def order_points(pts):
			"""
			initialzie a list of coordinates that will be ordered such that the first entry in the list is the top-left,
			the second entry is the top-right, the third is the bottom-right, and the fourth is the bottom-left
			:param pts: array containing 4 points
			:return: ordered list of 4 points
			"""
			rect = np.zeros((4, 2), dtype="float32")

			# the top-left point will have the smallest sum, whereas
			# the bottom-right point will have the largest sum
			s = pts.sum(axis=1)
			rect[0] = pts[np.argmin(s)]
			rect[2] = pts[np.argmax(s)]

			# now, compute the difference between the points, the
			# top-right point will have the smallest difference,
			# whereas the bottom-left will have the largest difference
			diff = np.diff(pts, axis=1)
			rect[1] = pts[np.argmin(diff)]
			rect[3] = pts[np.argmax(diff)]

			# return the ordered coordinates
			return rect


			def four_point_transform(image, pts):
			"""
			Transform a quadrilateral section of an image into a rectangular area
			From: www.pyimagesearch.com/2014/08/25/4-point-opencv-getperspective-transform-example/
			:param image: source image
			:param pts: 4 corners of the quadrilateral
			:return: rectangular image of the specified area
			"""
			# obtain a consistent order of the points and unpack them
			# individually
			rect = order_points(pts)
			(tl, tr, br, bl) = rect

			# compute the width of the new image, which will be the
			# maximum distance between bottom-right and bottom-left
			# x-coordiates or the top-right and top-left x-coordinates
			widthA = np.sqrt(((br[0] - bl[0]) 2) + ((br[1] - bl[1]) 2))
			widthB = np.sqrt(((tr[0] - tl[0]) 2) + ((tr[1] - tl[1]) 2))
			maxWidth = max(int(widthA), int(widthB))

			# compute the height of the new image, which will be the
			# maximum distance between the top-right and bottom-right
			# y-coordinates or the top-left and bottom-left y-coordinates
			heightA = np.sqrt(((tr[0] - br[0]) 2) + ((tr[1] - br[1]) 2))
			heightB = np.sqrt(((tl[0] - bl[0]) 2) + ((tl[1] - bl[1]) 2))
			maxHeight = max(int(heightA), int(heightB))

			# now that we have the dimensions of the new image, construct
			# the set of destination points to obtain a "birds eye view",
			# (i.e. top-down view) of the image, again specifying points
			# in the top-left, top-right, bottom-right, and bottom-left
			# order
			dst = np.array([
			[0, 0],
			[maxWidth - 1, 0],
			[maxWidth - 1, maxHeight - 1],
			[0, maxHeight - 1]], dtype="float32")

			# compute the perspective transform matrix and then apply it
			mat = cv2.getPerspectiveTransform(rect, dst)
			warped = cv2.warpPerspective(image, mat, (maxWidth, maxHeight))

			# If the image is horizontally long, rotate it by 90
			if maxWidth > maxHeight:
			center = (maxHeight / 2, maxHeight / 2)
			mat_rot = cv2.getRotationMatrix2D(center, 270, 1.0)
			warped = cv2.warpAffine(warped, mat_rot, (maxHeight, maxWidth))

			# return the warped image
			return warped


			'''
			# The following functions are only used in conjunction with YOLO, and is deprecated:
			# - get_outputs_names()
			# - post_process()
			# - draw_pred()
			# Get the names of the output layers
			def get_outputs_names(net):
			# Get the names of all the layers in the network
			@@ -17,7 +180,8 @@


			# Remove the bounding boxes with low confidence using non-maxima suppression
			def postprocess(frame, outs, classes, thresh_conf, thresh_nms):
			# https://www.learnopencv.com/deep-learning-based-object-detection-using-yolov3-with-opencv-python-c/
			def post_process(frame, outs, thresh_conf, thresh_nms):
			frame_height = frame.shape[0]
			frame_width = frame.shape[1]

