def SimpleRecognizePlateByE2E(image):
t0 = time.time()
images = detect.detectPlateRough(image,
image.shape[0],
top_bottom_padding_rate=0.1)
res_set = []
for j, plate in enumerate(images):
plate, rect, origin_plate = plate
# plate = cv2.cvtColor(plate, cv2.COLOR_RGB2GRAY)
plate = cv2.resize(plate, (136, 36 * 2))
res, confidence = e2e.recognizeOne(origin_plate)
print("res", res)
t1 = time.time()
ptype = td.SimplePredict(plate)
if (ptype > 0) and (ptype < 5):
# pass
plate = cv2.bitwise_not(plate)
image_rgb = fm.findContoursAndDrawBoundingBox(plate)
image_rgb = fv.finemappingVertical(image_rgb)
image_rgb = fv.finemappingVertical(image_rgb)
cache.verticalMappingToFolder(image_rgb)
cv2.imwrite("./" + str(j) + ".jpg", image_rgb)
res, confidence = e2e.recognizeOne(image_rgb)
print(res, confidence)
res_set.append([[], res, confidence])
if confidence > 0.7:
image = drawRectBox(image, rect,
res + " " + str(round(confidence, 3)))
return image, res_set
def SimpleRecognizePlateByE2E(image):
t0 = time.time()
images = detect.detectPlateRough(image,image.shape[0],top_bottom_padding_rate=0.1)
res_set = []
for j,plate in enumerate(images):
plate, rect, origin_plate =plate
# plate = cv2.cvtColor(plate, cv2.COLOR_RGB2GRAY)
plate =cv2.resize(plate,(136,36*2))
res,confidence = e2e.recognizeOne(origin_plate)
print "res",res
t1 = time.time()
ptype = td.SimplePredict(plate)
if ptype>0 and ptype<5:
# pass
plate = cv2.bitwise_not(plate)
image_rgb = fm.findContoursAndDrawBoundingBox(plate)
image_rgb = fv.finemappingVertical(image_rgb)
image_rgb = fv.finemappingVertical(image_rgb)
cache.verticalMappingToFolder(image_rgb)
cv2.imwrite("./"+str(j)+".jpg",image_rgb)
res,confidence = e2e.recognizeOne(image_rgb)
print res,confidence
res_set.append([[],res,confidence])
if confidence>0.7:
image = drawRectBox(image, rect, res+" "+str(round(confidence,3)))
return image,res_set
def RecognizePlateJson(image):
images = detect.detectPlateRough(image,image.shape[0],top_bottom_padding_rate=0.1)
jsons = []
for j,plate in enumerate(images):
plate,rect,origin_plate =plate
res, confidence = e2e.recognizeOne(origin_plate)
print "res",res
cv2.imwrite("./"+str(j)+"_rough.jpg",plate)
# print "车牌类型:",ptype
# plate = cv2.cvtColor(plate, cv2.COLOR_RGB2GRAY)
plate =cv2.resize(plate,(136,int(36*2.5)))
t1 = time.time()
ptype = td.SimplePredict(plate)
if ptype>0 and ptype<4:
plate = cv2.bitwise_not(plate)
# demo = verticalEdgeDetection(plate)
image_rgb = fm.findContoursAndDrawBoundingBox(plate)
image_rgb = fv.finemappingVertical(image_rgb)
cache.verticalMappingToFolder(image_rgb)
# print time.time() - t1,"校正"
print "e2e:",e2e.recognizeOne(image_rgb)[0]
image_gray = cv2.cvtColor(image_rgb,cv2.COLOR_BGR2GRAY)
cv2.imwrite("./"+str(j)+".jpg",image_gray)
# image_gray = horizontalSegmentation(image_gray)
t2 = time.time()
res, confidence = e2e.recognizeOne(image_rgb)
res_json = {}
if confidence > 0.6:
res_json["Name"] = res
res_json["Type"] = td.plateType[ptype]
res_json["Confidence"] = confidence;
res_json["x"] = int(rect[0])
res_json["y"] = int(rect[1])
res_json["w"] = int(rect[2])
res_json["h"] = int(rect[3])
jsons.append(res_json)
print json.dumps(jsons,ensure_ascii=False,encoding="gb2312")
return json.dumps(jsons,ensure_ascii=False,encoding="gb2312")
