| | |
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| | | 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 sys |
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| | | import math |
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| | | from operator import itemgetter |
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| | | import random |
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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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| | | |
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| | | card_width = 315 |
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| | | card_height = 440 |
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| | | |
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| | | |
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| | | # Disclaimer: majority of the basic framework in this file is modified from the following tutorial: |
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| | | # https://www.learnopencv.com/deep-learning-based-object-detection-using-yolov3-with-opencv-python-c/ |
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| | | def calc_image_hashes(card_pool, save_to=None): |
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| | | card_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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| | | 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=32, highfreq_factor=4) |
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| | | card_pool.at[ind, 'card_hash'] = card_hash |
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| | | card_pool = card_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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| | | card_pool.to_pickle(save_to) |
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| | | return card_pool |
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| | | |
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| | | |
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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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| | | |
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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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| | | |
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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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| | | |
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| | | # return the ordered coordinates |
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| | | return rect |
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| | | |
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| | | |
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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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| | | |
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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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| | | |
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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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| | | |
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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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| | | |
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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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| | | |
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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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| | | |
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| | | # return the warped image |
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| | | return warped |
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| | | |
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| | | |
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| | | # Get the names of the output layers |
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| | |
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| | | |
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| | | |
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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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| | |
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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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| | | #print(detection[0:3]) |
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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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| | |
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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 > 240) * 255 |
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| | | glare = (v > 200) * 255 |
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| | | |
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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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| | |
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| | | return corrected |
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| | | |
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| | | |
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| | | def find_card(img, thresh_val=80, blur_radius=None, dilate_radius=None, min_hyst=80, max_hyst=200, min_line_length=None, max_line_gap=None, debug=False): |
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| | | # Default values |
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| | | if blur_radius is None: |
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| | | blur_radius = math.floor(min(img.shape[:2]) / 100 + 0.5) // 2 * 2 + 1 # Rounded to the nearest odd |