			@@ -42,17 +206,11 @@
			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)
			# Perform non maximum suppression to eliminate redundant overlapping boxes with lower confidences.
			indices = [ind[0] for ind in cv2.dnn.NMSBoxes(boxes, confidences, thresh_conf, thresh_nms)]

			ret = [[class_ids[i], confidences[i], boxes[i]] for i in indices]
			return ret


			# Draw the predicted bounding box
			@@ -71,114 +229,362 @@
			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)
			def remove_glare(img):
			"""
			Reduce the effect of glaring in the image
			Inspired from:
			http://www.amphident.de/en/blog/preprocessing-for-automatic-pattern-identification-in-wildlife-removing-glare.html
			The idea is to find area that has low saturation but high value, which is what a glare usually look like.
			:param img: source image
			:return: corrected image with glaring smoothened out
			"""
			img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
			_, s, v = cv2.split(img_hsv)
			non_sat = (s < 32) * 255 # Find all pixels that are not very saturated

			# Sets the input to the network
			net.setInput(blob)
			# Slightly decrease the area of the non-satuared pixels by a erosion operation.
			disk = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
			non_sat = cv2.erode(non_sat.astype(np.uint8), disk)

			# Runs the forward pass to get output of the output layers
			outs = net.forward(get_outputs_names(net))
			# Set all brightness values, where the pixels are still saturated to 0.
			v[non_sat == 0] = 0
			# filter out very bright pixels.
			glare = (v > 200) * 255

			# Remove the bounding boxes with low confidence
			postprocess(img, outs, classes, thresh_conf, thresh_nms)
			# Slightly increase the area for each pixel
			glare = cv2.dilate(glare.astype(np.uint8), disk)
			glare_reduced = np.ones((img.shape[0], img.shape[1], 3), dtype=np.uint8) * 200
			glare = cv2.cvtColor(glare, cv2.COLOR_GRAY2BGR)
			corrected = np.where(glare, glare_reduced, img)
			return corrected

			# 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))

			def find_card(img, thresh_c=5, kernel_size=(3, 3), size_thresh=10000):
			"""
			Find contours of all cards in the image
			:param img: source image
			:param thresh_c: value of the constant C for adaptive thresholding
			:param kernel_size: dimension of the kernel used for dilation and erosion
			:param size_thresh: threshold for size (in pixel) of the contour to be a candidate
			:return: list of candidate contours
			"""
			# Typical pre-processing - grayscale, blurring, thresholding
			img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
			img_blur = cv2.medianBlur(img_gray, 5)
			img_thresh = cv2.adaptiveThreshold(img_blur, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 5, thresh_c)

			# Dilute the image, then erode them to remove minor noises
			kernel = np.ones(kernel_size, np.uint8)
			img_dilate = cv2.dilate(img_thresh, kernel, iterations=1)
			img_erode = cv2.erode(img_dilate, kernel, iterations=1)

			# Find the contour
			_, cnts, hier = cv2.findContours(img_erode, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
			if len(cnts) == 0:
			#print('no contours')
			return []

			# The hierarchy from cv2.findContours() is similar to a tree: each node has an access to the parent, the first child
			# their previous and next node
			# Using recursive search, find the uppermost contour in the hierarchy that satisfies the condition
			# The candidate contour must be rectangle (has 4 points) and should be larger than a threshold
			cnts_rect = []
			stack = [(0, hier[0][0])]
			while len(stack) > 0:
			i_cnt, h = stack.pop()
			i_next, i_prev, i_child, i_parent = h
			if i_next != -1:
			stack.append((i_next, hier[0][i_next]))
			cnt = cnts[i_cnt]
			size = cv2.contourArea(cnt)
			peri = cv2.arcLength(cnt, True)
			approx = cv2.approxPolyDP(cnt, 0.04 * peri, True)
			if size >= size_thresh and len(approx) == 4:
			cnts_rect.append(approx)
			else:
			if i_child != -1:
			stack.append((i_child, hier[0][i_child]))
			return cnts_rect


