From 0a342d718ede0c363da223345607d493584c4591 Mon Sep 17 00:00:00 2001
From: Edmond Yoo <hj3yoo@uwaterloo.ca>
Date: Sat, 13 Oct 2018 06:16:06 +0000
Subject: [PATCH] Cleaning & commenting #1 - opencv_dnn.py
---
opencv_dnn.py | 496 ++++++++++++++++++++++++++----------------------------
1 files changed, 241 insertions(+), 255 deletions(-)
diff --git a/opencv_dnn.py b/opencv_dnn.py
index c7ff523..624aea8 100644
--- a/opencv_dnn.py
+++ b/opencv_dnn.py
@@ -1,35 +1,39 @@
-import cv2
-import numpy as np
-import pandas as pd
-import imagehash as ih
-import os
import ast
-import queue
-import sys
-import math
-import random
import collections
+import cv2
+import imagehash as ih
+import numpy as np
from operator import itemgetter
-import time
+import os
+import pandas as pd
from PIL import Image
+import time
+
import fetch_data
import transform_data
-card_width = 315
-card_height = 440
-
def calc_image_hashes(card_pool, save_to=None, hash_size=32, highfreq_factor=4):
+ """
+ Calculate perceptual hash (pHash) value for each cards in the database, then store them if needed
+ :param card_pool: pandas dataframe containing all card information
+ :param save_to: path for the pickle file to be saved
+ :param hash_size: param for pHash algorithm
+ :param highfreq_factor: param for pHash algorithm
+ :return: pandas dataframe
+ """
+ # Since some double-faced cards may result in two different cards, create a new dataframe to store the result
new_pool = pd.DataFrame(columns=list(card_pool.columns.values))
new_pool['card_hash'] = np.NaN
- new_pool['art_hash'] = np.NaN
+ #new_pool['art_hash'] = np.NaN
for ind, card_info in card_pool.iterrows():
if ind % 100 == 0:
- print(ind)
+ print('Calculating hashes: %dth card' % ind)
card_names = []
+ # Double-faced cards have a different json format than normal cards
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
+ if isinstance(card_info['card_faces'], str):
card_faces = ast.literal_eval(card_info['card_faces'])
else:
card_faces = card_info['card_faces']
@@ -39,27 +43,33 @@
card_names.append(card_info['name'])
for card_name in card_names:
+ # Fetch the image - name can be found based on the card's information
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 the image doesn't exist, download it from the URL
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
+
+ # 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
+ art_hash = ih.phash(img_art, hash_size=hash_size, highfreq_factor=highfreq_factor)
+ card_info['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
+ # Remove uselesss fields, then pickle it if needed
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']]
@@ -70,10 +80,12 @@
# www.pyimagesearch.com/2014/08/25/4-point-opencv-getperspective-transform-example/
def order_points(pts):
- # initialzie a list of coordinates that will be ordered
- # such that the first entry in the list is the top-left,
- # the second entry is the top-right, the third is the
- # bottom-right, and the fourth is the bottom-left
+ """
+ initialzie a list of coordinates that will be ordered such that the first entry in the list is the top-left,
+ the second entry is the top-right, the third is the bottom-right, and the fourth is the bottom-left
+ :param pts: array containing 4 points
+ :return: ordered list of 4 points
+ """
rect = np.zeros((4, 2), dtype="float32")
# the top-left point will have the smallest sum, whereas
@@ -93,8 +105,14 @@
return rect
-# www.pyimagesearch.com/2014/08/25/4-point-opencv-getperspective-transform-example/
def four_point_transform(image, pts):
+ """
+ Transform a quadrilateral section of an image into a rectangular area
+ From: www.pyimagesearch.com/2014/08/25/4-point-opencv-getperspective-transform-example/
+ :param image: source image
+ :param pts: 4 corners of the quadrilateral
+ :return: rectangular image of the specified area
+ """
# obtain a consistent order of the points and unpack them
# individually
rect = order_points(pts)
@@ -139,6 +157,11 @@
return warped
+'''
+# The following functions are only used in conjunction with YOLO, and is deprecated:
+# - get_outputs_names()
+# - post_process()
+# - draw_pred()
# Get the names of the output layers
def get_outputs_names(net):
# Get the names of all the layers in the network
@@ -197,13 +220,17 @@
label_size, base_line = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
top = max(top, label_size[1])
cv2.putText(frame, label, (left, top), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255))
+'''
def remove_glare(img):
"""
+ Reduce the effect of glaring in the image
Inspired from:
http://www.amphident.de/en/blog/preprocessing-for-automatic-pattern-identification-in-wildlife-removing-glare.html
The idea is to find area that has low saturation but high value, which is what a glare usually look like.