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| | | if dilate_radius is None: |
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| | | dilate_radius = math.floor(min(img.shape[:2]) / 67 + 0.5) |
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| | | if min_line_length is None: |
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| | | min_line_length = min(img.shape[:2]) / 3 |
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| | | if max_line_gap is None: |
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| | | max_line_gap = min(img.shape[:2]) / 10 |
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| | | |
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| | | thresh_radius = math.floor(min(img.shape[:2]) / 50 + 0.5) // 2 * 2 + 1 # Rounded to the nearest odd |
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| | | |
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| | | print(blur_radius, dilate_radius, thresh_radius, min_line_length, max_line_gap) |
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| | | ''' |
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| | | blur_radius = 3 |
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| | | dilate_radius = 3 |
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| | | thresh_radius = 3 |
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| | | min_line_length = 5 |
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| | | max_line_gap = 5 |
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| | | ''' |
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| | | |
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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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| | | # Median blur better removes background textures than Gaussian blur |
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| | | img_blur = cv2.medianBlur(img_gray, blur_radius) |
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| | | # Truncate the bright area while detecting the border |
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| | | img_thresh = cv2.adaptiveThreshold(img_blur, 128, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, thresh_radius, 5) |
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| | | # _, img_thresh = cv2.threshold(img_blur, thresh_val, 255, cv2.THRESH_TRUNC) |
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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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| | | |
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| | | # Dilate the image to emphasize thick borders around the card |
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| | | kernel_dilate = np.ones((dilate_radius, dilate_radius), np.uint8) |
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| | | img_dilate = cv2.dilate(img_thresh, kernel_dilate, iterations=1) |
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| | | img_dilate = cv2.erode(img_dilate, kernel_dilate, iterations=1) |
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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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| | | |
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| | | img_contour = img_dilate.copy() |
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| | | _, contours, _ = cv2.findContours(img_contour, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) |
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| | | img_contour = cv2.cvtColor(img_contour, cv2.COLOR_GRAY2BGR) |
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| | | img_contour = cv2.drawContours(img_contour, contours, -1, (128, 0, 0), 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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| | | |
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| | | # find the biggest area |
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| | | c = max(contours, key=cv2.contourArea) |
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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(len(cnts)): |
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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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| | | |
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| | | x, y, w, h = cv2.boundingRect(c) |
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| | | # draw the book contour (in green) |
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| | | img_contour = cv2.drawContours(img_contour, [c], -1, (0, 255, 0), 1) |
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| | | |
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| | | # Canny edge - low minimum hysteresis to detect glowed area, |
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| | | # and high maximum hysteresis to compensate for high false positives. |
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| | | img_canny = cv2.Canny(img_dilate, min_hyst, max_hyst) |
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| | | |
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| | | detected_lines = cv2.HoughLinesP(img_dilate, 1, np.pi / 180, threshold=300, |
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| | | minLineLength=min_line_length, |
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| | | maxLineGap=max_line_gap) |
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| | | card_found = detected_lines is not None |
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| | | if card_found: |
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| | | print(len(detected_lines)) |
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| | | |
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| | | img_hough = cv2.cvtColor(img_canny.copy(), cv2.COLOR_GRAY2BGR) |
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| | | if card_found: |