			def draw_card_graph(exist_cards, card_pool, f_len):
			"""
			Given the history of detected cards in the current and several previous frames, draw a simple graph
			displaying the detected cards with its confidence level
			:param exist_cards: History of all detected cards in the previous (f_len) frames
			:param card_pool: pandas dataframe of all card's information
			:param f_len: length of windows (in frames) to consider for confidence level
			:return:
			"""
			# Lots of constants to set the dimension of each elements
			w_card = 63 # Width of the card image displayed
			h_card = 88
			gap = 25 # Offset between each elements
			gap_sm = 10 # Small offset
			w_bar = 300 # Length of the confidence bar at 100%
			h_bar = 12
			txt_scale = 0.8
			n_cards_p_col = 4 # Number of cards displayed per one column
			w_img = gap + (w_card + gap + w_bar + gap) * 2 # Dimension of the entire graph (for 2 columns)
			h_img = 480
			img_graph = np.zeros((h_img, w_img, 3), dtype=np.uint8)
			x_anchor = gap
			y_anchor = gap

			i = 0

			# Cards are displayed from the most confident to the least
			# Confidence level is calculated by number of frames that the card was detected in
			for key, val in sorted(exist_cards.items(), key=itemgetter(1), reverse=True)[:n_cards_p_col * 2]:
			card_name = key[:key.find('(') - 1]
			card_set = key[key.find('(') + 1:key.find(')')]
			confidence = sum(val) / f_len
			card_info = card_pool[(card_pool['name'] == card_name) & (card_pool['set'] == card_set)].iloc[0]
			img_name = '%s/card_img/tiny/%s/%s_%s.png' % (Config.data_dir, card_info['set'],
			card_info['collector_number'],
			fetch_data.get_valid_filename(card_info['name']))
			# If the card image is not found, just leave it blank
			if os.path.exists(img_name):
			card_img = cv2.imread(img_name)
			else:
			card_img = np.ones((h_card, w_card))

			# Insert the card image, card name, and confidence bar to the graph
			img_graph[y_anchor:y_anchor + h_card, x_anchor:x_anchor + w_card] = card_img
			cv2.putText(img_graph, '%s (%s)' % (card_name, card_set),
			(x_anchor + w_card + gap, y_anchor + gap_sm + int(txt_scale * 25)), cv2.FONT_HERSHEY_SIMPLEX,
			txt_scale, (255, 255, 255), 1)
			cv2.rectangle(img_graph, (x_anchor + w_card + gap, y_anchor + h_card - (gap_sm + h_bar)),
			(x_anchor + w_card + gap + int(w_bar * confidence), y_anchor + h_card - gap_sm), (0, 255, 0),
			thickness=cv2.FILLED)
			y_anchor += h_card + gap
			i += 1
			if i % n_cards_p_col == 0:
			x_anchor += w_card + gap + w_bar + gap
			y_anchor = gap
			pass
			return img_graph


			def detect_frame(img, card_pool, hash_size=32, highfreq_factor=4, size_thresh=10000,
			out_path=None, display=True, debug=False):
			"""
			Identify all cards in the input frame, display or save the frame if needed
			:param img: input frame
			:param card_pool: pandas dataframe of all card's information
			:param hash_size: param for pHash algorithm
			:param highfreq_factor: param for pHash algorithm
			:param size_thresh: threshold for size (in pixel) of the contour to be a candidate
			:param out_path: path to save the result
			:param display: flag for displaying the result
			:param debug: flag for debug mode
			:return: list of detected card's name/set and resulting image
			"""

			img_result = img.copy() # For displaying and saving
			det_cards = []
			# Detect contours of all cards in the image
			cnts = find_card(img_result, size_thresh=size_thresh)
			for i in range(len(cnts)):
			cnt = cnts[i]
			# For the region of the image covered by the contour, transform them into a rectangular image
			pts = np.float32([p[0] for p in cnt])
			img_warp = four_point_transform(img, pts)