+ :param img: source image
+ :return: corrected image with glaring smoothened out
"""
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
_, s, v = cv2.split(img_hsv)
@@ -226,7 +253,15 @@
return corrected
-def find_card(img, thresh_c=5, kernel_size=(3, 3), size_thresh=5000):
+def find_card(img, thresh_c=5, kernel_size=(3, 3), size_thresh=10000):
+ """
+ Find contours of all cards in the image
+ :param img: source image
+ :param thresh_c: value of the constant C for adaptive thresholding
+ :param kernel_size: dimension of the kernel used for dilation and erosion
+ :param size_thresh: threshold for size (in pixel) of the contour to be a candidate
+ :return: list of candidate contours
+ """
# Typical pre-processing - grayscale, blurring, thresholding
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_blur = cv2.medianBlur(img_gray, 5)
@@ -238,33 +273,15 @@
img_erode = cv2.erode(img_dilate, kernel, iterations=1)
# Find the contour
- #img_contour = img_erode.copy()
_, cnts, hier = cv2.findContours(img_erode, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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)
- '''
- 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
- '''
-
+ # 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
+ # 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:
@@ -276,55 +293,57 @@
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)
+ if size >= size_thresh and 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(len(cnts)):
- peri = cv2.arcLength(cnts[ind_sort[i]], True)
- approx = cv2.approxPolyDP(cnts[ind_sort[i]], 0.04 * peri, True)
- if len(approx) == 4:
- cnts_rect.append(approx)
- '''
-
return cnts_rect
def draw_card_graph(exist_cards, card_pool, f_len):
- w_card = 63
+ """
+ Given the history of detected cards in the current and several previous frames, draw a simple graph
+ displaying the detected cards with its confidence level
+ :param exist_cards: History of all detected cards in the previous (f_len) frames
+ :param card_pool: pandas dataframe of all card's information
+ :param f_len: length of windows (in frames) to consider for confidence level
+ :return:
+ """
+ # Lots of constants to set the dimension of each elements
+ w_card = 63 # Width of the card image displayed
h_card = 88
- gap = 25
- gap_sm = 10
- w_bar = 300
+ gap = 25 # Offset between each elements
+ gap_sm = 10 # Small offset
+ w_bar = 300 # Length of the confidence bar at 100%
h_bar = 12
txt_scale = 0.8
- n_cards_p_col = 4
- w_img = gap + (w_card + gap + w_bar + gap) * 2
- #h_img = gap + (h_card + gap) * n_cards_p_col
+ n_cards_p_col = 4 # Number of cards displayed per one column
+ w_img = gap + (w_card + gap + w_bar + gap) * 2 # Dimension of the entire graph (for 2 columns)
h_img = 480
img_graph = np.zeros((h_img, w_img, 3), dtype=np.uint8)
x_anchor = gap
y_anchor = gap
i = 0
+
+ # Cards are displayed from the most confident to the least
+ # Confidence level is calculated by number of frames that the card was detected in
