From 85506b439f7d04b6e3aec347e373ea9f1a673282 Mon Sep 17 00:00:00 2001
From: Constantin Wenger <constantin.wenger@googlemail.com>
Date: Tue, 13 Aug 2019 20:38:51 +0000
Subject: [PATCH] make sure duplicate set abbreviations are removed
---
opencv_dnn.py | 163 +++++++++++++++++++++++++++++++++++++++++++-----------
1 files changed, 129 insertions(+), 34 deletions(-)
diff --git a/opencv_dnn.py b/opencv_dnn.py
index e9b8d5a..a2c9d3b 100644
--- a/opencv_dnn.py
+++ b/opencv_dnn.py
@@ -94,7 +94,9 @@
hash_size = [hash_size]
num_cores = 15
- num_partitions = 60
+ num_partitions = round(card_pool.shape[0]/100)
+ if num_partitions < min(num_cores, card_pool.shape[0]):
+ num_partitions = min(num_cores, card_pool.shape[0])
pool = Pool(num_cores)
df_split = np.array_split(card_pool, num_partitions)
new_pool = pd.concat(pool.map(do_calc, [(split, hash_size) for split in df_split]))
@@ -216,7 +218,7 @@
return corrected
-def find_card(img, thresh_c=5, kernel_size=(3, 3), size_thresh=10000):
+def find_card(img, thresh_c=5, kernel_size=(3, 3), size_thresh=10000, debug=False):
"""
Find contours of all cards in the image
:param img: source image
@@ -229,24 +231,28 @@
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_blur = cv2.medianBlur(img_gray, 5)
img_thresh = cv2.adaptiveThreshold(img_blur, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 11, thresh_c)
- cv2.imshow('Thres', img_thresh)
+ if debug:
+ cv2.imshow('Thres', img_thresh)
# 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)
- cv2.imshow('Eroded', img_erode)
+ if debug:
+ cv2.imshow('Eroded', img_erode)
# Find the contour
cnts, hier = cv2.findContours(img_erode, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
if len(cnts) == 0:
#print('no contours')
return []
img_cont = cv2.cvtColor(img_erode, cv2.COLOR_GRAY2BGR)
+ img_cont_base = img_cont.copy()
cnts2 = sorted(cnts, key=cv2.contourArea, reverse=True)
cnts2 = cnts2[:10]
for i in range(0, len(cnts2)):
print(i, len(cnts2[i]))
- cv2.drawContours(img_cont, cnts2, -1, (0, 255, 0), 3)
- cv2.imshow('Contours', img_cont)
+ if debug:
+ cv2.drawContours(img_cont, cnts2, -1, (0, 255, 0), 3)
+ cv2.imshow('Contours', img_cont)
# 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
@@ -262,9 +268,45 @@
size = cv2.contourArea(cnt)
peri = cv2.arcLength(cnt, True)
approx = cv2.approxPolyDP(cnt, 0.04 * peri, True)
- if size >= size_thresh and len(approx) < 6:
- print('Size:', size)
- cnts_rect.append(approx)
+ if size >= size_thresh and len(approx) == 4:
+ # lets see if we got a contour very close in size as child
+ if i_child != -1:
+ img_ccont = img_cont_base.copy()
+ # lets collect all children
+ c_list = [cnts[i_child]]
+ h_info = hier[0][i_child]
+ while h_info[0] != -1:
+ cld = cnts[h_info[0]]
+ c_list.append(cld)
+ h_info = hier[0][h_info[0]]
+ # child with biggest area
+ c_list.sort(key=cv2.contourArea, reverse=True)
+ c_cnt = c_list[0] # the biggest child
+ if debug:
+ cv2.drawContours(img_ccont, c_list[:1], -1, (0, 255, 0), 1)
+ cv2.imshow('CCont %d' % i_cnt, img_ccont)
+ c_size = cv2.contourArea(c_cnt)
+ c_approx = cv2.approxPolyDP(c_cnt, 0.04 * peri, True)
+ if len(c_approx) == 4 and (c_size/size) > 0.85:
+ rect = cv2.minAreaRect(c_cnt)
+ box = cv2.boxPoints(rect)
+ box = np.intp(box)
+ print(c_cnt)
+ print(box)
+
+ print('CSize:', c_size, '%:', c_size/size)
+ b2 = []
+ for x in box:
+ b2.append([x])
+ cnts_rect.append(np.array(b2))
+ else:
+ print('CF:', (c_size/size))
