From 504ece5b00f192d5c1b343fd06ce1648f9139180 Mon Sep 17 00:00:00 2001
From: Edmond Yoo <hj3yoo@uwaterloo.ca>
Date: Mon, 17 Sep 2018 03:06:19 +0000
Subject: [PATCH] Code cleaning & training new YOLO model
---
opencv_dnn.py | 222 +++++++++++++++++++++++++------------------------------
1 files changed, 102 insertions(+), 120 deletions(-)
diff --git a/opencv_dnn.py b/opencv_dnn.py
index 1e71983..9acdb5c 100644
--- a/opencv_dnn.py
+++ b/opencv_dnn.py
@@ -6,6 +6,7 @@
import sys
import math
import random
+import time
from PIL import Image
import fetch_data
import transform_data
@@ -29,7 +30,7 @@
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])
+ 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)
@@ -42,27 +43,6 @@
card_pool.to_pickle(save_to)
return card_pool
-'''
-df_list = []
-for set_name in fetch_data.all_set_list:
- csv_name = '%s/csv/%s.csv' % (transform_data.data_dir, set_name)
- df = fetch_data.load_all_cards_text(csv_name)
- df_list.append(df)
- #print(df)
-card_pool = pd.concat(df_list)
-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')
-'''
-#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')
-
-
-# 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/
-
# www.pyimagesearch.com/2014/08/25/4-point-opencv-getperspective-transform-example/
def order_points(pts):
@@ -89,6 +69,7 @@
return rect
+# www.pyimagesearch.com/2014/08/25/4-point-opencv-getperspective-transform-example/
def four_point_transform(image, pts):
# obtain a consistent order of the points and unpack them
# individually
@@ -121,14 +102,14 @@
[0, maxHeight - 1]], dtype="float32")
# compute the perspective transform matrix and then apply it
- M = cv2.getPerspectiveTransform(rect, dst)
- warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
+ 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)
- M_rot = cv2.getRotationMatrix2D(center, 270, 1.0)
- warped = cv2.warpAffine(warped, M_rot, (maxHeight, maxWidth))
+ mat_rot = cv2.getRotationMatrix2D(center, 270, 1.0)
+ warped = cv2.warpAffine(warped, mat_rot, (maxHeight, maxWidth))
# return the warped image
return warped
@@ -143,11 +124,11 @@
# 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]
-
# Scan through all the bounding boxes output from the network and keep only the
# ones with high confidence scores. Assign the box's class label as the class with the highest score.
class_ids = []
@@ -159,6 +140,7 @@
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > thresh_conf:
+ #print(detection[0:3])
center_x = int(detection[0] * frame_width)
center_y = int(detection[1] * frame_height)
width = int(detection[2] * frame_width)
@@ -221,7 +203,7 @@
return corrected
-def find_card(img, thresh_c=5, kernel_size=(3, 3), size_ratio=0.3):
+def find_card(img, thresh_c=5, kernel_size=(3, 3), size_ratio=0.2):
# Typical pre-processing - grayscale, blurring, thresholding
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_blur = cv2.medianBlur(img_gray, 5)
@@ -255,7 +237,8 @@
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):
+def detect_frame(net, classes, img, thresh_conf=0.1, thresh_nms=0.4, in_dim=(416, 416), out_path=None, display=True,
+ debug=False):
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)
@@ -266,125 +249,107 @@
# Runs the forward pass to get output of the output layers
outs = net.forward(get_outputs_names(net))
+ img_result = img.copy()
+
# 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)
+ draw_pred(img_result, class_id, classes, confidence, left, top, left + width, top + height)
# 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 display:
+ # t, _ = net.getPerfProfile()
+ # label = 'Inference time: %.2f ms' % (t * 1000.0 / cv2.getTickFrequency())
+ # cv2.putText(img_result, label, (0, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))
+
+ '''
+ Assuming that the model has properly identified all cards, there should be 1 card that can be classified per
+ bounding box. Find the largest rectangular contour from the region of interest, and identify the card by
+ comparing the perceptual hashing of the image with the other cards' image from the database.