for key, val in sorted(exist_cards.items(), key=itemgetter(1), reverse=True)[:n_cards_p_col * 2]:
card_name = key[:key.find('(') - 1]
card_set = key[key.find('(') + 1:key.find(')')]
confidence = sum(val) / f_len
card_info = card_pool[(card_pool['name'] == card_name) & (card_pool['set'] == card_set)].iloc[0]
img_name = '%s/card_img/tiny/%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)
+ card_info['collector_number'],
+ fetch_data.get_valid_filename(card_info['name']))
+ # If the card image is not found, just leave it blank
+ if os.path.exists(img_name):
+ card_img = cv2.imread(img_name)
+ else:
+ card_img = np.ones((h_card, w_card))
+
+ # Insert the card image, card name, and confidence bar to the graph
img_graph[y_anchor:y_anchor + h_card, x_anchor:x_anchor + w_card] = card_img
cv2.putText(img_graph, '%s (%s)' % (card_name, card_set),
(x_anchor + w_card + gap, y_anchor + gap_sm + int(txt_scale * 25)), cv2.FONT_HERSHEY_SIMPLEX,
@@ -341,101 +360,94 @@
return img_graph
-def detect_frame(net, classes, img, card_pool, thresh_conf=0.5, thresh_nms=0.4, in_dim=(416, 416), card_size=1000,
+def detect_frame(img, card_pool, hash_size=32, highfreq_factor=4, size_thresh=10000,
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)
+ """
+ Identify all cards in the input frame, display or save the frame if needed
+ :param img: input frame
+ :param card_pool: pandas dataframe of all card's information
+ :param hash_size: param for pHash algorithm
+ :param highfreq_factor: param for pHash algorithm
+ :param size_thresh: threshold for size (in pixel) of the contour to be a candidate
+ :param out_path: path to save the result
+ :param display: flag for displaying the result
+ :param debug: flag for debug mode
+ :return: list of detected card's name/set and resulting image
+ """
- # Sets the input to the network
- net.setInput(blob)
-
- # 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
- 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_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:
- # 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.
- '''
+ img_result = img.copy() # For displaying and saving
det_cards = []
- start_3 = time.time()
- cnts = find_card(img_result)
+ # Detect contours of all cards in the image
+ cnts = find_card(img_result, size_thresh=size_thresh)
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()
+ # 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])
img_warp = four_point_transform(img, pts)
- img_warp = cv2.resize(img_warp, (card_width, card_height))
- elapsed.append((time.time() - start_4) * 1000)
+
+ # To identify the card from the card image, perceptual hashing (pHash) algorithm is used
+ # Perceptual hash is a hash string built from features of the input medium. If two media are similar
+ # (ie. has similar features), their resulting pHash value will be very close.
+ # Using this property, the matching card for the given card image can be found by comparing pHash of
+ # all cards in the database, then finding the card that results in the minimal difference in pHash value.
