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demo_camera.py
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demo_camera.py
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import os
import sys
import argparse
import cv2
import math
import time
import numpy as np
import util
from config_reader import config_reader
from scipy.ndimage.filters import gaussian_filter
sys.path.append(os.path.join(os.path.dirname(__file__), ".."))
from model.cmu_model import get_testing_model
currentDT = time.localtime()
start_datetime = time.strftime("-%m-%d-%H-%M-%S", currentDT)
# find connection in the specified sequence, center 29 is in the position 15
limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], \
[10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], \
[1, 16], [16, 18], [3, 17], [6, 18]]
# the middle joints heatmap correpondence
mapIdx = [[31, 32], [39, 40], [33, 34], [35, 36], [41, 42], [43, 44], [19, 20], [21, 22], \
[23, 24], [25, 26], [27, 28], [29, 30], [47, 48], [49, 50], [53, 54], [51, 52], \
[55, 56], [37, 38], [45, 46]]
# visualize
colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0],
[0, 255, 0], \
[0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255],
[85, 0, 255], \
[170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]]
def process (input_image, params, model_params):
#fx=0.5, fy=0.5
#oriImg = cv2.imread(input_image) # B,G,R order
oriImg = cv2.cvtColor(input_image, cv2.COLOR_RGB2BGR)
print("orig", oriImg.shape)
#width = oriImg.shape[1]
#height = oriImg.shape[0]
factor = 0.3
#oriImg = oriImg[int(height*factor):int(height*(1-factor))]
#oriImg = cv2.resize(oriImg, (0, 0), fx=1/4, fy=1/4, interpolation=cv2.INTER_CUBIC)
#scale_search = params['scale_search']
scale_search = [0.22,0.25,.5, 1, 1.5, 2] # [.5, 1, 1.5, 2]
scale_search = scale_search[0:process_speed]
#multiplier = [x * model_params['boxsize'] / oriImg.shape[0] for x in scale_search]
multiplier = [x * model_params['boxsize'] / oriImg.shape[1] for x in scale_search]
heatmap_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 19))
paf_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 38))
for m in range(len(multiplier)):
scale = multiplier[m]
imageToTest = cv2.resize(oriImg, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC)
imageToTest_padded, pad = util.padRightDownCorner(imageToTest, model_params['stride'],
model_params['padValue'])
input_img = np.transpose(np.float32(imageToTest_padded[:,:,:,np.newaxis]), (3,0,1,2)) # required shape (1, width, height, channels)
#input_img = np.transpose(np.float32(imageToTest_padded[:,:,:,np.newaxis]), (3,1,0,2)) # required shape (1, width, height, channels)
print(input_img.shape)
output_blobs = model.predict(input_img)
# extract outputs, resize, and remove padding
heatmap = np.squeeze(output_blobs[1]) # output 1 is heatmaps
heatmap = cv2.resize(heatmap, (0, 0), fx=model_params['stride'], fy=model_params['stride'],
interpolation=cv2.INTER_CUBIC)
heatmap = heatmap[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3],
:]
heatmap = cv2.resize(heatmap, (oriImg.shape[1], oriImg.shape[0]), interpolation=cv2.INTER_CUBIC)
paf = np.squeeze(output_blobs[0]) # output 0 is PAFs
paf = cv2.resize(paf, (0, 0), fx=model_params['stride'], fy=model_params['stride'],
interpolation=cv2.INTER_CUBIC)
paf = paf[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :]
paf = cv2.resize(paf, (oriImg.shape[1], oriImg.shape[0]), interpolation=cv2.INTER_CUBIC)
heatmap_avg = heatmap_avg + heatmap / len(multiplier)
paf_avg = paf_avg + paf / len(multiplier)
all_peaks = []
peak_counter = 0
for part in range(18):
map_ori = heatmap_avg[:, :, part]
map = gaussian_filter(map_ori, sigma=3)
map_left = np.zeros(map.shape)
map_left[1:, :] = map[:-1, :]
map_right = np.zeros(map.shape)
map_right[:-1, :] = map[1:, :]
map_up = np.zeros(map.shape)
map_up[:, 1:] = map[:, :-1]
map_down = np.zeros(map.shape)
map_down[:, :-1] = map[:, 1:]
peaks_binary = np.logical_and.reduce(
(map >= map_left, map >= map_right, map >= map_up, map >= map_down, map > params['thre1']))