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| | | for line in detected_lines: |
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| | | x1, y1, x2, y2 = line[0] |
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| | | cv2.line(img_hough, (x1, y1), (x2, y2), (0, 0, 255), 1) |
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| | | |
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| | | img_thresh = cv2.cvtColor(img_thresh, cv2.COLOR_GRAY2BGR) |
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| | | img_dilate = cv2.cvtColor(img_dilate, cv2.COLOR_GRAY2BGR) |
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| | | #img_canny = cv2.cvtColor(img_canny, cv2.COLOR_GRAY2BGR) |
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| | | return img_thresh, img_dilate, img_contour, img_hough |
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| | | return cnts_rect |
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| | | |
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| | | |
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| | | def detect_frame(net, classes, img, thresh_conf=0.5, thresh_nms=0.4, in_dim=(416, 416), display=True, out_path=None): |
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| | | def detect_frame(net, classes, img, thresh_conf=0.1, thresh_nms=0.4, in_dim=(416, 416), out_path=None, display=True, |
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| | | debug=False): |
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| | | img_copy = img.copy() |
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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) |
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| | |
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| | | # Runs the forward pass to get output of the output layers |
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| | | outs = net.forward(get_outputs_names(net)) |
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| | | |
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| | | img_result = img.copy() |
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| | | |
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| | | # Remove the bounding boxes with low confidence |
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| | | obj_list = post_process(img, outs, thresh_conf, thresh_nms) |
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| | | for obj in obj_list: |
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| | | class_id, confidence, box = obj |
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| | | left, top, width, height = box |
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| | | draw_pred(img, class_id, classes, confidence, left, top, left + width, top + height) |
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| | | draw_pred(img_result, class_id, classes, confidence, left, top, left + width, top + height) |
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| | | |
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| | | # Put efficiency information. The function getPerfProfile returns the |
|---|
| | | # overall time for inference(t) and the timings for each of the layers(in layersTimes) |
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| | | t, _ = net.getPerfProfile() |
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| | | label = 'Inference time: %.2f ms' % (t * 1000.0 / cv2.getTickFrequency()) |
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| | | cv2.putText(img, label, (0, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255)) |
|---|
| | | #if display: |
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| | | # 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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| | | |
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| | | ''' |
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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 |
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| | | comparing the perceptual hashing of the image with the other cards' image from the database. |
|---|
| | | ''' |
|---|
| | | card_name_list = [] |
|---|
| | | for i in range(len(obj_list)): |
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| | | _, _, box = obj_list[i] |
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| | | 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)) |
|---|
| | | y2 = min(img.shape[0], int(top + (1 + offset_ratio) * height)) |
|---|
| | | img_snip = img[y1:y2, x1:x2] |
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| | | cnts = find_card(img_snip) |
|---|
| | | if len(cnts) > 0: |
|---|
| | | cnt = cnts[0] # The largest (rectangular) contour |
|---|
| | | pts = np.float32([p[0] for p in cnt]) |
|---|
| | | img_warp = four_point_transform(img_snip, pts) |
|---|
| | | img_warp = cv2.resize(img_warp, (card_width, card_height)) |
|---|
| | | ''' |
|---|
| | | 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=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'])] |
|---|
| | | card_name = min_cards.iloc[0]['name'] |
|---|
| | | ''' |
|---|
| | | img_card = Image.fromarray(img_warp.astype('uint8'), 'RGB') |
|---|
| | | card_hash = ih.phash(img_card, hash_size=32, highfreq_factor=4) |
|---|
| | | card_pool['hash_diff'] = card_pool['card_hash'] - card_hash |
|---|
| | | min_cards = card_pool[card_pool['hash_diff'] == min(card_pool['hash_diff'])] |
|---|
| | | card_name = min_cards.iloc[0]['name'] |
|---|
| | | card_name_list.append(card_name) |
|---|
| | | hash_diff = min_cards.iloc[0]['hash_diff'] |
|---|
| | | |
|---|
| | | # 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.astype(np.uint8)) |
|---|
| | | if display: |
|---|
| | | no_glare = remove_glare(img_copy) |
|---|
| | | img_concat = np.concatenate((img, no_glare), axis=1) |
|---|
| | | cv2.imshow('result', img_concat) |
|---|
| | | for i in range(len(obj_list)): |
|---|
| | | class_id, confidence, box = obj_list[i] |
|---|
| | | left, top, width, height = box |
|---|
