From 6fc8c0f924c2c47ac3d518652fdba25da0dcdcb8 Mon Sep 17 00:00:00 2001
From: SpeedProg <speedprog@googlemail.com>
Date: Thu, 02 Jan 2020 16:06:12 +0000
Subject: [PATCH] removed figures
---
opencv_dnn.py | 58 +++++++++++++++++++++++++++++++++++++++++++---------------
1 files changed, 43 insertions(+), 15 deletions(-)
diff --git a/opencv_dnn.py b/opencv_dnn.py
old mode 100644
new mode 100755
index bf6142e..4b2c4e0
--- a/opencv_dnn.py
+++ b/opencv_dnn.py
@@ -28,7 +28,7 @@
new_pool = pd.DataFrame(columns=list(card_pool.columns.values))
for hs in hash_size:
new_pool['card_hash_%d' % hs] = np.NaN
- new_pool['set_hash_%d' % hs] = np.NaN
+ new_pool['set_hash_%d' % 64] = np.NaN
#new_pool['art_hash_%d' % hs] = np.NaN
for ind, card_info in card_pool.iterrows():
if ind % 100 == 0:
@@ -68,18 +68,33 @@
if card_img is None:
print('WARNING: card %s is not found!' % img_name)
continue
-
- set_img = card_img[575:638, 567:700]
+ """
+ img_cc = cv2.cvtColor(card_img, cv2.COLOR_BGR2GRAY)
+ img_thresh = cv2.adaptiveThreshold(img_cc, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 11, 5)
+ # Dilute the image, then erode them to remove minor noises
+ kernel = np.ones((3, 3), np.uint8)
+ img_dilate = cv2.dilate(img_thresh, kernel, iterations=1)
+ img_erode = cv2.erode(img_dilate, kernel, iterations=1)
+ cnts, hier = cv2.findContours(img_erode, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+ cnts2 = sorted(cnts, key=cv2.contourArea, reverse=True)
+ cnts2 = cnts2[:10]
+ if True:
+ cv2.drawContours(img_cc, cnts2, -1, (0, 255, 0), 3)
+ #cv2.imshow('Contours', card_img)
+ #cv2.waitKey(10000)
+ """
+ set_img = card_img[595:635, 600:690]
#cv2.imshow(card_info['name'], set_img)
# 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
img_card = Image.fromarray(card_img)
img_set = Image.fromarray(set_img)
+ #cv2.imshow('Set' + card_names[0], set_img)
for hs in hash_size:
card_hash = ih.phash(img_card, hash_size=hs)
- set_hash = ih.whash(img_set, hash_size=hs)
+ set_hash = ih.whash(img_set, hash_size=64)
card_info['card_hash_%d' % hs] = card_hash
- card_info['set_hash_%d' % hs] = set_hash
+ card_info['set_hash_%d' % 64] = set_hash
#print('Setting set_hash_%d' % hs)
#art_hash = ih.phash(img_art, hash_size=hs)
#card_info['art_hash_%d' % hs] = art_hash
@@ -99,8 +114,8 @@
elif isinstance(hash_size, int):
hash_size = [hash_size]
- num_cores = 15
- num_partitions = round(card_pool.shape[0]/100)
+ num_cores = 16
+ num_partitions = round(card_pool.shape[0]/1000)
if num_partitions < min(num_cores, card_pool.shape[0]):
num_partitions = min(num_cores, card_pool.shape[0])
pool = Pool(num_cores)
@@ -248,7 +263,7 @@
# Find the contour
cnts, hier = cv2.findContours(img_erode, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
if len(cnts) == 0:
- #print('no contours')
+ print('no contours')
return []
img_cont = cv2.cvtColor(img_erode, cv2.COLOR_GRAY2BGR)
img_cont_base = img_cont.copy()
@@ -274,6 +289,8 @@
size = cv2.contourArea(cnt)
peri = cv2.arcLength(cnt, True)
approx = cv2.approxPolyDP(cnt, 0.04 * peri, True)
+ print('Base Size:', size)
+ print('Len Approx:', len(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:
@@ -290,7 +307,7 @@
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)
+ cv2.imshow('CCont', 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:
@@ -380,7 +397,7 @@
return img_graph
-def detect_frame(img, card_pool, hash_size=32, size_thresh=100000,
+def detect_frame(img, card_pool, hash_size=32, size_thresh=10000,
out_path=None, display=True, debug=False):
"""
Identify all cards in the input frame, display or save the frame if needed
@@ -398,7 +415,9 @@
det_cards = []
# Detect contours of all cards in the image
cnts = find_card(img_result, size_thresh=size_thresh, debug=debug)
+ print('Countours:', len(cnts))
for i in range(len(cnts)):
+ print('Contour', i)
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])
@@ -423,6 +442,7 @@
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')
+ print('img set')
if debug:
cv2.imshow("Set Img#%d" % i, img_set_part)
@@ -448,7 +468,7 @@
print('Idx:', ix, 'Name:', cd['name'], 'Set:', cd['set'], 'Diff:', top_matches[ix])
- cd_data['set_hash_diff'] = cd_data['set_hash_%d' % hash_size]
+ cd_data['set_hash_diff'] = cd_data['set_hash_%d' % 64]
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)
@@ -471,9 +491,10 @@
cv2.imshow('card#%d' % i, img_warp)
if display:
cv2.imshow('Result', img_result)
- cv2.waitKey(0)
+ inp = cv2.waitKey(0)
if out_path is not None:
+ print(out_path)
cv2.imwrite(out_path, img_result.astype(np.uint8))
return det_cards, img_result
@@ -604,7 +625,9 @@
print('Elapsed time: %.2f ms' % elapsed_ms)
if out_path is not None:
vid_writer.write(img_save.astype(np.uint8))
- cv2.waitKey(1)
+ inp = cv2.waitKey(0)
+ if 'q' == chr(inp & 255):
+ break
except KeyboardInterrupt:
capture.release()
if out_path is not None:
@@ -640,7 +663,7 @@
card_pool.drop('Unnamed: 0', axis=1, inplace=True, errors='ignore')
card_pool = calc_image_hashes(card_pool, save_to=pck_path, hash_size=hash_sizes)
ch_key = 'card_hash_%d' % args.hash_size
- set_key = 'set_hash_%d' % args.hash_size
+ set_key = 'set_hash_%d' % 64
if ch_key not in card_pool.columns:
# we did not generate this hash_size yet
print('We need to add hash_size=%d' % (args.hash_size,))
@@ -663,8 +686,11 @@
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)
+
+ thres = int(((1920-2*500)*(1080-2*200)*0.3))
+ print('Threshold:', thres)
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, crop_x=500, crop_y=200)
+ display=args.display, show_graph=args.show_graph, debug=args.debug, crop_x=500, crop_y=200, size_thresh=thres)
capture.release()
else:
# Save the detection result if args.out_path is provided
@@ -682,6 +708,8 @@
if test_ext in ['jpg', 'jpeg', 'bmp', 'png', 'tiff']:
# Test file is an image
img = cv2.imread(args.in_path)
+ if img is None:
+ print('Could not read', args.in_path)
detect_frame(img, card_pool, hash_size=args.hash_size, out_path=out_path, display=args.display,
debug=args.debug)
else:
--
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