			# To identify the card from the card image, perceptual hashing (pHash) algorithm is used
			# Perceptual hash is a hash string built from features of the input medium. If two media are similar
			# (ie. has similar features), their resulting pHash value will be very close.
			# Using this property, the matching card for the given card image can be found by comparing pHash of
			# all cards in the database, then finding the card that results in the minimal difference in pHash value.
			'''
			img_art = img_warp[47:249, 22:294]
			img_art = Image.fromarray(img_art.astype('uint8'), 'RGB')
			art_hash = ih.phash(img_art, hash_size=hash_size, highfreq_factor=highfreq_factor).hash.flatten()
			card_pool['hash_diff'] = card_pool['art_hash'].apply(lambda x: np.count_nonzero(x != art_hash))
			'''
			img_card = Image.fromarray(img_warp.astype('uint8'), 'RGB')
			# the stored values of hashes in the dataframe is pre-emptively flattened already to minimize computation time
			card_hash = ih.phash(img_card, hash_size=hash_size, highfreq_factor=highfreq_factor).hash.flatten()
			card_pool['hash_diff'] = card_pool['card_hash'].apply(lambda x: np.count_nonzero(x != card_hash))
			min_card = card_pool[card_pool['hash_diff'] == min(card_pool['hash_diff'])].iloc[0]
			card_name = min_card['name']
			card_set = min_card['set']
			det_cards.append((card_name, card_set))
			hash_diff = min_card['hash_diff']

			# Render the result, and display them if needed
			cv2.drawContours(img_result, [cnt], -1, (0, 255, 0), 2)
			cv2.putText(img_result, card_name, (pts[0][0], pts[0][1]), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
			if debug:
			# cv2.rectangle(img_warp, (22, 47), (294, 249), (0, 255, 0), 2)
			cv2.putText(img_warp, card_name + ', ' + str(hash_diff), (0, 50),
			cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
			cv2.imshow('card#%d' % i, img_warp)
			if display:
			cv2.imshow('Result', img_result)
			cv2.waitKey(0)

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


			def detect_video(net, classes, capture, thresh_conf=0.5, thresh_nms=0.4, in_dim=(416, 416), out_path=None):
			def detect_video(capture, card_pool, hash_size=32, highfreq_factor=4, size_thresh=10000,
			out_path=None, display=True, show_graph=True, debug=False):
			"""
			Identify all cards in the continuous video stream, display or save the result if needed
			:param capture: input video stream
			:param card_pool: pandas dataframe of all card's information
			:param hash_size: param for pHash algorithm
			:param highfreq_factor: param for pHash algorithm
			:param size_thresh: threshold for size (in pixel) of the contour to be a candidate
			:param out_path: path to save the result
			:param display: flag for displaying the result
			:param show_graph: flag to show graph
			:param debug: flag for debug mode
			:return: list of detected card's name/set and resulting image
			:return:
			"""
			# Get the dimension of the output video, and set it up
			if show_graph:
			img_graph = draw_card_graph({}, pd.DataFrame(), -1) # Black image of the graph just to get the dimension
			width = round(capture.get(cv2.CAP_PROP_FRAME_WIDTH)) + img_graph.shape[1]
			height = max(round(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)), img_graph.shape[0])
			else:
			width = round(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
			height = round(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
			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)
			vid_writer = cv2.VideoWriter(out_path, cv2.VideoWriter_fourcc(*'MJPG'), 10.0, (width, height))
			max_num_obj = 0
			f_len = 10 # number of frames to consider to check for existing cards
			exist_cards = {}
			try:
			while True:
			ret, frame = capture.read()
			start_time = time.time()
			if not ret:
			# End of video
			print("End of video. Press any key to exit")
			cv2.waitKey(0)
			break
			# Detect all cards from the current frame
			det_cards, img_result = detect_frame(frame, card_pool, hash_size=hash_size, highfreq_factor=highfreq_factor,
			size_thresh=size_thresh, out_path=None, display=False, debug=debug)
			if show_graph:
			# If the card was already detected in the previous frame, append 1 to the list
			# If the card previously detected was not found in this trame, append 0 to the list
			# If the card wasn't previously detected, make a new list and add 1 to it
			# If the same card is detected multiple times in the same frame, keep track of the duplicates
			# The confidence will be calculated based on the number of frames the card was detected for
			det_cards_count = collections.Counter(det_cards).items()
			det_cards_list = []
			for card, count in det_cards_count:
			card_name, card_set = card
			for i in range(count): 1
			key = '%s (%s) #%d' % (card_name, card_set, i + 1)
			det_cards_list.append(key)
			gone = []
			for key, val in exist_cards.items():
			if key in det_cards_list:
			exist_cards[key] = exist_cards[key][1 - f_len:] + [1]
			else:
			exist_cards[key] = exist_cards[key][1 - f_len:] + [0]
			if len(val) == f_len and sum(val) == 0:
			gone.append(key)
			for key in det_cards_list:
			if key not in exist_cards.keys():
			exist_cards[key] = [1]
			for key in gone:
			exist_cards.pop(key)

