import cv2
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import numpy as np
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import pandas as pd
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import imagehash as ih
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import os
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import ast
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import sys
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import math
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import random
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import collections
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from operator import itemgetter
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import time
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from PIL import Image
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import fetch_data
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import transform_data
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card_width = 315
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card_height = 440
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def calc_image_hashes(card_pool, save_to=None, hash_size=32, highfreq_factor=4):
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new_pool = pd.DataFrame(columns=list(card_pool.columns.values))
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new_pool['card_hash'] = np.NaN
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new_pool['art_hash'] = np.NaN
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for ind, card_info in card_pool.iterrows():
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if ind % 100 == 0:
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print(ind)
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card_names = []
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if card_info['layout'] in ['transform', 'double_faced_token']:
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if isinstance(card_info['card_faces'], str): # For some reason, dict isn't being parsed in the previous step
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card_faces = ast.literal_eval(card_info['card_faces'])
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else:
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card_faces = card_info['card_faces']
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for i in range(len(card_faces)):
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card_names.append(card_faces[i]['name'])
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else: # if card_info['layout'] == 'normal':
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card_names.append(card_info['name'])
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for card_name in card_names:
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card_info['name'] = card_name
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img_name = '%s/card_img/png/%s/%s_%s.png' % (transform_data.data_dir, card_info['set'],
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card_info['collector_number'],
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fetch_data.get_valid_filename(card_info['name']))
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card_img = cv2.imread(img_name)
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if card_img is None:
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fetch_data.fetch_card_image(card_info,
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out_dir='%s/card_img/png/%s' % (transform_data.data_dir, card_info['set']))
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card_img = cv2.imread(img_name)
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if card_img is None:
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print('WARNING: card %s is not found!' % img_name)
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#img_art = Image.fromarray(card_img[121:580, 63:685]) # For 745*1040 size card image
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#art_hash = ih.phash(img_art, hash_size=32, highfreq_factor=4)
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#card_pool.at[ind, 'art_hash'] = art_hash
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img_card = Image.fromarray(card_img)
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card_hash = ih.phash(img_card, hash_size=hash_size, highfreq_factor=highfreq_factor)
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#card_pool.at[ind, 'card_hash'] = card_hash
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card_info['card_hash'] = card_hash
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#print(new_pool.index.max())
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new_pool.loc[0 if new_pool.empty else new_pool.index.max() + 1] = card_info
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new_pool = new_pool[['artist', 'border_color', 'collector_number', 'color_identity', 'colors', 'flavor_text',
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'image_uris', 'mana_cost', 'legalities', 'name', 'oracle_text', 'rarity', 'type_line',
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'set', 'set_name', 'power', 'toughness', 'art_hash', 'card_hash']]
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if save_to is not None:
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new_pool.to_pickle(save_to)
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return new_pool
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# www.pyimagesearch.com/2014/08/25/4-point-opencv-getperspective-transform-example/
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def order_points(pts):
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# initialzie a list of coordinates that will be ordered
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# such that the first entry in the list is the top-left,
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# the second entry is the top-right, the third is the
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# bottom-right, and the fourth is the bottom-left
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rect = np.zeros((4, 2), dtype="float32")
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# the top-left point will have the smallest sum, whereas
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# the bottom-right point will have the largest sum
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s = pts.sum(axis=1)
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rect[0] = pts[np.argmin(s)]
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rect[2] = pts[np.argmax(s)]
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# now, compute the difference between the points, the
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# top-right point will have the smallest difference,
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# whereas the bottom-left will have the largest difference
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diff = np.diff(pts, axis=1)
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rect[1] = pts[np.argmin(diff)]
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rect[3] = pts[np.argmax(diff)]
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# return the ordered coordinates
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return rect
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# www.pyimagesearch.com/2014/08/25/4-point-opencv-getperspective-transform-example/
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def four_point_transform(image, pts):
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# obtain a consistent order of the points and unpack them
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# individually
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rect = order_points(pts)
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(tl, tr, br, bl) = rect
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# compute the width of the new image, which will be the
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# maximum distance between bottom-right and bottom-left
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# x-coordiates or the top-right and top-left x-coordinates
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widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
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widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
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maxWidth = max(int(widthA), int(widthB))
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# compute the height of the new image, which will be the