+ print('Size:', size)
+ cnts_rect.append(approx)
+ else:
+ #print('CF:', (c_size/size))
+ print('Size:', size)
+ cnts_rect.append(approx)
else:
if i_child != -1:
stack.append((i_child, hier[0][i_child]))
@@ -349,7 +391,7 @@
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)
+ cnts = find_card(img_result, size_thresh=size_thresh, debug=debug)
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
@@ -375,7 +417,8 @@
img_set_part = img_warp[cut[0]:cut[1], cut[2]:cut[3]]
print(img_set_part.shape)
img_set = Image.fromarray(img_set_part.astype('uint8'), 'RGB')
- cv2.imshow("Set Img#%d" % i, img_set_part)
+ if debug:
+ cv2.imshow("Set Img#%d" % i, img_set_part)
# 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).hash.flatten()
@@ -385,24 +428,28 @@
hash_diff = min_card['hash_diff']
top_matches = sorted(card_pool['hash_diff'])
- set_img_hash = ih.whash(img_set, hash_size=hash_size).hash.flatten()
- cd_data = pd.DataFrame(columns=list(card_pool.columns.values))
- print(list(card_pool.columns.values))
- candidates = []
- for ix in range(0, 2):
- cdr = card_pool[card_pool['hash_diff'] == top_matches[ix]].iloc[0]
- cd_data.loc[0 if cd_data.empty else cd_data.index.max()+1] = cdr
- cd = cdr
- print('Idx:', ix, 'Name:', cd['name'], 'Set:', cd['set'], 'Diff:', top_matches[ix])
+ card_one = card_pool[card_pool['hash_diff'] == top_matches[0]].iloc[0]
+ card_two = card_pool[card_pool['hash_diff'] == top_matches[1]].iloc[0]
- cd_data['set_hash_diff'] = cd_data['set_hash_%d' % hash_size]
- cd_data['set_hash_diff'] = cd_data['set_hash_diff'].apply(lambda x: np.count_nonzero(x != set_img_hash))
- conf = sorted(cd_data['set_hash_diff'])
- print('Confs:', conf)
- best_match = cd_data[cd_data['set_hash_diff'] == min(cd_data['set_hash_diff'])].iloc[0]
- print('Best Match', 'Name:', best_match['name'], 'Set:', best_match['set'])
+ if card_one['name'] == card_two['name'] and card_one['set'] != card_two['set']:
+ set_img_hash = ih.whash(img_set, hash_size=hash_size).hash.flatten()
+ cd_data = pd.DataFrame(columns=list(card_pool.columns.values))
+ print(list(card_pool.columns.values))
+ candidates = []
+ for ix in range(0, 2):
+ cd = card_pool[card_pool['hash_diff'] == top_matches[ix]].iloc[0]
+ cd_data.loc[0 if cd_data.empty else cd_data.index.max()+1] = cd
+ print('Idx:', ix, 'Name:', cd['name'], 'Set:', cd['set'], 'Diff:', top_matches[ix])
- min_card = best_match
+
+ cd_data['set_hash_diff'] = cd_data['set_hash_%d' % hash_size]
+ cd_data['set_hash_diff'] = cd_data['set_hash_diff'].apply(lambda x: np.count_nonzero(x != set_img_hash))
+ conf = sorted(cd_data['set_hash_diff'])
+ print('Confs:', conf)
+ best_match = cd_data[cd_data['set_hash_diff'] == min(cd_data['set_hash_diff'])].iloc[0]
+ print('Best Match', 'Name:', best_match['name'], 'Set:', best_match['set'])
+
+ min_card = best_match
card_name = min_card['name']
card_set = min_card['set']
det_cards.append((card_name, card_set))
@@ -426,7 +473,7 @@
def detect_video(capture, card_pool, hash_size=32, size_thresh=10000,
- out_path=None, display=True, show_graph=True, debug=False):
+ out_path=None, display=True, show_graph=True, debug=False, crop_x=0, crop_y=0):
"""
Identify all cards in the continuous video stream, display or save the result if needed
:param capture: input video stream
@@ -443,8 +490,9 @@
# 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])
+ width = round(capture.get(cv2.CAP_PROP_FRAME_WIDTH)) - 2*crop_x + img_graph.shape[1]