			# Sets the input to the network
			net.setInput(blob)
			# Draw the graph based on the history of detected cards, then concatenate it with the result image
			img_graph = draw_card_graph(exist_cards, card_pool, f_len)
			img_save = np.zeros((height, width, 3), dtype=np.uint8)
			img_save[0:img_result.shape[0], 0:img_result.shape[1]] = img_result
			img_save[0:img_graph.shape[0], img_result.shape[1]:img_result.shape[1] + img_graph.shape[1]] = img_graph
			else:
			img_save = img_result

			# Runs the forward pass to get output of the output layers
			outs = net.forward(get_outputs_names(net))
			# Display the result
			if display:
			cv2.imshow('result', img_save)
			if debug:
			max_num_obj = max(max_num_obj, len(det_cards))
			for i in range(len(det_cards), max_num_obj):
			cv2.imshow('card#%d' % i, np.zeros((1, 1), dtype=np.uint8))

			# 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)
			elapsed_ms = (time.time() - start_time) * 1000
			print('Elapsed time: %.2f ms' % elapsed_ms)
			if out_path is not None:
			vid_writer.write(img_save.astype(np.uint8))
			cv2.waitKey(1)
			except KeyboardInterrupt:
			capture.release()
			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
			vid_writer.release()
			cv2.destroyAllWindows()


			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"
			#test_path = os.path.abspath('test_file/test4.mp4')
			test_path = None
			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))
			hash_size = 32
			highfreq_factor = 4

			# 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)
			pck_path = os.path.abspath('card_pool_%d_%d.pck' % (hash_size, highfreq_factor))
			if os.path.isfile(pck_path):
			card_pool = pd.read_pickle(pck_path)
			else:
			# Merge database for all cards, then calculate pHash values of each, store them
			df_list = []
			for set_name in Config.all_set_list:
			csv_name = '%s/csv/%s.csv' % (Config.data_dir, set_name)
			df = fetch_data.load_all_cards_text(csv_name)
			df_list.append(df)
			card_pool = pd.concat(df_list, sort=True)
			card_pool.reset_index(drop=True, inplace=True)
			card_pool.drop('Unnamed: 0', axis=1, inplace=True, errors='ignore')

			# 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)
			card_pool = calc_image_hashes(card_pool, save_to=pck_path, hash_size=hash_size, highfreq_factor=highfreq_factor)
			card_pool = card_pool[['name', 'set', 'collector_number', 'card_hash']]

			# ImageHash is basically just one numpy.ndarray with (hash_size)^2 number of bits. pre-emptively flattening it
			# significantly increases speed for subtracting hashes in the future.
			card_pool['card_hash'] = card_pool['card_hash'].apply(lambda x: x.hash.flatten())


			# If the test file isn't given, use webcam to capture video
			if test_path is None:
			capture = cv2.VideoCapture(0)
			detect_video(capture, card_pool, out_path='%s/result.avi' % out_dir, display=True, show_graph=True, debug=False)
			capture.release()
			else:
			# Save the detection result if out_dir is provided
			if out_dir is None or out_dir == '':
			out_path = None
			else:
			f_name = os.path.split(test_path)[1]
			out_path = '%s/%s.avi' % (out_dir, f_name[:f_name.find('.')])

			if not os.path.isfile(test_path):
			print('The test file %s doesn\'t exist!' % os.path.abspath(test_path))
			return
			# 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']:
			# Test file is an image
			img = cv2.imread(test_path)
			detect_frame(img, card_pool, out_path=out_path)
			else:
			# Test file is a video
			capture = cv2.VideoCapture(test_path)
			detect_video(capture, card_pool, out_path=out_path, display=True, show_graph=True, debug=False)
			capture.release()
			pass