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# maximum distance between the top-right and bottom-right
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# y-coordinates or the top-left and bottom-left y-coordinates
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heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
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heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
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maxHeight = max(int(heightA), int(heightB))
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# now that we have the dimensions of the new image, construct
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# the set of destination points to obtain a "birds eye view",
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# (i.e. top-down view) of the image, again specifying points
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# in the top-left, top-right, bottom-right, and bottom-left
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# order
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dst = np.array([
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[0, 0],
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[maxWidth - 1, 0],
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[maxWidth - 1, maxHeight - 1],
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[0, maxHeight - 1]], dtype="float32")
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# compute the perspective transform matrix and then apply it
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mat = cv2.getPerspectiveTransform(rect, dst)
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warped = cv2.warpPerspective(image, mat, (maxWidth, maxHeight))
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# If the image is horizontally long, rotate it by 90
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if maxWidth > maxHeight:
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center = (maxHeight / 2, maxHeight / 2)
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mat_rot = cv2.getRotationMatrix2D(center, 270, 1.0)
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warped = cv2.warpAffine(warped, mat_rot, (maxHeight, maxWidth))
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# return the warped image
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return warped
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# Get the names of the output layers
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def get_outputs_names(net):
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# Get the names of all the layers in the network
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layers_names = net.getLayerNames()
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# Get the names of the output layers, i.e. the layers with unconnected outputs
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return [layers_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
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# Remove the bounding boxes with low confidence using non-maxima suppression
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# https://www.learnopencv.com/deep-learning-based-object-detection-using-yolov3-with-opencv-python-c/
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def post_process(frame, outs, thresh_conf, thresh_nms):
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frame_height = frame.shape[0]
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frame_width = frame.shape[1]
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# Scan through all the bounding boxes output from the network and keep only the
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# ones with high confidence scores. Assign the box's class label as the class with the highest score.
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class_ids = []
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confidences = []
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boxes = []
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for out in outs:
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for detection in out:
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scores = detection[5:]
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class_id = np.argmax(scores)
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confidence = scores[class_id]
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if confidence > thresh_conf:
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center_x = int(detection[0] * frame_width)
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center_y = int(detection[1] * frame_height)
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width = int(detection[2] * frame_width)
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height = int(detection[3] * frame_height)
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left = int(center_x - width / 2)
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top = int(center_y - height / 2)
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class_ids.append(class_id)
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confidences.append(float(confidence))
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boxes.append([left, top, width, height])
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# Perform non maximum suppression to eliminate redundant overlapping boxes with lower confidences.
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indices = [ind[0] for ind in cv2.dnn.NMSBoxes(boxes, confidences, thresh_conf, thresh_nms)]
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ret = [[class_ids[i], confidences[i], boxes[i]] for i in indices]
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return ret
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# Draw the predicted bounding box
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def draw_pred(frame, class_id, classes, conf, left, top, right, bottom):
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# Draw a bounding box.
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cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255))
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label = '%.2f' % conf
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# Get the label for the class name and its confidence
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if classes:
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assert (class_id < len(classes))
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label = '%s:%s' % (classes[class_id], label)
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# Display the label at the top of the bounding box
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label_size, base_line = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
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top = max(top, label_size[1])
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cv2.putText(frame, label, (left, top), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255))
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def remove_glare(img):
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"""
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Inspired from:
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http://www.amphident.de/en/blog/preprocessing-for-automatic-pattern-identification-in-wildlife-removing-glare.html
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The idea is to find area that has low saturation but high value, which is what a glare usually look like.
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"""
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img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
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_, s, v = cv2.split(img_hsv)
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non_sat = (s < 32) * 255 # Find all pixels that are not very saturated
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# Slightly decrease the area of the non-satuared pixels by a erosion operation.
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disk = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
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non_sat = cv2.erode(non_sat.astype(np.uint8), disk)
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# Set all brightness values, where the pixels are still saturated to 0.
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v[non_sat == 0] = 0
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# filter out very bright pixels.