'''
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']
+ art_hash = ih.phash(img_art, hash_size=hash_size, highfreq_factor=highfreq_factor).hash.flatten()
+ card_pool['hash_diff'] = card_pool['art_hash'].apply(lambda x: np.count_nonzero(x != art_hash))
'''
- 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()
+ # 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, highfreq_factor=highfreq_factor).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']
+ min_card = card_pool[card_pool['hash_diff'] == min(card_pool['hash_diff'])].iloc[0]
+ card_name = min_card['name']
+ card_set = min_card['set']
det_cards.append((card_name, card_set))
- hash_diff = min_cards.iloc[0]['hash_diff']
- elapsed.append((time.time() - start_5) * 1000)
+ hash_diff = min_card['hash_diff']
- # Display the result
+ # Render the result, and display them if needed
+ cv2.drawContours(img_result, [cnt], -1, (0, 255, 0), 2)
+ 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.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 display:
+ cv2.imshow('Result', img_result)
+ cv2.waitKey(0)
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 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,
- display=True, debug=False):
- if out_path is not None:
- img_graph = draw_card_graph({}, None, -1) # Black image of the graph just to get the dimension
+def detect_video(capture, card_pool, hash_size=32, highfreq_factor=4, size_thresh=10000,
+ out_path=None, display=True, show_graph=True, debug=False):
+ """
+ Identify all cards in the continuous video stream, display or save the result if needed
+ :param capture: input video stream
+ :param card_pool: pandas dataframe of all card's information
+ :param hash_size: param for pHash algorithm
+ :param highfreq_factor: param for pHash algorithm
+ :param size_thresh: threshold for size (in pixel) of the contour to be a candidate
+ :param out_path: path to save the result
+ :param display: flag for displaying the result
+ :param show_graph: flag to show graph
+ :param debug: flag for debug mode
+ :return: list of detected card's name/set and resulting image
+ :return:
+ """
+ # 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])
+ else:
+ width = round(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
+ height = round(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
+ if out_path is not None:
vid_writer = cv2.VideoWriter(out_path, cv2.VideoWriter_fourcc(*'MJPG'), 10.0, (width, height))
max_num_obj = 0
f_len = 10 # number of frames to consider to check for existing cards
@@ -449,55 +461,54 @@
print("End of video. Press any key to exit")
cv2.waitKey(0)
break
- # Use the YOLO model to identify each cards annonymously
- 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
- # If the same card is detected multiple times in the same frame, keep track of the duplicates
- # The confidence will be calculated based on the number of frames the card was detected for
- det_cards_count = collections.Counter(det_cards).items()
- det_cards_list = []
- for card, count in det_cards_count:
- card_name, card_set = card
- for i in range(count): 1
- key = '%s (%s) #%d' % (card_name, card_set, i + 1)
- det_cards_list.append(key)
- gone = []
- for key, val in exist_cards.items():
- if key in det_cards_list:
- exist_cards[key] = exist_cards[key][1 - f_len:] + [1]
- else:
- exist_cards[key] = exist_cards[key][1 - f_len:] + [0]
- if len(val) == f_len and sum(val) == 0:
- gone.append(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)
- 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))
+ # Detect all cards from the current frame
+ det_cards, img_result = detect_frame(frame, card_pool, hash_size=hash_size, highfreq_factor=highfreq_factor,
+ 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
+ # 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
+ # If the same card is detected multiple times in the same frame, keep track of the duplicates
+ # The confidence will be calculated based on the number of frames the card was detected for
+ det_cards_count = collections.Counter(det_cards).items()
+ det_cards_list = []
+ for card, count in det_cards_count:
+ card_name, card_set = card
+ for i in range(count): 1
+ key = '%s (%s) #%d' % (card_name, card_set, i + 1)
+ det_cards_list.append(key)
+ gone = []
+ for key, val in exist_cards.items():
+ if key in det_cards_list:
+ exist_cards[key] = exist_cards[key][1 - f_len:] + [1]
+ else:
+ exist_cards[key] = exist_cards[key][1 - f_len:] + [0]
+ if len(val) == f_len and sum(val) == 0:
+ gone.append(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)