def SimpleRecognizePlate(image):
t0 = time.time()
images = detect.detectPlateRough(image,
image.shape[0],
top_bottom_padding_rate=0.1)
res_set = []
for j, plate in enumerate(images):
plate, rect, origin_plate = plate
# plate = cv2.cvtColor(plate, cv2.COLOR_RGB2GRAY)
plate = cv2.resize(plate, (136, 36 * 2))
t1 = time.time()
ptype = td.SimplePredict(plate)
if (ptype > 0) and (ptype < 5):
plate = cv2.bitwise_not(plate)
image_rgb = fm.findContoursAndDrawBoundingBox(plate)
image_rgb = fv.finemappingVertical(image_rgb)
cache.verticalMappingToFolder(image_rgb)
print("e2e:", e2e.recognizeOne(image_rgb))
image_gray = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2GRAY)
# image_gray = horizontalSegmentation(image_gray)
cv2.imshow("image_gray", image_gray)
# cv2.waitKey()
cv2.imwrite("./" + str(j) + ".jpg", image_gray)
# cv2.imshow("image",image_gray)
# cv2.waitKey(0)
print("校正", time.time() - t1, "s")
# cv2.imshow("image,",image_gray)
# cv2.waitKey(0)
t2 = time.time()
val = segmentation.slidingWindowsEval(image_gray)
# print val
print("分割和识别", time.time() - t2, "s")
if len(val) == 3:
blocks, res, confidence = val
if confidence / 7 > 0.7:
image = drawRectBox(image, rect, res)
res_set.append(res)
for i, block in enumerate(blocks):
block_ = cv2.resize(block, (25, 25))
block_ = cv2.cvtColor(block_, cv2.COLOR_GRAY2BGR)
image[j * 25:(j * 25) + 25, i * 25:(i * 25) + 25] = block_
if image[j * 25:(j * 25) + 25,
i * 25:(i * 25) + 25].shape == block_.shape:
pass
if confidence > 0:
print("车牌:", res, "置信度:", confidence / 7)
else:
pass
# print "不确定的车牌:", res, "置信度:", confidence
print(time.time() - t0, "s")
return image, res_set
def SimpleRecognizePlate(image):
t0 = time.time()
images = detect.detectPlateRough(image,image.shape[0],top_bottom_padding_rate=0.1)
res_set = []
for j,plate in enumerate(images):
plate, rect, origin_plate =plate
# plate = cv2.cvtColor(plate, cv2.COLOR_RGB2GRAY)
plate =cv2.resize(plate,(136,36*2))
t1 = time.time()
ptype = td.SimplePredict(plate)
if ptype>0 and ptype<5:
plate = cv2.bitwise_not(plate)
image_rgb = fm.findContoursAndDrawBoundingBox(plate)
image_rgb = fv.finemappingVertical(image_rgb)
cache.verticalMappingToFolder(image_rgb)
print "e2e:", e2e.recognizeOne(image_rgb)
image_gray = cv2.cvtColor(image_rgb,cv2.COLOR_RGB2GRAY)
# image_gray = horizontalSegmentation(image_gray)
cv2.imshow("image_gray",image_gray)
# cv2.waitKey()
cv2.imwrite("./"+str(j)+".jpg",image_gray)
# cv2.imshow("image",image_gray)
# cv2.waitKey(0)
print "校正",time.time() - t1,"s"
# cv2.imshow("image,",image_gray)
# cv2.waitKey(0)
t2 = time.time()
val = segmentation.slidingWindowsEval(image_gray)
# print val
print "分割和识别",time.time() - t2,"s"
if len(val)==3:
blocks, res, confidence = val
if confidence/7>0.7:
image = drawRectBox(image,rect,res)
res_set.append(res)
for i,block in enumerate(blocks):
block_ = cv2.resize(block,(25,25))
block_ = cv2.cvtColor(block_,cv2.COLOR_GRAY2BGR)
image[j * 25:(j * 25) + 25, i * 25:(i * 25) + 25] = block_
if image[j*25:(j*25)+25,i*25:(i*25)+25].shape == block_.shape:
pass
if confidence>0:
print "车牌:",res,"置信度:",confidence/7
else:
pass
# print "不确定的车牌:", res, "置信度:", confidence
print time.time() - t0,"s"
return image,res_set