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glare = (v > 200) * 255
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# Slightly increase the area for each pixel
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glare = cv2.dilate(glare.astype(np.uint8), disk)
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glare_reduced = np.ones((img.shape[0], img.shape[1], 3), dtype=np.uint8) * 200
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glare = cv2.cvtColor(glare, cv2.COLOR_GRAY2BGR)
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corrected = np.where(glare, glare_reduced, img)
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return corrected
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def find_card(img, thresh_c=5, kernel_size=(3, 3), size_ratio=0.2):
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# Typical pre-processing - grayscale, blurring, thresholding
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img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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img_blur = cv2.medianBlur(img_gray, 5)
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img_thresh = cv2.adaptiveThreshold(img_blur, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 5, thresh_c)
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# Dilute the image, then erode them to remove minor noises
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kernel = np.ones(kernel_size, np.uint8)
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img_dilate = cv2.dilate(img_thresh, kernel, iterations=1)
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img_erode = cv2.erode(img_dilate, kernel, iterations=1)
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# Find the contour
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#img_contour = img_erode.copy()
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_, cnts, hier = cv2.findContours(img_erode, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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if len(cnts) == 0:
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print('no contours')
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return []
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#img_contour = cv2.cvtColor(img_contour, cv2.COLOR_GRAY2BGR)
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#img_contour = cv2.drawContours(img_contour, cnts, -1, (0, 255, 0), 1)
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#cv2.imshow('test', img_contour)
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# For each contours detected, check if they are large enough and are rectangle
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cnts_rect = []
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ind_sort = sorted(range(len(cnts)), key=lambda i: cv2.contourArea(cnts[i]), reverse=True)
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for i in range(min(len(cnts), 5)): # The card should be within top 5 largest contour
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size = cv2.contourArea(cnts[ind_sort[i]])
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peri = cv2.arcLength(cnts[ind_sort[i]], True)
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approx = cv2.approxPolyDP(cnts[ind_sort[i]], 0.04 * peri, True)
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if size > img.shape[0] * img.shape[1] * size_ratio and len(approx) == 4:
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cnts_rect.append(approx)
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return cnts_rect
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def draw_card_graph(exist_cards, card_pool, f_len):
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w_card = 63
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h_card = 88
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gap = 25
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gap_sm = 10
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w_bar = 300
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h_bar = 12
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txt_scale = 0.8
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n_cards_p_col = 4
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w_img = gap + (w_card + gap + w_bar + gap) * 2
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#h_img = gap + (h_card + gap) * n_cards_p_col
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h_img = 480
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img_graph = np.zeros((h_img, w_img, 3), dtype=np.uint8)
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x_anchor = gap
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y_anchor = gap
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i = 0
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for key, val in sorted(exist_cards.items(), key=itemgetter(1), reverse=True)[:n_cards_p_col * 2]:
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card_name = key[:key.find('(') - 1]
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card_set = key[key.find('(') + 1:key.find(')')]
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confidence = sum(val) / f_len
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card_info = card_pool[(card_pool['name'] == card_name) & (card_pool['set'] == card_set)].iloc[0]
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img_name = '%s/card_img/tiny/%s/%s_%s.png' % (transform_data.data_dir, card_info['set'],
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card_info['collector_number'],
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fetch_data.get_valid_filename(card_info['name']))
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card_img = cv2.imread(img_name)
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img_graph[y_anchor:y_anchor + h_card, x_anchor:x_anchor + w_card] = card_img
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cv2.putText(img_graph, '%s (%s)' % (card_name, card_set),
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(x_anchor + w_card + gap, y_anchor + gap_sm + int(txt_scale * 25)), cv2.FONT_HERSHEY_SIMPLEX,
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txt_scale, (255, 255, 255), 1)
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cv2.rectangle(img_graph, (x_anchor + w_card + gap, y_anchor + h_card - (gap_sm + h_bar)),
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(x_anchor + w_card + gap + int(w_bar * confidence), y_anchor + h_card - gap_sm), (0, 255, 0),
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thickness=cv2.FILLED)
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y_anchor += h_card + gap
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i += 1
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if i % n_cards_p_col == 0:
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x_anchor += w_card + gap + w_bar + gap
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y_anchor = gap
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pass
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return img_graph
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|
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def detect_frame(net, classes, img, card_pool, thresh_conf=0.5, thresh_nms=0.4, in_dim=(416, 416), out_path=None, display=True,
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debug=False):
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start_1 = time.time()
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elapsed = []
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'''
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# Create a 4D blob from a frame.