+ height = max(round(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)) - 2*crop_y, img_graph.shape[0])
+ height += 200 # some space to display last detected cards
else:
width = round(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
height = round(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
@@ -453,9 +501,15 @@
max_num_obj = 0
f_len = 10 # number of frames to consider to check for existing cards
exist_cards = {}
+
+ exist_card_single = {}
+ written_out_cards = set()
+ found_cards = []
try:
while True:
ret, frame = capture.read()
+ croped_img = frame[crop_y:-crop_y, crop_x:-crop_x]
+ fimg = cv2.flip(croped_img, -1)
start_time = time.time()
if not ret:
# End of video
@@ -463,7 +517,7 @@
cv2.waitKey(0)
break
# Detect all cards from the current frame
- det_cards, img_result = detect_frame(frame, card_pool, hash_size=hash_size, size_thresh=size_thresh,
+ det_cards, img_result = detect_frame(fimg, card_pool, hash_size=hash_size, 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
@@ -485,18 +539,50 @@
else:
exist_cards[key] = exist_cards[key][1 - f_len:] + [0]
if len(val) == f_len and sum(val) == 0:
- gone.append(key)
+ gone.append(key) # not there anymore
+
+ det_card_map = {}
+ gone_single = []
+ for card_name, card_set in det_cards:
+ skey = '%s (%s)' % (card_name, card_set)
+ det_card_map[skey] = (card_name, card_set)
+
+ for key, val in exist_card_single.items():
+ if key in det_card_map:
+ exist_card_single[key] = exist_card_single[key][1 - f_len:] + [1]
+ else:
+ exist_card_single[key] = exist_card_single[key][1 - f_len:] + [0]
+
+ if len(val) == f_len and sum(val) == 0:
+ gone_single.append(key)
+ if key in written_out_cards:
+ written_out_cards.remove(key)
+ if len(val) == f_len and sum(val) == f_len:
+ if key not in written_out_cards and key in det_card_map:
+ written_out_cards.add(key)
+ found_cards.append(det_card_map[key])
+
+ for key in det_card_map:
+ if key not in exist_card_single.keys():
+ exist_card_single[key] = [1]
+ for key in gone_single:
+ exist_card_single.pop(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)
+
# 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
+ for c, card in enumerate(reversed(found_cards[-10:]), 1):
+ cv2.putText(img_save, f'{card[0]} ({card[1].upper()})',(0, height-200+18*c), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0))
else:
img_save = img_result
@@ -519,6 +605,12 @@
vid_writer.release()
cv2.destroyAllWindows()
+ with open('detect.txt', 'w') as of:
+ counter = collections.Counter(found_cards)
+ for key in counter:
+ of.write(f'{counter[key]} [{key[1].upper()}] {key[0]}\n')
+
+
def main(args):
# Specify paths for all necessary files
@@ -559,9 +651,12 @@
# If the test file isn't given, use webcam to capture video
if args.in_path is None:
- capture = cv2.VideoCapture(0)
+ capture = cv2.VideoCapture(0, cv2.CAP_V4L)
+ capture.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*"MJPG"))
+ capture.set(cv2.CAP_PROP_FRAME_WIDTH, 1920)
+ capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 1080)
detect_video(capture, card_pool, hash_size=args.hash_size, out_path='%s/result.avi' % args.out_path,
- display=args.display, show_graph=args.show_graph, debug=args.debug)
+ display=args.display, show_graph=args.show_graph, debug=args.debug, crop_x=500, crop_y=200)
capture.release()
else:
# Save the detection result if args.out_path is provided
--
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