| | | img_snip = img_copy[max(0, top):min(img.shape[0], top + height), |
|---|
| | | max(0, left):min(img.shape[1], left + width)] |
|---|
| | | img_thresh, img_dilate, img_canny, img_hough = find_card(img_snip) |
|---|
| | | img_concat = np.concatenate((img_snip, img_thresh, img_dilate, img_canny, img_hough), axis=1) |
|---|
| | | cv2.imshow('feature#%d' % i, img_concat) |
|---|
| | | cv2.waitKey(0) |
|---|
| | | cv2.destroyAllWindows() |
|---|
| | | cv2.imwrite(out_path, img_result.astype(np.uint8)) |
|---|
| | | |
|---|
| | | return obj_list |
|---|
| | | return obj_list, card_name_list, img_result |
|---|
| | | |
|---|
| | | |
|---|
| | | def detect_video(net, classes, capture, thresh_conf=0.5, thresh_nms=0.4, in_dim=(416, 416), display=True, out_path=None): |
|---|
| | | def detect_video(net, classes, capture, 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: |
|---|
| | | vid_writer = cv2.VideoWriter(out_path, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), 30, |
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| | | (round(capture.get(cv2.CAP_PROP_FRAME_WIDTH)), |
|---|
| | | round(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)))) |
|---|
| | | max_num_obj = 0 |
|---|
| | | while True: |
|---|
| | | start_time = time.time() |
|---|
| | | ret, frame = capture.read() |
|---|
| | | if not ret: |
|---|
| | | # End of video |
|---|
| | | print("End of video. Press any key to exit") |
|---|
| | | cv2.waitKey(0) |
|---|
| | | break |
|---|
| | | img = frame.copy() |
|---|
| | | obj_list = detect_frame(net, classes, frame, thresh_conf=thresh_conf, thresh_nms=thresh_nms, in_dim=in_dim, |
|---|
| | | display=False, out_path=None) |
|---|
| | | max_num_obj = max(max_num_obj, len(obj_list)) |
|---|
| | | if display: |
|---|
| | | no_glare = remove_glare(img) |
|---|
| | | img_concat = np.concatenate((frame, no_glare), axis=1) |
|---|
| | | cv2.imshow('result', img_concat) |
|---|
| | | for i in range(len(obj_list)): |
|---|
| | | class_id, confidence, box = obj_list[i] |
|---|
| | | left, top, width, height = box |
|---|
| | | img_snip = img[max(0, top):min(img.shape[0], top + height), |
|---|
| | | max(0, left):min(img.shape[1], left + width)] |
|---|
| | | img_thresh, img_dilate, img_canny, img_hough = find_card(img_snip) |
|---|
| | | img_concat = np.concatenate((img_snip, img_thresh, img_dilate, img_canny, img_hough), axis=1) |
|---|
| | | cv2.imshow('feature#%d' % i, img_concat) |
|---|
| | | # Use the YOLO model to identify each cards annonymously |
|---|
| | | obj_list, card_name_list, img_result = detect_frame(net, classes, frame, thresh_conf=thresh_conf, |
|---|
| | | thresh_nms=thresh_nms, in_dim=in_dim, out_path=None, |
|---|
| | | display=display, debug=debug) |
|---|
| | | if debug: |
|---|
| | | max_num_obj = max(max_num_obj, len(obj_list)) |
|---|
| | | for i in range(len(obj_list), max_num_obj): |
|---|
| | | cv2.imshow('feature#%d' % i, np.zeros((1, 1), dtype=np.uint8)) |
|---|
| | | if len(obj_list) > 0: |
|---|
| | | cv2.waitKey(0) |
|---|
| | | cv2.imshow('card#%d' % i, np.zeros((1, 1), dtype=np.uint8)) |
|---|
| | | if display: |
|---|
| | | cv2.imshow('result', img_result) |
|---|
| | | |
|---|
| | | elapsed_ms = (time.time() - start_time) * 1000 |
|---|
| | | print('Elapsed time: %.2f ms' % elapsed_ms) |
|---|
| | | if out_path is not None: |
|---|
| | | vid_writer.write(frame.astype(np.uint8)) |
|---|
| | | vid_writer.write(img_result.astype(np.uint8)) |
|---|
| | | cv2.waitKey(1) |
|---|
| | | |
|---|
| | | if out_path is not None: |
|---|
| | |
|---|
| | | |
|---|
| | | def main(): |
|---|
| | | # Specify paths for all necessary files |
|---|
| | | test_path = os.path.abspath('../data/test1.jpg') |
|---|
| | | test_path = os.path.abspath('test_file/test4.mp4') |
|---|
| | | #weight_path = 'backup/tiny_yolo_10_39500.weights' |
|---|
| | | #cfg_path = 'cfg/tiny_yolo_10.cfg' |
|---|
| | | #class_path = "data/obj_10.names" |
|---|
| | | weight_path = 'weights/second_general/tiny_yolo_final.weights' |
|---|
| | | cfg_path = 'cfg/tiny_yolo.cfg' |
|---|
| | | class_path = "data/obj.names" |
|---|
| | | cfg_path = 'cfg/tiny_yolo_old.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)) |
|---|
| | |
|---|
| | | print('The class file %s doesn\'t exist!' % os.path.abspath(test_path)) |
|---|
| | | return |
|---|
| | | |
|---|
| | | thresh_conf = 0.01 |
|---|
| | | thresh_nms = 0.8 |
|---|
| | | |
|---|
| | | # Setup |
|---|
| | | # Read class names from text file |
|---|
| | | with open(class_path, 'r') as f: |
|---|
| | |
|---|
| | | |
|---|
| | | if test_ext in ['jpg', 'jpeg', 'bmp', 'png', 'tiff']: |
|---|
| | | img = cv2.imread(test_path) |
|---|
| | | detect_frame(net, classes, img, out_path=out_path) |
|---|
| | | detect_frame(net, classes, img, out_path=out_path, thresh_conf=thresh_conf, thresh_nms=thresh_nms) |
|---|
| | | else: |
|---|
| | | capture = cv2.VideoCapture(test_path) |
|---|
| | | detect_video(net, classes, capture, out_path=out_path) |
|---|
| | | capture = cv2.VideoCapture(0) |
|---|
| | | detect_video(net, classes, capture, out_path=out_path, thresh_conf=thresh_conf, thresh_nms=thresh_nms, |
|---|
| | | display=False, debug=False) |
|---|
| | | capture.release() |
|---|
| | | pass |
|---|
| | | |
|---|
| | | |
|---|
| | | if __name__ == '__main__': |
|---|
| | | ''' |
|---|
| | | df_list = [] |
|---|
| | | for set_name in fetch_data.all_set_list: |
|---|
| | | csv_name = '%s/csv/%s.csv' % (transform_data.data_dir, set_name) |
|---|
| | | df = fetch_data.load_all_cards_text(csv_name) |
|---|
| | | df_list.append(df) |
|---|
| | | #print(df) |
|---|
| | | card_pool = pd.concat(df_list) |
|---|
| | | card_pool.reset_index(drop=True, inplace=True) |
|---|
| | | card_pool.drop('Unnamed: 0', axis=1, inplace=True, errors='ignore') |
|---|
| | | card_pool = calc_image_hashes(card_pool, save_to='card_pool.pck') |
|---|
| | | ''' |
|---|
| | | # 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) |
|---|
| | | card_pool = pd.read_pickle('card_pool.pck') |
|---|
| | | main() |
|---|