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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))
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elapsed.append((time.time() - start_1) * 1000)
|
|
start_2 = time.time()
|
img_result = img.copy()
|
|
# Remove the bounding boxes with low confidence
|
obj_list = post_process(img, outs, thresh_conf, thresh_nms)
|
for obj in obj_list:
|
class_id, confidence, box = obj
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left, top, width, height = box
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draw_pred(img_result, class_id, classes, confidence, left, top, left + width, top + height)
|
elapsed.append((time.time() - start_2) * 1000)
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'''
|
img_result = img.copy()
|
obj_list = []
|
# Put efficiency information. The function getPerfProfile returns the
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# overall time for inference(t) and the timings for each of the layers(in layersTimes)
|
#if display:
|
# t, _ = net.getPerfProfile()
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# label = 'Inference time: %.2f ms' % (t * 1000.0 / cv2.getTickFrequency())
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# cv2.putText(img_result, label, (0, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))
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'''
|
Assuming that the model has properly identified all cards, there should be 1 card that can be classified per
|
bounding box. Find the largest rectangular contour from the region of interest, and identify the card by
|
comparing the perceptual hashing of the image with the other cards' image from the database.
|
'''
|
det_cards = []
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for i in range(len(obj_list)):
|
start_3 = time.time()
|
_, _, box = obj_list[i]
|
left, top, width, height = box
|
# Just in case the bounding box trimmed the edge of the cards, give it a bit of offset around the edge
|
offset_ratio = 0.1
|
x1 = max(0, int(left - offset_ratio * width))
|
x2 = min(img.shape[1], int(left + (1 + offset_ratio) * width))
|
y1 = max(0, int(top - offset_ratio * height))
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y2 = min(img.shape[0], int(top + (1 + offset_ratio) * height))
|
img_snip = img[y1:y2, x1:x2]
|
cnts = find_card(img_snip)
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elapsed.append((time.time() - start_3) * 1000)
|
if len(cnts) > 0:
|
start_4 = time.time()
|
cnt = cnts[0] # The largest (rectangular) contour
|
pts = np.float32([p[0] for p in cnt])
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img_warp = four_point_transform(img_snip, pts)
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img_warp = cv2.resize(img_warp, (card_width, card_height))
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elapsed.append((time.time() - start_4) * 1000)
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'''
|
img_art = img_warp[47:249, 22:294]
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img_art = Image.fromarray(img_art.astype('uint8'), 'RGB')
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art_hash = ih.phash(img_art, hash_size=32, highfreq_factor=4)
|
card_pool['hash_diff'] = card_pool['art_hash'] - art_hash
|
min_cards = card_pool[card_pool['hash_diff'] == min(card_pool['hash_diff'])]
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card_name = min_cards.iloc[0]['name']
|
'''
|
start_5 = time.time()
|
img_card = Image.fromarray(img_warp.astype('uint8'), 'RGB')
|
card_hash = ih.phash(img_card, hash_size=32, highfreq_factor=4).hash.flatten()
|
card_pool['hash_diff'] = card_pool['card_hash'].apply(lambda x: np.count_nonzero(x != card_hash))
|
min_cards = card_pool[card_pool['hash_diff'] == min(card_pool['hash_diff'])]
|
card_name = min_cards.iloc[0]['name']
|
card_set = min_cards.iloc[0]['set']
|
det_cards.append((card_name, card_set))
|
hash_diff = min_cards.iloc[0]['hash_diff']
|
elapsed.append((time.time() - start_5) * 1000)
|
|
# Display the result
|
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.putText(img_result, card_name, (x1, y1), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
|
if debug:
|
cv2.imshow('card#%d' % i, img_warp)
|
elif debug:
|
cv2.imshow('card#%d' % i, np.zeros((1, 1), dtype=np.uint8))
|
|
if out_path is not None:
|
cv2.imwrite(out_path, img_result.astype(np.uint8))
|
elapsed = [(time.time() - start_1) * 1000] + elapsed
|
#print(', '.join(['%.2f' % t for t in elapsed]))
|
return obj_list, det_cards, img_result
|
|
|
def detect_video(net, classes, capture, card_pool, thresh_conf=0.5, thresh_nms=0.4, in_dim=(416, 416), out_path=None,
|
display=True, debug=False):
|
if out_path is not None:
|
img_graph = draw_card_graph({}, None, -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])
|
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
|
# Use the YOLO model to identify each cards annonymously
|
start_yolo = time.time()