- 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
+ # 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
+ else:
+ img_save = img_result
+
+ # Display the result
if display:
cv2.imshow('result', img_save)
- elapsed_display = (time.time() - start_display) * 1000
+ if debug:
+ max_num_obj = max(max_num_obj, len(det_cards))
+ for i in range(len(det_cards), max_num_obj):
+ cv2.imshow('card#%d' % i, np.zeros((1, 1), dtype=np.uint8))
elapsed_ms = (time.time() - start_time) * 1000
- print('Elapsed time: %.2f ms, %.2f, %.2f, %.2f' % (elapsed_ms, elapsed_yolo, elapsed_graph, elapsed_display))
+ print('Elapsed time: %.2f ms' % elapsed_ms)
if out_path is not None:
vid_writer.write(img_save.astype(np.uint8))
cv2.waitKey(1)
@@ -510,86 +521,61 @@
def main():
# Specify paths for all necessary files
- test_path = os.path.abspath('test_file/test4.mp4')
- #weight_path = 'backup/tiny_yolo_10_39500.weights'
- #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_old.cfg'
- class_path = 'data/obj.names'
+ #test_path = os.path.abspath('test_file/test4.mp4')
+ test_path = None
out_dir = 'out'
- if not os.path.isfile(test_path):
- print('The test file %s doesn\'t exist!' % os.path.abspath(test_path))
- return
- if not os.path.isfile(weight_path):
- print('The weight file %s doesn\'t exist!' % os.path.abspath(test_path))
- return
- if not os.path.isfile(cfg_path):
- print('The config file %s doesn\'t exist!' % os.path.abspath(test_path))
- return
- if not os.path.isfile(class_path):
- print('The class file %s doesn\'t exist!' % os.path.abspath(test_path))
- return
+ hash_size = 32
+ highfreq_factor = 4
+ pck_path = os.path.abspath('card_pool_%d_%d.pck' % (hash_size, highfreq_factor))
+ if os.path.isfile(pck_path):
+ card_pool = pd.read_pickle(pck_path)
+ else:
+ # Merge database for all cards, then calculate pHash values of each, store them
+ 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)
+ 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')
- '''
- 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, sort=True)
- card_pool.reset_index(drop=True, inplace=True)
- card_pool.drop('Unnamed: 0', axis=1, inplace=True, errors='ignore')
- 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, 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 = calc_image_hashes(card_pool, save_to=pck_path, hash_size=hash_size, highfreq_factor=highfreq_factor)
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
- # Setup
- # Read class names from text file
- with open(class_path, 'r') as f:
- classes = [line.strip() for line in f.readlines()]
- # Load up the neural net using the config and weights
- net = cv2.dnn.readNetFromDarknet(cfg_path, weight_path)
- net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
- net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)
-
- # Save the detection result if out_dir is provided
- if out_dir is None or out_dir == '':
- out_path = None
- else:
- f_name = os.path.split(test_path)[1]
- out_path = out_dir + '/' + f_name[:f_name.find('.')] + '.avi'
- # Check if test file is image or video
- test_ext = test_path[test_path.find('.') + 1:]
-
- if test_ext in ['jpg', 'jpeg', 'bmp', 'png', 'tiff']:
- img = cv2.imread(test_path)
- detect_frame(net, classes, img, card_pool, out_path=out_path, thresh_conf=thresh_conf, thresh_nms=thresh_nms)
- else:
+ # If the test file isn't given, use webcam to capture video
+ if test_path is None:
capture = cv2.VideoCapture(0)
- detect_video(net, classes, capture, card_pool, out_path=out_path, thresh_conf=thresh_conf, thresh_nms=thresh_nms,
- display=True, debug=False)
+ detect_video(capture, card_pool, out_path='%s/result.avi' % out_dir, display=True, show_graph=True, debug=False)
capture.release()
+ else:
+ # Save the detection result if out_dir is provided
+ if out_dir is None or out_dir == '':
+ out_path = None
+ else:
+ f_name = os.path.split(test_path)[1]
+ out_path = '%s/%s.avi' % (out_dir, f_name[:f_name.find('.')])
+
+ if not os.path.isfile(test_path):
+ print('The test file %s doesn\'t exist!' % os.path.abspath(test_path))
+ return
+ # Check if test file is image or video
+ test_ext = test_path[test_path.find('.') + 1:]
+ if test_ext in ['jpg', 'jpeg', 'bmp', 'png', 'tiff']:
+ # Test file is an image
+ img = cv2.imread(test_path)
+ detect_frame(img, card_pool, out_path=out_path)
+ else:
+ # Test file is a video
+ capture = cv2.VideoCapture(test_path)
+ detect_video(capture, card_pool, out_path=out_path, display=True, show_graph=True, debug=False)
+ capture.release()
pass
--
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