In the process of fish breeding, fishery resources survey, and aquaculture, the length, thickness, and height of the fish body are measured. The traditional measurement method mainly adopts artificial measurement, which has the disadvantages of high labor intensity, low efficiency, and low measurement accuracy. Using machine vision technology to measure fish traits can effectively improve measurement efficiency and accuracy, and is easy to be automated. Based on machine vision technology, we designed an integrated trait measurement platform that  integrated fish image collection and analysis, body weight and weight available trait measurement, and PIT scanning code, which can realize the accurate measurement of trait parameters of carp and crucian carp. Pixel calibration is required for character measurement of 2D fish images. Due to the different body thicknesses of each fish, it is difficult to achieve accurate measurement with a traditional fixed pixel calibration plane when the body thickness is large. To solve this problem, this study designed a pixel calibration method. During the collection of fish body images, the thickness of the fish body was measured by a distance sensor, and pixel calibration parameters of each fish contour surface were determined according to the thickness, so as to reduce the measurement error and improve the accuracy of the measurement of measurable traits of fish. The measurement errors of the system were compared with the manual measurement results. The results showed that the relative measurement errors of body length, body height, head length, caudal stem length, caudal stem height, and body thickness have no significantly change with the increase in body thickness. The maximum relative errors of body height and body length were 1.05% and -1.39% respectively. The maximum relative errors of head length, caudal peduncle height, caudal peduncle length, and body thickness were 1.73%, -2.79%, -2.88%, and -2.1% respectively, and the absolute errors were all less than 1mm. The maximum relative error of body weight was -0.36%. This indicates that the system fits the requirements of fish characters measurement.