|
obj_list, det_cards, img_result = detect_frame(net, classes, frame, card_pool, thresh_conf=thresh_conf,
|
thresh_nms=thresh_nms, in_dim=in_dim, out_path=None,
|
display=display, debug=debug)
|
elapsed_yolo = (time.time() - start_yolo) * 1000
|
# 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)
|
start_graph = time.time()
|
img_graph = draw_card_graph(exist_cards, card_pool, f_len)
|
elapsed_graph = (time.time() - start_graph) * 1000
|
if debug:
|
max_num_obj = max(max_num_obj, len(obj_list))
|
for i in range(len(obj_list), max_num_obj):
|
cv2.imshow('card#%d' % i, np.zeros((1, 1), dtype=np.uint8))
|
|
start_display = time.time()
|
img_save = np.zeros((height, width, 3), dtype=np.uint8)
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img_save[0:img_result.shape[0], 0:img_result.shape[1]] = img_result
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img_save[0:img_graph.shape[0], img_result.shape[1]:img_result.shape[1] + img_graph.shape[1]] = img_graph
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if display:
|
cv2.imshow('result', img_save)
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elapsed_display = (time.time() - start_display) * 1000
|
|
elapsed_ms = (time.time() - start_time) * 1000
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#print('Elapsed time: %.2f ms, %.2f, %.2f, %.2f' % (elapsed_ms, elapsed_yolo, elapsed_graph, elapsed_display))
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if out_path is not None:
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vid_writer.write(img_save.astype(np.uint8))
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cv2.waitKey(1)
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except KeyboardInterrupt:
|
capture.release()
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if out_path is not None:
|
vid_writer.release()
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cv2.destroyAllWindows()
|
|
|
def main():
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# Specify paths for all necessary files
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test_path = os.path.abspath('test_file/test4.mp4')
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#weight_path = 'backup/tiny_yolo_10_39500.weights'
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#cfg_path = 'cfg/tiny_yolo_10.cfg'
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#class_path = "data/obj_10.names"
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weight_path = 'weights/second_general/tiny_yolo_final.weights'
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cfg_path = 'cfg/tiny_yolo_old.cfg'
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class_path = 'data/obj.names'
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out_dir = 'out'
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if not os.path.isfile(test_path):
|
print('The test file %s doesn\'t exist!' % os.path.abspath(test_path))
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return
|
if not os.path.isfile(weight_path):
|
print('The weight file %s doesn\'t exist!' % os.path.abspath(test_path))
|
return
|
if not os.path.isfile(cfg_path):
|
print('The config file %s doesn\'t exist!' % os.path.abspath(test_path))
|
return
|
if not os.path.isfile(class_path):
|
print('The class file %s doesn\'t exist!' % os.path.abspath(test_path))
|
return
|
|
|
'''
|
df_list = []
|
for set_name in fetch_data.all_set_list:
|
csv_name = '%s/csv/%s.csv' % (transform_data.data_dir, set_name)
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df = fetch_data.load_all_cards_text(csv_name)
|
df_list.append(df)
|
#print(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')
|
for hash_size in [8, 16, 32, 64]:
|
for highfreq_factor in [4, 8, 16, 32]:
|
pck_name = 'card_pool_%d_%d.pck' % (hash_size, highfreq_factor)
|
if not os.path.exists(pck_name):
|
print(pck_name)
|
calc_image_hashes(card_pool, save_to=pck_name, hash_size=hash_size, highfreq_factor=highfreq_factor)
|
'''
|
#csv_name = '%s/csv/%s.csv' % (transform_data.data_dir, 'rtr')
|
#card_pool = fetch_data.load_all_cards_text(csv_name)
|
#card_pool = calc_image_hashes(card_pool, save_to='card_pool.pck')
|
#return
|
card_pool = pd.read_pickle('card_pool_32_4.pck')
|
#card_pool = card_pool[(card_pool['set'] == 'rtr') | (card_pool['set'] == 'isd')]
|
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())
|
|
thresh_conf = 0.01
|
thresh_nms = 0.8
|
|
# 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:
|
f_name = os.path.split(test_path)[1]
|
out_path = out_dir + '/' + f_name[:f_name.find('.')] + '.avi'
|
# 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, card_pool, out_path=out_path, thresh_conf=thresh_conf, thresh_nms=thresh_nms)
|
else:
|
capture = cv2.VideoCapture(0)
|
detect_video(net, classes, capture, card_pool, out_path=out_path, thresh_conf=thresh_conf, thresh_nms=thresh_nms,
|
display=True, debug=False)
|
capture.release()
|
pass
|
|
|
if __name__ == '__main__':
|
main()
|