peaks = list(zip(np.nonzero(peaks_binary)[1], np.nonzero(peaks_binary)[0])) # note reverse
peaks_with_score = [x + (map_ori[x[1], x[0]],) for x in peaks]
id = range(peak_counter, peak_counter + len(peaks))
peaks_with_score_and_id = [peaks_with_score[i] + (id[i],) for i in range(len(id))]
all_peaks.append(peaks_with_score_and_id)
peak_counter += len(peaks)
connection_all = []
special_k = []
mid_num = 10
for k in range(len(mapIdx)):
score_mid = paf_avg[:, :, [x - 19 for x in mapIdx[k]]]
candA = all_peaks[limbSeq[k][0] - 1]
candB = all_peaks[limbSeq[k][1] - 1]
nA = len(candA)
nB = len(candB)
indexA, indexB = limbSeq[k]
if (nA != 0 and nB != 0):
connection_candidate = []
for i in range(nA):
for j in range(nB):
vec = np.subtract(candB[j][:2], candA[i][:2])
norm = math.sqrt(vec[0] * vec[0] + vec[1] * vec[1])
# failure case when 2 body parts overlaps
if norm == 0:
continue
vec = np.divide(vec, norm)
startend = list(zip(np.linspace(candA[i][0], candB[j][0], num=mid_num), \
np.linspace(candA[i][1], candB[j][1], num=mid_num)))
vec_x = np.array(
[score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 0] \
for I in range(len(startend))])
vec_y = np.array(
[score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 1] \
for I in range(len(startend))])
score_midpts = np.multiply(vec_x, vec[0]) + np.multiply(vec_y, vec[1])
score_with_dist_prior = sum(score_midpts) / len(score_midpts) + min(
0.5 * oriImg.shape[0] / norm - 1, 0)
criterion1 = len(np.nonzero(score_midpts > params['thre2'])[0]) > 0.8 * len(
score_midpts)
criterion2 = score_with_dist_prior > 0
if criterion1 and criterion2:
connection_candidate.append([i, j, score_with_dist_prior,
score_with_dist_prior + candA[i][2] + candB[j][2]])
connection_candidate = sorted(connection_candidate, key=lambda x: x[2], reverse=True)
connection = np.zeros((0, 5))
for c in range(len(connection_candidate)):
i, j, s = connection_candidate[c][0:3]
if (i not in connection[:, 3] and j not in connection[:, 4]):
connection = np.vstack([connection, [candA[i][3], candB[j][3], s, i, j]])
if (len(connection) >= min(nA, nB)):
break
connection_all.append(connection)
else:
special_k.append(k)
connection_all.append([])
# last number in each row is the total parts number of that person
# the second last number in each row is the score of the overall configuration
subset = -1 * np.ones((0, 20))
candidate = np.array([item for sublist in all_peaks for item in sublist])
for k in range(len(mapIdx)):
if k not in special_k:
partAs = connection_all[k][:, 0]
partBs = connection_all[k][:, 1]
indexA, indexB = np.array(limbSeq[k]) - 1
for i in range(len(connection_all[k])): # = 1:size(temp,1)
found = 0
subset_idx = [-1, -1]
for j in range(len(subset)): # 1:size(subset,1):
if subset[j][indexA] == partAs[i] or subset[j][indexB] == partBs[i]:
subset_idx[found] = j
found += 1
if found == 1:
j = subset_idx[0]
if (subset[j][indexB] != partBs[i]):
subset[j][indexB] = partBs[i]
subset[j][-1] += 1
subset[j][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2]
elif found == 2: # if found 2 and disjoint, merge them
j1, j2 = subset_idx
membership = ((subset[j1] >= 0).astype(int) + (subset[j2] >= 0).astype(int))[:-2]
if len(np.nonzero(membership == 2)[0]) == 0: # merge
subset[j1][:-2] += (subset[j2][:-2] + 1)
subset[j1][-2:] += subset[j2][-2:]
subset[j1][-2] += connection_all[k][i][2]
subset = np.delete(subset, j2, 0)
else: # as like found == 1
subset[j1][indexB] = partBs[i]
subset[j1][-1] += 1
subset[j1][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2]
# if find no partA in the subset, create a new subset
elif not found and k < 17:
row = -1 * np.ones(20)
row[indexA] = partAs[i]
row[indexB] = partBs[i]
row[-1] = 2
row[-2] = sum(candidate[connection_all[k][i, :2].astype(int), 2]) + \
connection_all[k][i][2]
subset = np.vstack([subset, row])
# delete some rows of subset which has few parts occur
deleteIdx = [];
for i in range(len(subset)):
if subset[i][-1] < 4 or subset[i][-2] / subset[i][-1] < 0.4:
deleteIdx.append(i)
subset = np.delete(subset, deleteIdx, axis=0)
#canvas = cv2.imread(input_image) # B,G,R order
