From 17c776a0eab276e9d1057cb1abf8fd7d77d54ada Mon Sep 17 00:00:00 2001
From: Edmond Yoo <hj3yoo@uwaterloo.ca>
Date: Sat, 13 Oct 2018 04:26:32 +0000
Subject: [PATCH] replaced neural net with opencv :'(
---
opencv_dnn.py | 264 ++++++++++++++++++++++++++++++++++------------------
1 files changed, 172 insertions(+), 92 deletions(-)
diff --git a/opencv_dnn.py b/opencv_dnn.py
index bf65a1f..c7ff523 100644
--- a/opencv_dnn.py
+++ b/opencv_dnn.py
@@ -3,6 +3,8 @@
import pandas as pd
import imagehash as ih
import os
+import ast
+import queue
import sys
import math
import random
@@ -17,33 +19,53 @@
card_height = 440
-def calc_image_hashes(card_pool, save_to=None):
- card_pool['art_hash'] = np.NaN
+def calc_image_hashes(card_pool, save_to=None, hash_size=32, highfreq_factor=4):
+ 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(ind)
- img_name = '%s/card_img/png/%s/%s_%s.png' % (transform_data.data_dir, card_info['set'],
- card_info['collector_number'],
- fetch_data.get_valid_filename(card_info['name']))
- card_img = cv2.imread(img_name)
- if card_img is None:
- fetch_data.fetch_card_image(card_info,
- out_dir='%s/card_img/png/%s' % (transform_data.data_dir, card_info['set']))
+
+ card_names = []
+ if card_info['layout'] in ['transform', 'double_faced_token']:
+ if isinstance(card_info['card_faces'], str): # For some reason, dict isn't being parsed in the previous step
+ 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:
+ card_info['name'] = card_name
+ img_name = '%s/card_img/png/%s/%s_%s.png' % (transform_data.data_dir, card_info['set'],
+ card_info['collector_number'],
+ fetch_data.get_valid_filename(card_info['name']))
card_img = cv2.imread(img_name)
- if card_img is None:
- print('WARNING: card %s is not found!' % img_name)
- img_art = Image.fromarray(card_img[121:580, 63:685]) # For 745*1040 size card image
- art_hash = ih.phash(img_art, hash_size=32, highfreq_factor=4)
- card_pool.at[ind, 'art_hash'] = art_hash
- img_card = Image.fromarray(card_img)
- card_hash = ih.phash(img_card, hash_size=32, highfreq_factor=4)
- card_pool.at[ind, 'card_hash'] = card_hash
- card_pool = card_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 card_img is None:
+ fetch_data.fetch_card_image(card_info,
+ out_dir='%s/card_img/png/%s' % (transform_data.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)
+ #img_art = Image.fromarray(card_img[121:580, 63:685]) # For 745*1040 size card image
+ #art_hash = ih.phash(img_art, hash_size=32, highfreq_factor=4)
+ #card_pool.at[ind, '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_pool.at[ind, 'card_hash'] = card_hash
+ card_info['card_hash'] = card_hash
+ #print(new_pool.index.max())
+ new_pool.loc[0 if new_pool.empty else new_pool.index.max() + 1] = card_info
+
+ 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:
- card_pool.to_pickle(save_to)
- return card_pool
+ new_pool.to_pickle(save_to)
+ return new_pool
# www.pyimagesearch.com/2014/08/25/4-point-opencv-getperspective-transform-example/
@@ -204,7 +226,7 @@
return corrected
-def find_card(img, thresh_c=5, kernel_size=(3, 3), size_ratio=0.2):
+def find_card(img, thresh_c=5, kernel_size=(3, 3), size_thresh=5000):
# Typical pre-processing - grayscale, blurring, thresholding
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_blur = cv2.medianBlur(img_gray, 5)
@@ -221,19 +243,58 @@
if len(cnts) == 0:
print('no contours')
return []
+ cv2.drawContours(img, cnts, -1, (0, 0, 255), 1)
+ '''
+ next = 0
+ while next != -1:
+ img_copy = img.copy()
+ print(hier[0][next])
+ cv2.drawContours(img_copy, cnts[hier[0][next][0]], -1, (0, 255, 0), 2)
+ cv2.imshow('hi', img_copy)
+ cv2.waitKey(0)
+ next = hier[0][next][0]
+ '''
#img_contour = cv2.cvtColor(img_contour, cv2.COLOR_GRAY2BGR)
#img_contour = cv2.drawContours(img_contour, cnts, -1, (0, 255, 0), 1)
#cv2.imshow('test', img_contour)
- # For each contours detected, check if they are large enough and are rectangle
+ '''
+ 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 (preorder) depth-first 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:
+ cv2.drawContours(img, [cnt], -1, (255, 0, 0), 1)
+ #print(size)
+ if len(approx) == 4:
+ cnts_rect.append(approx)
+ else:
+ if i_child != -1:
+ stack.append((i_child, hier[0][i_child]))
+
+
+ '''
+ # For each contours detected, check if they are large enough and are rectangle
ind_sort = sorted(range(len(cnts)), key=lambda i: cv2.contourArea(cnts[i]), reverse=True)
- for i in range(min(len(cnts), 5)): # The card should be within top 5 largest contour
- size = cv2.contourArea(cnts[ind_sort[i]])
+ for i in range(len(cnts)):
peri = cv2.arcLength(cnts[ind_sort[i]], True)
approx = cv2.approxPolyDP(cnts[ind_sort[i]], 0.04 * peri, True)
- if size > img.shape[0] * img.shape[1] * size_ratio and len(approx) == 4:
+ if len(approx) == 4:
cnts_rect.append(approx)
+ '''
return cnts_rect
@@ -280,9 +341,11 @@
return img_graph
-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,
- debug=False):
- img_copy = img.copy()
+def detect_frame(net, classes, img, card_pool, thresh_conf=0.5, thresh_nms=0.4, in_dim=(416, 416), card_size=1000,
+ out_path=None, display=True, debug=False):
+ start_1 = time.time()
+ elapsed = []
+ '''
