From dea64611730c84a59c711c61f7f80948f82bcd31 Mon Sep 17 00:00:00 2001
From: Edmond Yoo <hj3yoo@uwaterloo.ca>
Date: Fri, 12 Oct 2018 20:12:47 +0000
Subject: [PATCH] Commit before removing YOLO
---
opencv_dnn.py | 123 ++++++++++++++++++++++++++++------------
1 files changed, 86 insertions(+), 37 deletions(-)
diff --git a/opencv_dnn.py b/opencv_dnn.py
index bf65a1f..03d4cc5 100644
--- a/opencv_dnn.py
+++ b/opencv_dnn.py
@@ -3,6 +3,7 @@
import pandas as pd
import imagehash as ih
import os
+import ast
import sys
import math
import random
@@ -17,33 +18,53 @@
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/
@@ -282,7 +303,9 @@
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()
+ 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 +314,9 @@
# 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 +325,10 @@
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()
+ obj_list = []
# 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:
@@ -315,6 +343,7 @@
'''
det_cards = []
for i in range(len(obj_list)):
+ start_3 = time.time()
_, _, 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
@@ -325,11 +354,14 @@
y2 = min(img.shape[0], int(top + (1 + offset_ratio) * height))
img_snip = img[y1:y2, x1:x2]
cnts = find_card(img_snip)
+ elapsed.append((time.time() - start_3) * 1000)
if len(cnts) > 0:
+ start_4 = time.time()
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))
+ elapsed.append((time.time() - start_4) * 1000)
'''
img_art = img_warp[47:249, 22:294]
img_art = Image.fromarray(img_art.astype('uint8'), 'RGB')
@@ -338,14 +370,16 @@
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)
- card_pool['hash_diff'] = card_pool['card_hash'] - card_hash
+ 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:
@@ -360,7 +394,8 @@
if out_path is not None:
cv2.imwrite(out_path, img_result.astype(np.uint8))
-
+ elapsed = [(time.time() - start_1) * 1000] + elapsed
+ #print(', '.join(['%.2f' % t for t in elapsed]))
return obj_list, det_cards, img_result
@@ -384,10 +419,11 @@
cv2.waitKey(0)
break
# Use the YOLO model to identify each cards annonymously
+ start_yolo = time.time()
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)
-
+ 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 +449,24 @@
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))
+ 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 +508,28 @@
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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