canvas = cropped
#all_peaks *= resize_fac
#subset *= resize_fac
#print(all_peaks)
for i in range(18):
for j in range(len(all_peaks[i])):
#all_peaks[i][j] *= resize_fac
a = all_peaks[i][j][0] * resize_fac
b = all_peaks[i][j][1] * resize_fac
#cv2.circle(canvas, all_peaks[i][j][0:2], 4, colors[i], thickness=-1)
cv2.circle(canvas, (a,b), 2, colors[i], thickness=-1)
stickwidth = 4
#print(subset)
for i in range(17):
for n in range(len(subset)):
index = subset[n][np.array(limbSeq[i]) - 1]
if -1 in index:
continue
#index *= resize_fac
cur_canvas = canvas.copy()
Y = candidate[index.astype(int), 0]
X = candidate[index.astype(int), 1]
mX = np.mean(X)
mY = np.mean(Y)
length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
polygon = cv2.ellipse2Poly((int(mY*resize_fac), int(mX*resize_fac)), (int(length*resize_fac / 2), stickwidth), int(angle), 0,360, 1)
#polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1)
cv2.fillConvexPoly(cur_canvas, polygon, colors[i])
canvas = cv2.addWeighted(canvas, 0.4, cur_canvas, 0.6, 0)
return canvas
if __name__ == '__main__':
parser = argparse.ArgumentParser()
#parser.add_argument('--video', type=str, required=True, help='input video file name')
parser.add_argument('--model', type=str, default='model/keras/model.h5', help='path to the weights file')
parser.add_argument('--frame_ratio', type=int, default=7, help='analyze every [n] frames')
# --process_speed changes at how many times the model analyzes each frame at a different scale
parser.add_argument('--process_speed', type=int, default=1, help='Int 1 (fastest, lowest quality) to 4 (slowest, highest quality)')
#parser.add_argument('--start', type=int, default=0, help='Video frame to start with')
parser.add_argument('--end', type=int, default=None, help='Last video frame to analyze')
args = parser.parse_args()
#input_image = args.image
#output = args.output
keras_weights_file = args.model
frame_rate_ratio = args.frame_ratio
process_speed = args.process_speed
#starting_frame = args.start
ending_frame = args.end
print('start processing...')
# Video input
video = 'webcam'
video_path = 'videos/'
video_file = video_path + video
# Output location
output_path = 'videos/outputs/'
output_format = '.mp4'
video_output = output_path + video + str(start_datetime) + output_format
# load model
# authors of original model don't use
# vgg normalization (subtracting mean) on input images
model = get_testing_model()
model.load_weights(keras_weights_file)
# load config
params, model_params = config_reader()
# Video reader
#cam = cv2.VideoCapture(video_file)
cam = cv2.VideoCapture(0)
#CV_CAP_PROP_FPS
#cam.set(cv2.CAP_PROP_FPS, 10)
#cam.set(cv2.CAP_PROP_FPS, 10)
input_fps = cam.get(cv2.CAP_PROP_FPS)
print("Running at {} fps.".format(input_fps))
ret_val, input_image = cam.read()
video_length = 1000 #int(cam.get(cv2.CAP_PROP_FRAME_COUNT))
if ending_frame == None:
ending_frame = video_length
# Video writer
output_fps = input_fps / frame_rate_ratio
#fourcc = cv2.VideoWriter_fourcc(*'mp4v')
#out = cv2.VideoWriter(video_output,fourcc, output_fps, (input_image.shape[1], input_image.shape[0]))
i = 0 # default is 0
resize_fac = 8
while(cam.isOpened()) and ret_val == True and i < ending_frame:
#if i%frame_rate_ratio == 0:
while True:
cv2.waitKey(10)
ret_val, orig_image = cam.read()
tic = time.time()
width = orig_image.shape[1]
height = orig_image.shape[0]
factor = 0.3
cropped = orig_image[:,int(width*factor):int(width*(1-factor))]
# generate image with body parts
input_image = cv2.resize(cropped, (0, 0), fx=1/resize_fac, fy=1/resize_fac, interpolation=cv2.INTER_CUBIC)
canvas = process(input_image, params, model_params)
print('Processing frame: ', i)
toc = time.time()
print ('processing time is %.5f' % (toc - tic))
#out.write(canvas)
#canvas = cv2.resize(canvas, (0, 0), fx=4, fy=4, interpolation=cv2.INTER_CUBIC)
cv2.imshow('frame',canvas)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
#elif i % 3 == 0:
#cv2.imshow('frame',input_image)
#if cv2.waitKey(1) & 0xFF == ord('q'):
#break
#ret_val, input_image = cam.read()
i += 1