From ecd2b231f55fa913b91fad8929fa5c2b4a929dcf Mon Sep 17 00:00:00 2001
From: Edmond Yoo <hj3yoo@uwaterloo.ca>
Date: Sat, 13 Oct 2018 19:22:57 +0000
Subject: [PATCH] Removing files from AlxeyAB's fork
---
opencv_dnn.py | 646 ++++++++++++++++++++++++++++++++++++++++++----------------
1 files changed, 468 insertions(+), 178 deletions(-)
diff --git a/opencv_dnn.py b/opencv_dnn.py
index 25d5848..9f83caa 100644
--- a/opencv_dnn.py
+++ b/opencv_dnn.py
@@ -1,15 +1,176 @@
+import ast
+import collections
import cv2
+import imagehash as ih
import numpy as np
-import os
-import sys
-import math
from operator import itemgetter
+import os
+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
@@ -19,6 +180,7 @@
# Remove the bounding boxes with low confidence using non-maxima suppression
+# 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]
@@ -67,13 +229,17 @@
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 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)
@@ -86,7 +252,7 @@
# Set all brightness values, where the pixels are still saturated to 0.
v[non_sat == 0] = 0
# filter out very bright pixels.
- glare = (v > 240) * 255
+ glare = (v > 200) * 255
# Slightly increase the area for each pixel
glare = cv2.dilate(glare.astype(np.uint8), disk)
@@ -96,205 +262,329 @@
return corrected
-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):
- # Default values
- if blur_radius is None:
- blur_radius = math.floor(min(img.shape[:2]) / 100 + 0.5) // 2 * 2 + 1 # Rounded to the nearest odd
- if dilate_radius is None:
- dilate_radius = math.floor(min(img.shape[:2]) / 67 + 0.5)
- if min_line_length is None:
- min_line_length = min(img.shape[:2]) / 3
- if max_line_gap is None:
- max_line_gap = min(img.shape[:2]) / 10
-
- thresh_radius = math.floor(min(img.shape[:2]) / 50 + 0.5) // 2 * 2 + 1 # Rounded to the nearest odd
-
- print(blur_radius, dilate_radius, thresh_radius, min_line_length, max_line_gap)
- '''
- blur_radius = 3
- dilate_radius = 3
- thresh_radius = 3
- min_line_length = 5
- max_line_gap = 5
- '''
-
+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)
- # Median blur better removes background textures than Gaussian blur
- img_blur = cv2.medianBlur(img_gray, blur_radius)
- # Truncate the bright area while detecting the border
- img_thresh = cv2.adaptiveThreshold(img_blur, 128, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, thresh_radius, 5)
- # _, img_thresh = cv2.threshold(img_blur, thresh_val, 255, cv2.THRESH_TRUNC)
+ 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)
- # Dilate the image to emphasize thick borders around the card
- kernel_dilate = np.ones((dilate_radius, dilate_radius), np.uint8)
- img_dilate = cv2.dilate(img_thresh, kernel_dilate, iterations=1)
- img_dilate = cv2.erode(img_dilate, kernel_dilate, iterations=1)
+ # 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)
- img_contour = img_dilate.copy()
- _, contours, _ = cv2.findContours(img_contour, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
- img_contour = cv2.cvtColor(img_contour, cv2.COLOR_GRAY2BGR)
- img_contour = cv2.drawContours(img_contour, contours, -1, (128, 0, 0), 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 []
- # find the biggest area
- c = max(contours, key=cv2.contourArea)
-
- x, y, w, h = cv2.boundingRect(c)
- # draw the book contour (in green)
- img_contour = cv2.drawContours(img_contour, [c], -1, (0, 255, 0), 1)
-
- # Canny edge - low minimum hysteresis to detect glowed area,
- # and high maximum hysteresis to compensate for high false positives.
- img_canny = cv2.Canny(img_dilate, min_hyst, max_hyst)
-
- detected_lines = cv2.HoughLinesP(img_dilate, 1, np.pi / 180, threshold=300,
- minLineLength=min_line_length,
- maxLineGap=max_line_gap)
- card_found = detected_lines is not None
- if card_found:
- print(len(detected_lines))
-
- img_hough = cv2.cvtColor(img_canny.copy(), cv2.COLOR_GRAY2BGR)
- if card_found:
- for line in detected_lines:
- x1, y1, x2, y2 = line[0]
- cv2.line(img_hough, (x1, y1), (x2, y2), (0, 0, 255), 1)
-
- img_thresh = cv2.cvtColor(img_thresh, cv2.COLOR_GRAY2BGR)
- img_dilate = cv2.cvtColor(img_dilate, cv2.COLOR_GRAY2BGR)
- #img_canny = cv2.cvtColor(img_canny, cv2.COLOR_GRAY2BGR)
- return img_thresh, img_dilate, img_contour, img_hough
+ # 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 detect_frame(net, classes, img, thresh_conf=0.5, thresh_nms=0.4, in_dim=(416, 416), display=True, out_path=None):
- img_copy = img.copy()
- # Create a 4D blob from a frame.
- blob = cv2.dnn.blobFromImage(img, 1 / 255, in_dim, [0, 0, 0], 1, crop=False)
+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
- # Sets the input to the network
- net.setInput(blob)
+ i = 0
- # Runs the forward pass to get output of the output layers
- outs = net.forward(get_outputs_names(net))
+ # 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))
- # 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
- left, top, width, height = box
- draw_pred(img, class_id, classes, confidence, left, top, left + width, top + height)
+ # 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
- # Put efficiency information. The function getPerfProfile returns the
- # overall time for inference(t) and the timings for each of the layers(in layersTimes)
- t, _ = net.getPerfProfile()
- label = 'Inference time: %.2f ms' % (t * 1000.0 / cv2.getTickFrequency())
- cv2.putText(img, label, (0, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))
- if out_path is not None:
- cv2.imwrite(out_path, img.astype(np.uint8))
+def detect_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:
- 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.imshow('Result', img_result)
cv2.waitKey(0)
- cv2.destroyAllWindows()
- return obj_list
-
-
-def detect_video(net, classes, capture, thresh_conf=0.5, thresh_nms=0.4, in_dim=(416, 416), display=True, out_path=None):
if out_path is not None:
- vid_writer = cv2.VideoWriter(out_path, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), 30,
- (round(capture.get(cv2.CAP_PROP_FRAME_WIDTH)),
- round(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))))
+ cv2.imwrite(out_path, img_result.astype(np.uint8))
+ return det_cards, img_result
+
+
+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(*'MJPG'), 10.0, (width, height))
max_num_obj = 0
- 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
- 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)
- 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:
+ 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)
- if out_path is not None:
- vid_writer.write(frame.astype(np.uint8))
- cv2.waitKey(1)
+ 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)
- if out_path is not None:
- vid_writer.release()
- cv2.destroyAllWindows()
+ # 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
+
+ # 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))
+
+ 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.release()
+ cv2.destroyAllWindows()
def main():
# Specify paths for all necessary files
- test_path = os.path.abspath('../data/test1.jpg')
- 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))
- 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
+ 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)
-
- # Save the detection result if out_dir is provided
- if out_dir is None or out_dir == '':
- out_path = None
+ 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:
- 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:]
+ # 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')
- 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
--
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