# Create a 4D blob from a frame.
blob = cv2.dnn.blobFromImage(img, 1 / 255, in_dim, [0, 0, 0], 1, crop=False)
@@ -291,7 +354,9 @@
# Runs the forward pass to get output of the output layers
outs = net.forward(get_outputs_names(net))
+ elapsed.append((time.time() - start_1) * 1000)
+ start_2 = time.time()
img_result = img.copy()
# Remove the bounding boxes with low confidence
@@ -300,7 +365,9 @@
class_id, confidence, box = obj
left, top, width, height = box
draw_pred(img_result, class_id, classes, confidence, left, top, left + width, top + height)
-
+ elapsed.append((time.time() - start_2) * 1000)
+ '''
+ img_result = img.copy()
# Put efficiency information. The function getPerfProfile returns the
# overall time for inference(t) and the timings for each of the layers(in layersTimes)
#if display:
@@ -314,54 +381,53 @@
comparing the perceptual hashing of the image with the other cards' image from the database.
'''
det_cards = []
- for i in range(len(obj_list)):
- _, _, 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))
- y2 = min(img.shape[0], int(top + (1 + offset_ratio) * height))
- img_snip = img[y1:y2, x1:x2]
- 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_set = min_cards.iloc[0]['set']
- det_cards.append((card_name, card_set))
- hash_diff = min_cards.iloc[0]['hash_diff']
+ start_3 = time.time()
+ cnts = find_card(img_result)
+ for i in range(len(cnts)):
+ cnt = cnts[i]
+ # ignore any contours smaller than threshold
+ elapsed.append((time.time() - start_3) * 1000)
+ start_4 = time.time()
+ pts = np.float32([p[0] for p in cnt])
+ img_warp = four_point_transform(img, pts)
+ img_warp = cv2.resize(img_warp, (card_width, card_height))
+ elapsed.append((time.time() - start_4) * 1000)
+ '''
+ 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']
+ '''
+ 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))
+ # 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.drawContours(img_result, [cnt], -1, (0, 255, 0), 1)
+ 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.imshow('card#%d' % i, img_warp)
+ #if 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))
-
- return obj_list, det_cards, img_result
+ elapsed = [(time.time() - start_1) * 1000] + elapsed
+ #print(', '.join(['%.2f' % t for t in elapsed]))
+ return 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,
@@ -384,10 +450,11 @@
cv2.waitKey(0)
break
# Use the YOLO model to identify each cards annonymously
- 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)
-
+ start_yolo = time.time()
+ 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
@@ -413,21 +480,24 @@
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))
- 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)
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
if display:
cv2.imshow('result', img_save)
+ elapsed_display = (time.time() - start_display) * 1000
elapsed_ms = (time.time() - start_time) * 1000
- print('Elapsed time: %.2f ms' % elapsed_ms)
+ print('Elapsed time: %.2f ms, %.2f, %.2f, %.2f' % (elapsed_ms, elapsed_yolo, elapsed_graph, elapsed_display))
if out_path is not None:
vid_writer.write(img_save.astype(np.uint8))
cv2.waitKey(1)
@@ -469,18 +539,28 @@
df = fetch_data.load_all_cards_text(csv_name)
df_list.append(df)
#print(df)
- card_pool = pd.concat(df_list)
+ 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')
- card_pool = calc_image_hashes(card_pool, save_to='card_pool.pck')
+ 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)
- card_pool = pd.read_pickle('card_pool.pck')
- card_pool = card_pool[(card_pool['set'] == 'rtr') | (card_pool['set'] == 'isd')]
+ #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
--
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