+ '''
+ card_name_list = []
+ 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_name_list.append(card_name)
+ hash_diff = min_cards.iloc[0]['hash_diff']
+
+ # 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))
if out_path is not None:
- cv2.imwrite(out_path, img.astype(np.uint8))
- if display:
- #no_glare = remove_glare(img_copy)
- #img_concat = np.concatenate((img, no_glare), axis=1)
- cv2.imshow('result', img)
- '''
- 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.waitKey(0)
- cv2.destroyAllWindows()
+ cv2.imwrite(out_path, img_result.astype(np.uint8))
- return obj_list
+ return obj_list, card_name_list, img_result
-def detect_video(net, classes, capture, thresh_conf=0.5, thresh_nms=0.4, in_dim=(416, 416), display=True, out_path=None):
+def detect_video(net, classes, capture, thresh_conf=0.5, thresh_nms=0.4, in_dim=(416, 416), out_path=None, display=True,
+ debug=False):
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))))
max_num_obj = 0
while True:
+ start_time = time.time()
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)
- #cnts_rect = find_card(img)
- max_num_obj = max(max_num_obj, len(obj_list))
- if display:
- img_result = frame.copy()
- #img_result = cv2.drawContours(img_result, cnts_rect, -1, (0, 255, 0), 2)
- #for i in range(len(cnts_rect)):
- # pts = np.float32([p[0] for p in cnts_rect[i]])
- # img_warp = four_point_transform(img, pts)
- # cv2.imshow('card#%d' % i, img_warp)
- #for i in range(len(cnts_rect), max_num_obj):
- # cv2.imshow('card#%d' % i, np.zeros((1, 1), dtype=np.uint8))
- #no_glare = remove_glare(img)
- #img_thresh, img_erode, img_contour = find_card(no_glare)
- #img_concat = np.concatenate((no_glare, img_contour), axis=1)
-
- for i in range(len(obj_list)):
- class_id, confidence, box = obj_list[i]
- left, top, width, height = box
- 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[-1]
- 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'])]
- guttersnipe = card_pool[card_pool['name'] == 'Cyclonic Rift']
- diff = guttersnipe['art_hash'] - art_hash
- print(diff)
- card_name = min_cards.iloc[0]['name']
- #print(min_cards.iloc[0]['name'], min_cards.iloc[0]['hash_diff'])
- '''
- 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']
- hash_diff = min_cards.iloc[0]['hash_diff']
- #guttersnipe = card_pool[card_pool['name'] == 'Cyclonic Rift']
- #diff = guttersnipe['card_hash'] - card_hash
- #print(diff)
- #img_thresh, img_dilate, img_contour = find_card(img_snip)
- #img_concat = np.concatenate((img_snip, img_contour), axis=1)
- 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)
- else:
- cv2.imshow('card#%d' % i, np.zeros((1, 1), dtype=np.uint8))
+ # Use the YOLO model to identify each cards annonymously
+ obj_list, card_name_list, img_result = detect_frame(net, classes, frame, thresh_conf=thresh_conf,
+ thresh_nms=thresh_nms, in_dim=in_dim, out_path=None,
+ display=display, debug=debug)
+ 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 display:
cv2.imshow('result', img_result)
- #if len(obj_list) > 0:
- # cv2.waitKey(0)
-
+ elapsed_ms = (time.time() - start_time) * 1000
+ print('Elapsed time: %.2f ms' % elapsed_ms)
if out_path is not None:
- vid_writer.write(frame.astype(np.uint8))
+ vid_writer.write(img_result.astype(np.uint8))
cv2.waitKey(1)
if out_path is not None:
@@ -399,7 +364,7 @@
#cfg_path = 'cfg/tiny_yolo_10.cfg'
#class_path = "data/obj_10.names"
weight_path = 'weights/second_general/tiny_yolo_final.weights'
- cfg_path = 'cfg/tiny_yolo.cfg'
+ cfg_path = 'cfg/tiny_yolo_old.cfg'
class_path = 'data/obj.names'
out_dir = 'out'
if not os.path.isfile(test_path):
@@ -440,10 +405,27 @@
detect_frame(net, classes, img, out_path=out_path, thresh_conf=thresh_conf, thresh_nms=thresh_nms)
else:
capture = cv2.VideoCapture(0)
- detect_video(net, classes, capture, out_path=out_path, thresh_conf=thresh_conf, thresh_nms=thresh_nms)
+ detect_video(net, classes, capture, out_path=out_path, thresh_conf=thresh_conf, thresh_nms=thresh_nms,
+ display=False, debug=False)
capture.release()
pass
if __name__ == '__main__':
+ '''
+ df_list = []
+ for set_name in fetch_data.all_set_list:
+ csv_name = '%s/csv/%s.csv' % (transform_data.data_dir, set_name)
+ df = fetch_data.load_all_cards_text(csv_name)
+ df_list.append(df)
+ #print(df)
+ card_pool = pd.concat(df_list)
+ 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')
+ '''
+ # 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')
main()
--
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